CHANGES IN NETWORK FUNCTIONAL CONNECTIVITY AFTER TOTAL KNEE REPLACEMENT SURGERY WITH GENERAL ANESTHESIA: AN FMRI STUDY

By

HUA HUANG

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

© 2018 Hua Huang

To my Mother, and Father

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ACKNOWLEDGMENTS

I would like to express my special appreciation and thanks to my advisor

Professor Mingzhou Ding at this moment. Thank you for accepting me to start my PhD

research in neuroimaging and neuroscience. Thank you for your support when I was in

helpless situation, when support was needed to focus on my research work, and when I

got lost. Thank you for your precious funding support and your forgiveness when

inconvenience was raised up because of me. It is an impossible mission to reach this

stage without your guidance in my study and my life. It is my best luck to be your

student.

I would especially like to thank my committee members, Professor Hans van

Oostrom, Professor Jonathan Li, and Professor Jorg Peters for serving as my

committee members during my PhD study. I also want to thank you for your comments,

suggestions, and your support, especially suggestions from Professor Peters in graph

theory. Thank you all for reserving your precious time to attend my final defense.

I would also like to thank Professor Catherine Price, Dr. Jared Tanner, and Dr.

Hari Parvataneni for allowing me to join this research project and this big research

group to accomplish my study. Thanks for providing all the collected precious data and

sharing the cutting-edge research in this field.

I would especially like to thank Professor Roger Howe at Stanford University for

precious suggestions so I can persist and move forward with confidence. I would like to

give my special appreciation to Professor Wesley Bolch, and Debra Anderson for their

selfless help.

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I would also thank other researchers and all students in our lab for the help

during my study. Especially thank Dr. Qing Zhao, Dr. Abhijit Rajan, and Bijurika Nandi

for providing help and suggestions for my study.

A special appreciation to my family. I would like to express my thanks to my

Mother, and my Father. Thank you all for supporting me under any circumstances in so

many years. To my beloved daughter, thank you for making my life meaningful.

Thank you all who provided support throughout my study at University of Florida.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 11

ABSTRACT ................................................................................................................... 13

CHAPTER

1 INTRODUCTION .................................................................................................... 15

2 CHANGES IN INTRA-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS FOLLOWING SURGERY .................................................................. 18

2.1 Introduction ....................................................................................................... 18 2.2 Methods ............................................................................................................ 20

2.2.1 Participant ............................................................................................... 20 2.2.2 Procedures .............................................................................................. 21

2.2.3 Anesthesia and Surgery Protocol ............................................................ 23 2.2.4 Neuroimaging .......................................................................................... 24

2.2.5 Functional MRI Data Preprocessing ........................................................ 24 2.2.6 FMRI Regions of Interest Selection ......................................................... 25

2.2.7 Variables for Regression ......................................................................... 26 2.2.8 Functional Connectivity Analysis ............................................................. 27 2.2.9 Statistical Analysis ................................................................................... 29

2.3 Results .............................................................................................................. 30 2.3.1 Intra-network Connectivity ....................................................................... 30 2.3.2 Node Strength of Intra-network Connectivity ........................................... 33

2.3.3 MCI versus Non-MCI ............................................................................... 34 2.4 Discussion ........................................................................................................ 35

3 CHANGES IN INTER-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS FOLLOWING SURGERY .................................................................. 55

3.1 Introduction ....................................................................................................... 55 3.2 Methods ............................................................................................................ 57

3.2.1 FMRI Regions of Interest Selection ......................................................... 57

3.2.2 Functional Connectivity Analysis ............................................................. 57 3.3 Results .............................................................................................................. 59

3.3.1 Inter-network Connectivity ....................................................................... 59

3.3.2 Node Strength in Inter-network Connectivity ........................................... 62

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3.3.3 Correlation between Changes in Intra-network Connectivity and in Inter-network Connectivity ............................................................................. 64

3.4 Discussion ........................................................................................................ 65

4 CHANGES IN FUNCTIONAL BRAIN CONNECTOME FOLLOWING SURGERY .. 93

4.1 Introduction ....................................................................................................... 93 4.2 Methods ............................................................................................................ 95

4.2.1 FMRI Regions of Brain Areas .................................................................. 95

4.2.2 Functional Connectivity Analysis ............................................................. 95 4.2.3 Graph Theoretical Analysis ..................................................................... 95

4.3 Results .............................................................................................................. 98 4.3.1 Changes in Global Network Properties .................................................... 98

4.3.2 Resilience Analysis of the Whole Brain Network ..................................... 98 4.3.3 Connection Density and Mean Functional Connectivity ......................... 100 4.3.4 Brain Areas with Connectivity Changes ................................................. 101

4.4 Discussion ...................................................................................................... 103

5 CONCLUSIONS ................................................................................................... 117

LIST OF REFERENCES ............................................................................................. 119

BIOGRAPHICAL SKETCH .......................................................................................... 129

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LIST OF TABLES

Table page 2-1 The MNI Coordinates of the Regions of Interest (ROI) ....................................... 42

2-2 Participant Characteristics: Surgery Group versus Non-Surgery Group. ............ 43

2-3 Participant Characteristics: MCI versus Non-MCI in Different Groups. ............... 44

2-4 Mixed Repeated ANOVA between Surgery and Non-Surgery ............................ 45

2-5 Changes in Node Strength in Surgery Group ..................................................... 46

2-6 MCI versus Non-MCI in Surgery Group .............................................................. 47

2-7 MCI versus Non-MCI in Non-Surgery Group ...................................................... 48

3-1 The MNI Coordinates of the Regions of Interest (ROI) ....................................... 70

3-2 Inter-network Pearson correlation coefficients of DMN and SN in surgery group including MCI and non-MCI subtypes ....................................................... 71

3-3 Inter-network Pearson correlation coefficients of DMN and CEN in surgery group including MCI and non-MCI subtypes ....................................................... 74

3-4 Inter-network Pearson correlation coefficients of CEN and SN in surgery group including MCI and non-MCI subtypes ....................................................... 77

3-5 Node strength of DMN and SN of the inter-network in surgery group including MCI and non-MCI subtypes ................................................................................ 80

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LIST OF FIGURES

Figure page 2-1 Schematic design of parallel surgery and non-surgery participant timelines ...... 49

2-2 TKA surgery group mean edge functional connectivity changes from pre to post surgery time points ..................................................................................... 51

2-3 The connectivity of pre and post-surgery in four resting state network networks ............................................................................................................. 52

2-4 The comparison between MCI and non-MCI surgery groups ............................. 52

2-5 The comparison between MCI and non-MCI non-surgery groups ...................... 53

2-6 Changes in node strength of functional connectivity pre and post-surgery ......... 53

2-7 Node strength changes in different groups ......................................................... 54

2-8 Nod strength changes in MCI and non-MCI ....................................................... 54

3-1 Schematic diagram of the functional interactions between three resting state networks: DMN, CEN and SN............................................................................. 83

3-2 The inter-network correlation between DMN and SN ......................................... 84

3-3 The inter-network correlation between DMN and CEN ....................................... 85

3-4 The inter-network correlation between CEN and SN .......................................... 86

3-5 The comparison between MCI and non-MCI groups in inter-network correlation ........................................................................................................... 87

3-6 The node strength of DMN in inter-network correlation between DMN and SN .. 88

3-7 The node strength of SN in inter-network correlation between DMN and SN ..... 89

3-8 The comparison of node strength between MCI and non-MCI in inter-network correlation of DMN-SN ....................................................................................... 90

3-9 The correlation between intra-network connectivity changes of SN pre-post surgery and inter-network connectivity pre-post surgery of DMN-SN ................. 91

3-10 The correlation between intra-network connectivity and inter-network connectivity. ........................................................................................................ 92

4-1 Schematic diagram for the whole brain network analysis ................................. 107

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4-2 The topological properties of the brain networks in surgery group and non-surgery group before and after surgery ............................................................ 108

4-3 Resilience analysis: global efficiency was calculated after removing the nodes with descending order ............................................................................ 109

4-4 Comparison between pre-surgery and post-surgery in descending order range from 181 to 220 ...................................................................................... 110

4-5 Connection density and mean functional connectivity calculated by removing the nodes with descending order ...................................................................... 111

4-6 Comparison between pre-surgery and post-surgery in descending order range from 181 to 220 ...................................................................................... 112

4-7 Adjacency matrix keeping top 40% of functional connectivity ........................... 113

4-8 Brain area showing changes in connectivity ..................................................... 114

4-9 Areas with increased or decreased functional connectivity following surgery (positive adjacency matrix) ............................................................................... 115

4-10 Brain areas with increased or decreased functional connectivity following surgery (negative adjacency matrix) ................................................................. 116

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LIST OF ABBREVIATIONS

ACC Anterior cingulate cortex

AG Angular gyrus

AI Anterior insula

BA Brodmann area

BOLD Blood oxygen level dependent

CEN Central executive network

CFNB Continuous femoral nerve blocks

D-KEFS Delis-Kaplan executive function system

dLPFC Dorsolateral prefrontal cortex

DMN Default mode network

ExC Extrastriate visual cortex in the central fields

ExP Extrastriate visual cortex in the peripheral fields

FA Fractional anisotropy

FDR False discovery rate

FIR Finite impulse response

fMRI Functional magnetic resonance

IN Insula

IPL Inferior parietal lobule

lAI Left anterior insula

MCI Mild cognitive impairment

MED Morphine equivalent dosage

mPFC Medial prefrontal cortex

Non-MCI Non mild cognitive impairment

PCC Posterior cingulate cortex

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POCD Postoperative cognitive dysfunction

rAI Right anterior insula

ROI Regions of interest

rsfMRI Resting state functional magnetic resonance

RSN Resting state network

SN Salience network

SST Stop signal task

TKA Total knee arthroplasty

V1C Central visual cortex

V1P Peripheral visual cortex

vmPFC Ventromedial prefrontal cortex

VN Visual network

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

CHANGES IN NETWORK FUNCTIONAL CONNECTIVITY AFTER TOTAL KNEE REPLACEMENT SURGERY WITH GENERAL ANESTHESIA: AN FMRI STUDY

By

Hua Huang

December 2018

Chair: Mingzhou Ding Major: Biomedical Engineering

The brain is a large network comprised of many subnetworks. Brain functional

connectivity reflects the organization and coordination of different brain areas to achieve

normal activities. Functional magnetic resonance imaging (fMRI) is an important

noninvasive method to look into these functional connections. The brain functions have

different properties at different stages of the whole life span. Older adults may have

weakened brain functions compared to young adults. Major surgery as a perturbation on

the brain may induce acute or chronic functional changes in older adults.

This dissertation examined the brain functional changes caused by the total knee

arthroplasty (TKA) in older adults. Three important resting state networks were analyzed

first to examine the changes of intra-network connectivity. The interactions between

networks were inspected next to evaluate the changes in the coordination between

these three networks in maintaining the normal brain activity. The complex whole brain

functional connectome defined by the standard mask with 234 regions was analyzed

using graph theory to evaluate the changes in the whole brain functional connectivity

following TKA surgery.

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The main findings were as follows. (1) The intra-network connectivity in DMN,

SN, and CEN had significant decline after surgery. No significant changes were found in

non-surgery group. MCI surgery group was more susceptible to the injury caused by

surgery and had more functional decline compared to non-MCI surgery group.

Furthermore, the node strength in DMN and SN had significant decline. (2) In inter-

network connectivity between three networks, the anti-correlated connections of DMN-

SN declined significantly after surgery. Increased connectivity of DMN-CEN was found,

but there were no significant changes in SN-CEN. MCI patients had more pronounced

DMN-SN functional decline. The intra-network connectivity and inter-network

connectivity had significant linear relationship. (3) The whole brain network resilience,

connection density, and mean functional connectivity had significant increase in brain

areas with low functional connections. The decreased connectivity were identified in

brain areas of bilateral insular, amygdala, and putamen, and increased connectivity

were found in precuneus, fusiform, and occipital cortex. This dissertation thus

suggested that the surgery had significant and acute impacts on brain functional

network organization in older adults.

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CHAPTER 1 INTRODUCTION

The human brain is a complex system which finely controls the body and the

mind. The brain consists of various anatomic areas which are in charge of different

functions and coordinate with each other to perform different tasks. The independent

and visible anatomic structures of the brain are closely connected to each other

functionally, which is invisible (Bullmore & Sporns, 2009; Sporns, 2013). The brain

functional connectivity is well maintained to coordinate different brain functions to

achieve the normal activities (Power et al., 2011). Functional magnetic resonance

imaging (fMRI) is one of the most important and noninvasive techniques to look into

these functional connections by extracting the functional information from the signals

generated during brain activities while it brings no interference to the brain. How to

understand the brain activities and its functions obtained from fMRI is an important

topic. The organization of the brain connectivity can be thought of as a network in which

every component needs to work cooperatively with each other to achieve specific

functions. Many methods based on network theory (Andrews-Hanna et al., 2007;

Koshimori et al., 2016; Menon, 2011; Schiff et al., 2005; Xia & He, 2017), signal

analysis (Anderson et al., 2011; Murphy et al., 2009; Murphy & Fox, 2017; Wen et al.,

2012; Zhang et al., 2016), and data mining (De Schutter, 2018; Floren et al., 2015;

Glaser et al., 2017; Vu et al., 2018) have been proposed to reconstruct the nature of the

brain according to our knowledge and understand the whole picture of the brain

functions.

The brain networks are not static but dynamic. Brain functions have different

properties within different ranges of normal aging (Andrews-Hanna et al., 2007;

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Krajcovicova et al., 2014). Older adults develop weakened or strengthened brain

functions compared to young adults. Many interferences from inside or outside of the

brain may be harmful to this delicate but susceptible system. Major surgery as a

perturbation to the brain may induce some rapid or long-term changes in the human

brain in older adults (Browndyke et al., 2017). The anesthesia applied during the

surgery may also cause some acute response or chronic injury to the brain functions

(Huang et al., 2018; Ramani, 2017).

Resting state networks (RSN) as important human brain networks can be

identified using resting state functional magnetic resonance imaging (rsfMRI) (Boveroux

et al., 2010; Li et al., 2017; Rombouts et al., 2005). The rsfMRI can be used in brain

mapping to evaluate the interactions between different brain regions when no task is

performed and the brain region shows spontaneous fluctuations in BOLD (Blood-

oxygen-level dependent) signals. Among the resting state networks, three important

networks are closely related to cognitive functions. They are default mode network

(DMN), central executive network (CEN), and salience network (SN).

Many intrinsic and external situations, such as trauma and brain disorders, can

induce changes in these three brain networks, which may lead to pathological

coordination between brain regions and the cognitive impairment. The postoperative

cognitive dysfunction (POCD) is the symptoms associated with the decline in cognitive

functions after the patients receive major surgery; memory functions and executive

functions are particularly vulnerable (Deiner & Silverstein, 2009). These effects can

cause long-term disorder which may last for several years and even a lifetime.

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This dissertation examined the brain functional changes to evaluate the acute

injury caused by the total knee replacement surgery in older adults. This research

included three aims:

1. Three important resting state brain networks defined using standard

coordinates were analyzed for the intra-network connectivity to examine the changes

within each network after surgery.

2. The connectivity between networks were analyzed to evaluate the interactions

among the three networks in coordinating the brain activity. The relationship between

inter-network connectivity and intra-network connectivity was examined to test their

relationship.

3. The whole brain connectivity was analyzed based on the standard mask

including 234 brain regions. The complex network properties were analyzed using the

graph theory to evaluate the properties of the brain network including resilience of the

network to perturbation or injury. The decreased and increased whole brain functional

connections in each brain area were examined to evaluate the changes in brain

networks caused by surgery or the anesthesia in older adults.

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CHAPTER 2 CHANGES IN INTRA-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS

FOLLOWING SURGERY

2.1 Introduction

Total knee arthroplasty (TKA) is one of the major surgeries which are normally

performed in older adults. This surgery also comes with side effects which cannot be

neglected which includes delirium and POCD (Moller et al., 1998; Rasmussen et al.,

2003). The mechanism of the POCD remains unknown and further investigation needs

to be conducted. Three important resting state networks including DMN, CEN, and SN

are closely related to cognitive functions. We hypothesized that examining these three

networks and their changes after surgery may shed light on the mechanisms of POCD.

The total knee replacement surgery with general anesthesia was reported to

show the acute cognitive decline within 48 hours after surgery in old adults (Boveroux et

al., 2010; Huang et al., 2018; Hudetz, 2012; Ramani, 2017). Important resting state

networks such as DMN were examined and all these networks show significant decline

in terms of the intra-network functional connectivity. The cognitive integrity was used to

predict the changes of the functional connectivity showing that patients with lower

cognitive integrity had more decline in DMN.

The cause of POCD is not well known so far, but several factors causing the

disruption of brain networks including anesthesia are thought to play a role. This effect

is pronounced in the older group of subjects when they receive major surgery. DMN,

CEN, and SN show significant decline of connectivity after surgery. However, whether

the declined functional connectivity are evenly distributed across all pairs of ROIs in the

network remains unknown (Liu et al., 2012; Xie et al., 2011; Ramani, 2017). Each ROI

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may be susceptible to the interference to different degrees, which needs to be looked

into.

According to the classification of the neuropsychological testing, patients can be

divided into two categories: MCI (mild cognitive impairment) patients and non-MCI

patients. The integrity of the brain functional network plays an important role in

maintaining the normal cognitive activities. The disorder of the cognitive status may

increase the susceptibility of brain networks to the trauma of surgery or anesthesia

(Huang et al., 2018; Browndyke et al., 2017; Sperling et al., 2011). This is another issue

that needed to be studied.

This chapter will focus on the intra-network connectivity in three cognitive

networks, DMN, CEN, and SN, before and after surgery to evaluate the changes

caused by the surgery. This may provide information on the prediction of long term

outcomes in the older adult group undergoing major surgery. First, we examined the

intra-network connectivity between each pair of ROIs in each of the three networks to

calculate the changes at the level of whole brain network and determine which network

suffers more injury after surgery. Second, we looked into the interactions between each

pair of ROIs within each network to find which pair of ROIs plays the most important role

in its network. Third, the importance of each ROI was examined using node strength in

each network to evaluate the susceptibility of each ROI having connectivity with the rest

of the other ROIs within each network. Last, the subtypes of MCI patients and non-MCI

patients in surgery group and non-surgery group were also examined for the

compromised functional connectivity to provide the evaluation of the insult or side

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effects of the surgery trauma for each group according to the classification of the

neuropsychological tests.

2.2 Methods

2.2.1 Participant

All participants who received the total knee arthroplasty (TKA) were recruited

through University of Florida orthopedic clinics; they were screened for dementia via a

telephone interview (Welsh et al., 1993; Cook et al., 2009), and enrolled between 2013

and 2016. This is part of the federally funded investigation. All participants in the non-

surgery group were recruited through University of Florida orthopedic clinics, community

mailings, and locally posted fliers. Non-surgery participants were selected as the control

group through a yoked review process to match individual surgery participant on age,

education, sex, and ethnicity or race. Non-surgery participants had to receive no

surgery for at least one year prior to enrollment. Both groups were recruited over the

same timeline and both were tested and scanned at the same time intervals. All

participants were selected to meet the following inclusion and exclusion criteria. 1) age

is 60 or older, 2) English is the primary language, 3) participants have osteoarthritis or

comparable joint pain, 4) participants have intact activities of daily living, and 5)

participants have baseline neuropsychological testing unsupportive for dementia criteria

per Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition (American

Psychiatric Association, 2013). Additional exclusion criteria included as following: 1) any

other major surgery within the study timeline, 2) history of head

trauma/neurodegenerative illness, 3) documented learning or seizure disorder, 4) less

than a sixth-grade education, 5) substance abuse in the last year, 6) major cardiac

disease, 7) chronic medical illness known to induce encephalopathy, 8) implantable

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device precluding an MRI, and 9) an unwillingness to complete the MRI. Two

neuropsychologists reviewed the baseline data to confirm that test scores met the

expected ranges for non-demented individuals.

A total of 116 patients out of 232 surgery patients referred by the study surgeon

(HP) and contacted for study inclusion agreed to join this study with 73 subjects meeting

inclusion and exclusion criteria and completing baseline neuropsychological

assessment and MRI scanning. Four surgery participants were excluded from the data

analysis due to presence of pre-existing silent strokes (2 participant) and MRI post-

surgery scanner complications (2 participants). For the surgery group, the final dataset

included 69 surgery participants who completed baseline assessment, pre surgery and

post-surgery rsfMRI. For the non-surgery control group, a total of 68 participants out of

104 participants were enrolled. Two subjects were excluded due to a learning disorder

in neuropsychological testing and one was excluded for missing rsfMRI. The final

dataset included 65 non-surgery participants who completed the baseline assessment,

pre surgery and post pseudo-surgery rsfMRI.

This research was approved by the University of Florida Institutional Review

Board in Gainesville, Florida. The research was conducted in accordance to principles

of the Declaration of Helsinki. All participants were informed appropriately and signed

the consents.

2.2.2 Procedures

A schematic diagram was shown in Figure 2-1 to illustrate the whole procedures

of this study. Participants completed a phone cognitive screening (Cook et al., 2009)

and a comprehensive history and systematic interview to confirm inclusion and

exclusion criteria and the following tests were conducted: an in-person comorbidity

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rating (Charlson et al., 1987) of activities of daily living (Lawton & Brody, 1969),

neuropsychological assessment, and the first brain image scanning of MRI. All

participants in the surgery group received surgery of TKA. At the same time, the

participants in the control group were assigned a date for pseudo-surgery. All

participants received the post-surgery brain image scanning of MRI within 48 hours after

the surgery for patients and pseudo-surgery for controls. The same examiner completed

all tests for the surgery group and the non-surgery group. Trained raters who are blind

to participant’s condition scored the data and the data were double entered.

All participants including the surgery group and non-surgery group were

classified as MCI or non-MCI according to the comprehensive criteria determined by

Jak and Bondi and colleagues (Jak et al., 2009). An individual was classified as the

MCI, when the individual’s scores fall below one standard deviation in at least two

measures within any one domain defined by MCI criteria. The comprehensive criteria

were not only used to classify MCI but also used for the classification of the subtypes of

MCI including amnestic and non-amnestic. This method was recommend instead of

conservative criteria because it has the stable balance between sensitivity and

specificity for testing impairment and has the advantage of less false negatives (Jak et

al., 2009). MCI patients was classified with impairment domains listed as following: 1)

Attention Domain - Part A of the Trail Making Test (Corrigan & Hinkeldey, 1987), and

the letter number sequencing and digit span forward subtests of the WAIS-III (David

Wechsler, 1997); 2) Executive Domain: Part B of the Trail making test (Corrigan &

Hinkeldey, 1987), the total achievement score from the Tower Test from the Delis-

Kaplan Executive Function System (D-KEFS) test (Dean C. Delis, Edith Kaplan, 2001),

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and the Stroop Color Word test (Stroop, 1935); 3) Language Domain: Animal (Lezak,

2012) and letter fluency (Spreen & Strauss, 1998) and the Boston Naming Task

(Kaplan, 1983); 4) Visuospatial Domain: Copy portion of the Rey-Osterrieth Complex

Figure Design (Rey, 1941; Osterrieth, 1944), the matrix-reasoning portion of the

Weschler Adult Intelligence Scale – third edition (David Wechsler, 1997), and the

Judgment of Line Orientation (Benton, 1983); 5) Memory Domain: Hopkins Verbal

Learning Task (Brandt, 1991), the Logical Memory Delay portion of the Wechsler

Memory Scale – third edition (David Wechsler, 1997), and the delay portion of the Rey-

Osterrieth Complex Figure Design (Rey, 1941; Osterrieth, 1944).

2.2.3 Anesthesia and Surgery Protocol

The procedures for anesthesia and surgery followed the standardized steps. All

the TKA surgeries were done by the same surgeon for all surgery patients according to

the standard protocol. Surgery patients received the intravenous midazolam (1–4 mg)

for reducing anxiety followed by continuous femoral nerve blocks (CFNB) and single-

injection subgluteal sciatic nerve blocks with 20ml and 30ml of 0.5% ropivacaine as a

bolus injection, respectively. The CFNB was combined with 0.2% ropivacaine at the

infusion rate of 10 ml/hour during the surgery. Propofol, fentanyl, and rocuronium were

used for anesthesia induction and intubation but no opioids were used. Surgery patients

were ventilated with the mixture of air and oxygen to maintain the end-tidal carbon

dioxide at 35±5 mm. The anesthesia was carefully maintained using both inhaled

isoflurane and intravenous fentanyl and rocuronium. A tourniquet was elevated to

250mmHg before incision and deflated when the surgery was close to closure. Bony

preparation used the intramedullary instrumentation for the femoral side and the

extramedullary instrumentation for the tibial side, respectively. Both the anterior and

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posterior cruciate ligaments were removed and replaced by the artificial implants

attached to the bone using bone cement for all surgery patients. All the TKA surgeries

lasted 2-3 hours in operating room.

2.2.4 Neuroimaging

Structural and resting state functional MRI was collected before surgery and

within 48 hours after surgery for the surgery group and the non-surgery group (pseudo

surgery), while the delirium was assessed within 24 hours after surgery using the

Confusion Assessment Method (Inouye et al., 1990). In order to avoid the difficulty of

focusing on the cross during the MRI data collection especially rsfMRI after surgery, the

eye closed condition was chosen for resting state fMRI recording.

All participants including the surgery group and the non-surgery group took the

MRI for T1 weighted image, rsfMRI, task fMRI, and DTI (3T Siemens Verio; 8 channel

head coil). T1 weighted images were acquired using the following parameters: TR:

2500ms; TE: 3.77ms; 176 sagittal 1mm3 slices, 1 mm isotropic resolution; 256x256x176

matrix, 7/8 phase partial Fourier, total acquisition time: 9:22 minutes. Resting state fMRI

were acquired while the participant’s eyes were closed using the following parameters:

TR: 2000ms; TE: 30ms; 36 transverse slices; 3.5 mm3 isotropic voxel size,

225x225x126 matrix, GRAPPA, total acquisition time: 7:38 minutes.

2.2.5 Functional MRI Data Preprocessing

The resting state fMRI data of all subjects were preprocessed firstly according to

the methods described as follows before data analysis. The first six functional scans of

each patient were removed to eliminate transients during the MRI acquisition. The

remaining fMRI slices were preprocessed using the method of SPM provided by

www.fil.ion.ucl.ac.uk/spm. The slice timing correction was conducted to eliminate

25

acquisition delays across slices. The motion artifacts were corrected by realigning all

functional MR images to the reference image after timing correction. Following the

motion correction, all the functional MR images were co-registered to the T1 structural

image, which were normalized to the standard MNI152 T1 template, and these slices

were resampled at the resolution of 3mm×3mm×3mm (X, Y, and Z, respectively).

Functional images in the MNI space were then smoothed with an 8mm full width at half

maximum (FWHM) isotropic Gaussian kernel for data analysis.

2.2.6 FMRI Regions of Interest Selection

Four resting state networks (RSNs): default mode network (DMN), central

executive network (CEN), salience network (SN), and visual network (VN) were chosen

according to the standard coordinates defined by Power and colleagues (Power et al.,

2011) and Yeo and colleagues (Thomas Yeo et al., 2011). The regions of interest

(ROIs) for the four RSNs were defined as following. DMN consists of 6 ROIs: medial

prefrontal cortex (mPFC), posterior cingulate cortex (PCC), bilateral angular gyrus (AG),

and bilateral temporal (LT); CEN includes bilateral dorsolateral prefrontal cortex

(DLPFC) and bilateral inferior parietal lobule (IPL); SN consists of dorsal anterior

cingulate cortex (ACC) and bilateral anterior insula (IN); and the VN consists of bilateral

central visual cortex (V1C), bilateral peripheral visual cortex (V1P), bilateral extrastriate

visual cortex in the central fields (ExC), and bilateral extrastriate visual cortex in the

peripheral fields (ExP). The ROIs representing each brain region were defined using a

5mm sphere in radius centered at the coordinates of that region to extract the BOLD

signals for further analysis. Table 2-1 listed coordinates of these regions. Here the main

interest is in the changes of the three major cognitive networks DMN, SN and CEN. VN

is mainly a sensory network and is included here as a control network.

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2.2.7 Variables for Regression

Many factors may contribute to the magnitude of the BOLD signals besides the

brain activities themselves. Cognitive reserve similar to brain reserve may result in the

discrepancy in brain integrity and cognitive functioning. Cognitive reserve is more about

the capability of the brain based on previous cognitive abilities to protect the brain

functions from pathological attacks to maintain the normal cognitive activities (Stern,

2002). The amount of cognitive reserve can prevent the pathological progression from

developing dementia (Valenzuela et al., 2006). Especially, cognitive reserve has shown

the possibility to modulate functional connectivity in patients with MCI (Bozzali et al.,

2015). Years of education is widely used for cognitive reserve evaluation (Valenzuela et

al., 2006). Thus, it was included as one variable for differentiating the cognitive reserve

for surgery groups and non-surgery groups.

Some variables such as morphine (Khalili-Mahani et al., 2012) and pain (Loggia

et al., 2013; Kuner & Flor, 2017) were also considered related to the magnitude of

functional connectivity. Morphine equivalent dosages (MED) were adopted to evaluate

the effects on postoperative surgery group using a published conversion algorithm

(Dowell, Haegerich, & Chou, 2016). The MED was potentially active if the dose was

administered within six hours before the MRI scanning after surgery. Pain assessment

ratings (0-100; 100=worst) were acquired before and after surgery prior to the MRI

scanning. If participants missed pain assessment ratings, a pain rating was imputed

based on the average of 10 imputed scores. Imputations were calibrated in SPSS using

a regression only including other participants who shared the same group for both

surgery and non-surgery patients.

27

Age and gender can also show the interaction with functional connectivity in

resting state networks (Goldstone et al., 2016). Thus, age, gender, pain, MED, and

education were taken as covariates in functional connectivity. To exclude the effects

caused by these variables on functional connectivity coefficients, the linear regression

was applied to regress out pain and education in both pre surgery group and non-

surgery group; pain, education, and active MED in post-surgery group; pain and

education in post non surgery group as well as age and gender for all groups. The

residual after linear regression plus mean value was used to replace the original

functional connectivity for all groups.

The linear regression formula is described as follows:

𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 휀 (2-1)

Where Y is dependent variable, β0 is intercept, β1, β2 are slope coefficients, X1, X2, …

are independent variable, ε is the residual term.

2.2.8 Functional Connectivity Analysis

The rsfMRI functional connectivity was evaluated based on the cross correlation

between the time series of BOLD signals extracted from the brain regions defined as

the ROIs. Nine nuisance signals were regressed out including 6 movement variables

and 3 averaged signals of white matter, cerebrospinal fluid, and global signal. The time

series were then filtered with a finite impulse response (FIR) band-pass filter (between

0.01 and 0.1 Hz). The filtered BOLD signals were averaged across all voxels to obtain

one mean signal representing each ROI. Motion scrubbing procedure for motion

censoring (Power et al., 2012) was conducted on the BOLD signals to reduce the

potential adverse effects of abrupt movements, no matter how small, on functional

connectivity. The functional connectivity between each pair of ROIs within each resting

28

state network was quantified using the Pearson cross correlation between each pair of

BOLD signals. Age, gender, pain, MED, and education (see above) were regressed out

from the functional connectivity between each pair of ROIs.

1. The mean functional connectivity of each RSN at the network level was

calculated by averaging the corrected cross correlation values of all pairs of ROIs within

each RSN for each subject (Fornito et al., 2016; Rubinov & Sporns, 2010). Both

preoperative and postoperative resting state functional connectivity of each subject

were calculated to evaluate the connectivity changes related to surgery.

The Pearson correlation coefficient is defined as follows:

𝜌𝑋,𝑌 =𝐸[(𝑋−𝜇𝑋)(𝑌−𝜇𝑌)]

𝜎𝑋𝜎𝑌 (2-2)

Where E is the expectation, µX is the mean of X, µY is the mean of Y, σX is the standard

deviation of X, σY is the standard deviation of Y.

The internal connectivity Cs is defined as follows:

𝐶𝑠 =∑ 𝜀𝑖,𝑗𝑖,𝑗∈𝑠

𝑁𝑠×(𝑁𝑠−1) (2-3)

Where Ns is the number of nodes within a RSN s, and εi,j is the existing edge within

module s, i≠j.

2. At node level, the functional connectivity between each pair of nodes in each

individual network were calculated to compare the difference between pre surgery and

post-surgery.

3. The node strength of each ROI which is the sum of the functional connectivity

between the ROI and the rest of other ROIs in each network was also calculated to

evaluate the importance of the ROI in the network at node level.

29

4. The subtypes of the MCI and non-MCI surgery groups were also analyzed at

node level and at the network level to examine the significant effects of the surgery on

the subtypes.

The node strength Si is defined as follows:

𝑆𝑖 = ∑ 𝑤𝑖𝑗𝑁𝑗=1 (2-4)

Where N is the number of nodes, wij is the weighted connectivity.

2.2.9 Statistical Analysis

An independent samples t-test was applied to compare surgery patients to non-

surgery participants on demographic variables. A one-way ANOVA was used to

examine demographic differences of MCI group between surgery and non-surgery

participants (MCI surgery group vs. non-MCI surgery group vs. MCI non-surgery group

vs. non-MCI non-surgery group). Bonferroni corrected Post Hoc analyses were

conducted on significant interactions between groups to find the group difference.

The difference of average connectivity in all RSNs between surgery group and

non-surgery group were examined using a mixed repeated measures analysis of

variance (ANOVA). All significant interactions were assessed using pairwise

comparisons, Bonferroni corrected. The difference in node strength within each network

between groups (surgery and non-surgery) were tested. To compare the difference

between pre-surgery and post-surgery for each group, paired sample t-test were applied

between pre and post time points with correction for multiple comparison using

Bonferroni correction. The statistic Cohen’s D was calculated (Faul et al., 2009; Faul et

al, 2007) to compare the differences between groups.

The Cohen’s D is determined as follows:

30

𝐷 = 𝑀2−𝑀1

√((𝑆𝐷1)2+(𝑆𝐷2)2)/2 (2-5)

Where M1 and M2 are the mean values, SD1 and SD2 are the standard deviation.

For the comparison between MCI group and non-MCI group in average

connectivity (pre-surgery and post-surgery), a paired sample t-test was applied to

compare pre and post time points for MCI surgery, non-MCI surgery, MCI non-surgery,

non-MCI non-surgery. The statistic Cohen’s D was also calculated to compare the

difference between groups.

2.3 Results

2.3.1 Intra-network Connectivity

Comparison of participant characteristics. Independent sample t-test was

examined to show that the surgery and non-surgery groups had no difference on age,

education, gender, race, ventricular volume, head size, pre-surgery pain at the time of

rsfMRI, and the interval days between pre-surgery and post-surgery rsfMRI. Although

non-surgery participants were matched with surgery participants demographically

through a thorough screening process, the surgery group had significantly lower

cognitive scores than the non-surgery group. The surgery group, however, had

significantly more pain at the time of post-surgery rsfMRI, which is expected. Four

surgery participants were identified as having delirium lasting less than one day after

surgery, but no participants had evidence of delirium at the time of the post-surgery

rsfMRI. Table 2-2 included the demographic characteristics for surgery and non-surgery

participants.

Intra-network changes after surgery. The effects of the surgery on the intra-

network connectivity were examined using the mixed repeated measures ANOVA for

31

significance in RSNs including DMN, CEN, SN and VN. Figure 2-2 included all four

networks for comparison between pre-surgery and post-surgery. For the connectivity in

DMN, the comparison between pre-surgery and post-surgery for surgery group and

non-surgery group were shown in Figure 2-2 and Figure 2-3 A. At network level, the

Pearson correlation in post-surgery group has significant decline in the intra-network

connectivity compared to the pre-surgery in the surgery group, as shown in Figure 2-3 A

(p<0.05). The non-surgery did not show significant changes between pre-pseudo

surgery and post-pseudo surgery in Figure 2-3 A (p>0.05). In order to look into the

effects of surgery on each pair of the connectivity in the DMN, the comparison were also

tested for each subject at node level shown in Figure 2-2. Several pairs of connectivity

including the ROIs associated with mPFC and PCC have significant decline in surgery

group (p<0.05). In non-surgery group, no significant declined was found across all pairs

of ROIs in DMN. All the statistic results were included in Table 2-4 for surgery and non-

surgery groups. Table 2-5 included the comparison of each pair of ROIs in surgery

group.

The comparison of CEN between pre-surgery and post-surgery for surgery group

and non-surgery group were shown in Figure 2-2 and Figure 2-3 B. At network level, the

Pearson correlation in post-surgery group has significant decline in the intra-network

connectivity compared to the pre-surgery in surgery group shown in Figure2-3 B

(p<0.05). The non-surgery did not show significant changes between pre-pseudo

surgery and post-pseudo surgery in Figure 2-3 B (p>0.05). In order to look into the

effects of surgery on each pair of the connectivity in the CEN, the comparison were also

examined for each subject at node level in Figure 2-2. Several pairs of connectivity have

32

significant decline in surgery group (p<0.05), including lIPL-rIPL. In the non-surgery

group, no significant declined was found across all pairs of ROIs in CEN. The statistic

results were listed in Table 2-4 for surgery and non-surgery groups. Table 2-5 included

the comparison of ROIs before and after surgery.

The comparison of SN between pre-surgery and post-surgery for surgery group

and non-surgery group were shown in Figure 2-2 and Figure 2-3 C. At network level, the

Pearson correlation in post-surgery group has significant decline in the intra-network

connectivity compared to the pre-surgery in surgery group shown in Figure 2-3 C

(p<0.05). The non-surgery group did not show significant changes between pre-pseudo

surgery and post-pseudo surgery in Figure 2-3 C (p>0.05). To examine the effects of

surgery on each pair of the connectivity in the SN, the comparison were also tested for

each subject at node level shown in Figure 2-2. Two pairs of connectivity associated

with dACC have significant decline in surgery group (p<0.05) including dACC-lIN and

dACC-rIN. In non-surgery group, no significant declined was found across all pairs of

ROIs in SN. The results were shown in Table 2-4 for surgery and non-surgery groups, in

Table 2-5 for surgery comparison.

For the control network VN, the comparison between pre-surgery and post-

surgery for surgery group and non-surgery group were examined in Figure 2-2 and

Figure 2-3 D. At network level, the Pearson correlation post-surgery did not show

significant changes in connectivity compared to pre-surgery in surgery group, as shown

in Figure 2-3 D (p>0.05). The non-surgery group had no significant changes between

pre-pseudo surgery and post-pseudo surgery in Figure 2-3 D (p>0.05). At node level,

the comparison were also tested for each subject to check the effects of surgery on

33

each pair of the connectivity in the VN in Figure 2-2. No pairs of connectivity have

significant decline in surgery group (p>0.05). In non-surgery group, no significant

declined was found among all pairs of ROIs in VN. It means that surgery has no

significant effects on VN. All the statistical results were listed in Table 2-4 for surgery

and non-surgery groups, and in table 2-5 for surgery comparison.

2.3.2 Node Strength of Intra-network Connectivity

The node vulnerabilities to surgery was calculated to evaluate the changes of

each node’s functional connectivity after surgery compared to pre surgery. The sum of

the functional connectivity between a ROI and the other ROIs within the same network

was calculated. The results were shown in Figure 2-6, Figure 2-7 and Table 2-5 for

graphical representation and numerical values.

For the connectivity in DMN, all the ROIs showed significant decline in node

strength after surgery in surgery group (p<0.05). In the non-surgery group, the node

strength did not show any significant changes between pre and post pseudo surgery

(p>0.05).

For the connectivity in CEN, some ROIs but not all had significant decline in node

strength after surgery (p<0.05). In the non-surgery group, the node strength did not

show any significant changes between pre and post pseudo surgery (p>0.05).

For the connectivity in SN, all the ROIs showed significant decline in node

strength after surgery in surgery group (p<0.05). In the non-surgery group, the node

strength did not show any obvious changes between pre and post pseudo surgery

(p>0.05).

For the control network VN, no ROIs showed significant changes in node

strength after surgery in both surgery group and non-surgery group (p>0.05).

34

2.3.3 MCI versus Non-MCI

Comparison of participant characteristics. The surgery group (69 patients)

included 13 MCI patients and 56 non-MCI patients who were cognitively normal

according to the criteria for MCI. The non-surgery group (65 controls) consisted of 10

MCI participants and 55 non-MCI participants.

One-way ANOVA results indicated that all groups including MCI and non-MCI in

the surgery group, and MCI and non-MCI in the non-surgery group showed no

difference in age, sex, race, head size, baseline pre-surgery pain level at the time of the

rsfMRI, days between baseline pre-surgery rsfMRI and post-surgery rsfMRI. These

groups, however, significantly differed on education, general cognitive screener, and

post-surgery rsfMRI pain level at the time of the rsfMRI. The analyses showed that the

MCI surgery group had significantly less education years and lower scores on the

cognitive screener than the non-MCI surgery and non-MCI non-surgery group. Because

medication levels were only calculated for the TKA surgery group, an independent

sample t-test was analyzed for the MCI surgery group and non-MCI surgery group. The

results showed no differences between morphine equivalent dose after surgery

(p=0.331). The surgery and non-surgery patient characteristics for MCI and non-MCI

groups were listed in Table 2-3.

Intra-network changes of MCI in surgery. The connectivity of the network in

pre-surgery and post-surgery were compared for the MCI surgery group (13 patients)

and non-MCI surgery group (56 patients). The MCI surgery group showed significant

decline in DMN and SN after surgery examined by paired t test (p<0.05). The non-MCI

surgery group also showed significant drop after surgery (p<0.05). The MCI surgery

group had large Cohen’s D values compared to the non-MCI surgery group. However,

35

there were no significant changes observed in CEN and VN for MCI and non-MCI

groups. The MCI results were included in Figure 2-4 and Table 2-6. The node strength

were also compared between MCI and non-MCI for surgery group and non-surgery

group in Figure 2-8. The MCI group showed more decline and big effect sizes compared

to non-MCI in DMN, SN, CEN, and VN. In DMN and SN, the node strength in MCI group

had big Cohen’s D values and maintained the significance in most of nodes even the

sample size in MCI was relatively small. CEN, however, did not show significance

between pre and post-groups in MCI and non-MCI except lIPL.

The same analyses were also applied to MCI non-surgery group and non-MCI

non-surgery group. No significant changes between pre-surgery and post-surgery were

identified in MCI non-surgery group and non-MCI non-surgery group. The results were

also shown in Figure 2-5 and Table 2-7.

2.4 Discussion

The functional connectivity has an acute decline in three major RSNs including

DMN, CEN and SN after the patients received the surgery of total knee replacement

surgery within 48 hours in older adults. No significant changes, however, in functional

connectivity after surgery were identified in VN. These results indicated that the

changes related to TKA surgery are selective at the network level with the major

cognitive networks being more vulnerable. At the node level, the changes were not

evenly distributed across all pairs of connectivity between ROIs in each network. These

findings also suggested that the effects of the surgery on connectivity were selective

and these changes showed different patterns on different networks. The changes in

node strength provided information on the vulnerability of each ROI within the network.

In DMN, the posterior nodes had more decline than the anterior nodes. In SN, the

36

dorsal anterior cingulate cortex had the most significant decline among all ROIs. The

significant changes in connectivity, however, were not observed in CEN and VN.

When participants were classified according to the cognitive criteria, the

connectivity changes were also quantified in MCI and non-MCI groups to evaluate the

postoperative effects in cognitive subtypes. The connectivity changes in MCI group

were more pronounced in DMN and SN compared to the non-MCI group.

Default Mode Network. The DMN is active during rest or internal thought

processes and deactivated during external tasks (Mason et al., 2007; Spreng et al.,

2009). It consisted of several brain areas which can be separated into anterior and

posterior subsections. The mPFC can represent the anterior subsection associated with

the self-referential mental thought and PCC the posterior brain subsection related to

episodic memory retrieval and semantic memory (Damoiseaux et al., 2008; Sestieri et

al., 2011; Xu et al., 2016).

In this chapter, we showed that the connectivity in DMN had significant drop after

TKA surgery. This phenomenon has been reported previously in other studies on the

decline of functional connectivity in surgery and diseases (Browndyke et al., 2017;

Huang et al., 2018; Ramani, 2017). In addition to these previous findings, the changes

in connectivity between nodes after surgery were examined within each network as well

as the mean connectivity at the network level. The PCC and bilateral AG in the posterior

subsystem are more vulnerable to the surgery than other ROIs in DMN. In surgery, the

anesthesia can disrupt the connectivity from lower levels to high levels and thus result in

the impairment of the integration of network. Previous studies revealed that the light

sedation of the anesthesia was related to the disruption of the communication between

37

anterior and posterior subsystems by decreasing the connectivity between PCC and the

other ROIs and causes the loss of consciousness (X. Liu et al., 2012; Ramani, 2017;

Xie et al., 2011).

Aging may be another reason which is associated with the decline of the

functional connectivity after surgery. In older adults, the cognitive performance is

relatively low because of the reduction of functional connectivity between anterior and

posterior subsystems in DMN (Andrews-Hanna et al., 2007). The aged brain structures

and functions may suffer more from anesthesia after surgery within 48 hours. This effect

may be acute or last longer. Our results suggested the disconnection between anterior

and posterior may last at least 48 hours after the restored consciousness, while this

disconnection is desirable for unconsciousness during the surgery for the purpose of

anesthesia. This decoupling side effect may be more obvious or serious for these

patients who have cognitive deficits or impairments. In DMN, the AG is mainly

associated with cognitive processes, especially lAG which is involved in semantic

processing, concept integration, and comprehension (Seghier, 2013). lAG showed the

largest decline among all ROIs in DMN after surgery. According to the functions of PCC

and AG in cognitive processing, the decline of the functional connectivity in these areas

may be considered as harbingers of the POCD (Browndyke et al., 2017).

Central Executive Network. The CEN is closely associated with the executive

tasks including working memory, problem solving, and decision making (Dosenbach et

al., 2006; Fang et al., 2016; Menon, 2011). Different from DMN, CEN shows increases

in activation during external tasks. In this study, the mean connectivity of CEN showed

significant drop after surgery which is in agreement with our previous findings (Huang et

38

al., 2018). The connectivity between each pair of ROIs in CEN, however, did not show

significant changes except for the pair lIPL-rIPL, even though there is a trend of decline

in connectivity in other pairs. These declines in CEN connectivity after surgery may

result in low cognitive performance including working memory.

Salience Network. Salience network is important in both internal and external

attention processing. It is critical for detecting behavioral stimuli and plays an important

role in coordinating the networks dynamically, especially the interactions between DMN

and CEN, in response to these events (Menon, 2011; Menon & Uddin, 2010; Seeley et

al., 2007). After surgery, the dACC had the significant decline compared to the other

two ROIs including lIN and rIN. The reduced intra-network connectivity may indicate the

compromised ability of dACC in attention or cognitive processing.

The SN is in charge of the switching of the engagement between the DMN and

the CEN according to the cognitive tasks. The DMN and CEN are anti-correlated and

alternatively response to the internal attention or external attention, respectively. In

response to the internal cognitive tasks, the DMN is activated and CEN is deactivated to

process these activities such as memory retrieval. When the response to the external

events is required, the DMN is deactivated and the SN is activated to be involved in the

task processing. The decreased connectivity in SN may inhibit the coordination between

DMN and CEN to respond to different tasks properly and quickly. DMN as the important

resting state network also showed the reduced intra-network connectivity as well as the

CEN. The coordination between DMN and SN is especially important not only in resting

state but also in task related performance. The connectivity strength between PCC in

DMN and dACC in SN is strongly associated with the task performance such as during

39

working memory tasks. When all these three networks showed the reduced intra-

network connectivity at the same time, the coordination between these three networks

was impeded and this side effects at least lasted for 48 hours after the surgery. These

weakened correlation and anti-correlation may be used to predict the susceptibility of

the network and the declined cognitive performance (Chen et al., 2016; Deiner et al.,

2009; He et al., 2014; Price et al., 2008). The inter-network connectivity analysis among

these three networks is included in Chapter 3.

Effects of Mild Cognitive Impairment. The participants included in this

dissertation are all older adults. Some participants developed mild impairment in brain

functions due to normal aging. According to the cognitive criteria for cognitive

impairment, all the participants including the surgery group and non-surgery can be

classified into two categories: MCI and non-MCI. In surgery group, the functional

connectivity in MCI subtype had more declined connectivity than the connectivity in the

non-MCI subtype within surgery patients. This phenomenon was clearest in DMN and

SN among the surgery patients with MCI.

The DMN in MCI group had impaired functional integrity, which was reported by

previous reports. The connectivity within DMN can be weakened and the deactivation

during the tasks such as visual coding and working memory reduced (Lee et al., 2016;

Rombouts et al., 2005). The weakened connectivity is also shown in the progression to

AD (Wu et al., 2011). Opposite In other cases, the increased connectivity in DMN can

also be observed which can be taken as the compensation for the dysfunction (Gardini

et al., 2015; Li et al., 2017). The abnormal connectivity has also been identified in SN

and the interaction between SN and CEN in MCI (He et al., 2014) as well as in AD

40

(Badhwar et al., 2017; Krajcovicova et al., 2014). The decline in functional connectivity

in MCI group may result in more serious cognitive dysfunctions than normal older

adults. In this chapter, the further weakened functional connectivity has been verified in

MCI group after the surgery and the surgery itself or anesthesia may exaggerate the

symptoms of cognitive impairment. The changes in connectivity can be quantified as a

biomarker to predict the postoperative cognitive changes in surgery with general

anesthesia.

Effects of General Anesthesia. General anesthesia may cause changes of the

functional connectivity of the brain. Our findings in this chapter is in agreement with the

previous reports. During general anesthesia, the breakdown or the rapid fragmentation

of the brain networks can be observed within the network or between networks during

unconsciousness (Boveroux et al., 2010; Lewis et al., 2012). Lewis found that during the

anesthesia, the communications between cortical networks with 2cm or greater distance

were impaired and the networks were disconnected consequently. This study was

conducted using the electrodes implanted in the temporal lobes. Boveroux reported that

the propofol-induced decrease in consciousness is linearly related to the decreased

DMN and CEN connectivity. The connectivity in low-level cortices, however, such as

auditory and visual networks was preserved during the sedation stages. These findings

strongly suggested that the general anesthesia can induce the acute disruption of the

brain networks. This disruption can last at least 48 hours after the surgery due to the

insult of the anesthesia. This effect may be exaggerated and the recovery from the

anesthesia may be impeded in MCI subtype.

41

This chapter mainly focused on evaluating the changes after surgery in the intra-

network connectivity among the three important resting state networks. These findings

open several important directions for future research. The declined connectivity may

lead to decline in cognitive performance. The task related fMRI study besides the

resting state fMRI can be conducted in the future to evaluate the effects of surgery on

behavior and the task activity of related networks. Due to the limited resources and time

span, the number of the MCI participants is relatively small in our study. Further study

will recruit more MCI participants to expand the sample pool and enhance statistical

power. Beside the acute effects of the surgery accomplished in this study, the long-term

effects of the postoperative cognition is important to evaluate the risk of the surgery or

the anesthesia in older adults and to guide the development of pre surgery intervention

to reduce the side effects on brain functions.

42

Table 2-1. The MNI Coordinates of the Regions of Interest (ROI)

NETWORK ROI MNI

BA X Y Z

DMN PCC 1 -51 29 23

mPFC -1 61 22 10 lAG -48 -66 34 39

lLT -65 -23 -9 21

rAG 53 -61 35 39

rLT 61 -21 -12 21

SN ACC -1 10 46 6 lIN -38 14 5 13

rIN 37 18 5 13

CEN lDLPFC -44 27 33 9

lIPL -53 -50 39 39

rDLPFC 46 28 31 9 rIPL 54 -44 43 40

VN lV1C -13 -100 -8 18

lV1P -16 -74 7 17

lExC -32 -89 -1 18

lExP -3 -74 23 18

rV1C 13 -100 -8 17

rV1P 16 -74 7 17

rExC 32 -89 -1 18

rExP 3 -74 23 18

43

Table 2-2. Participant Characteristics: Surgery Group versus Non-Surgery Group.

Demographic TKA (n = 69) NS (n = 65)

Mean ± SD Mean ± SD

Age 69.35±7.12 (range: 60–85) 68.37±5.50 (range: 60–83)

Education 15.23±2.83 (range: 10–23) 16.11±2.64 (range: 12–24)

Sex (M:F) 33:36 28:37

Race (W:NW) 61:8 61:4

TICS 36.71±4.23 (range: 26–47)* 38.55±3.25 (range: 30–44)

PreMRI Pain 12.57±19.87 (range: 0–75) 7.67±14.72 (range: 0–70)

PostMRI Pain 40.10±22.98 (range: 0–100)* 7.05±10.61 (range: 0–40)

PrePost MRI day span

8.77±5.91 (range: 3-41) 7.36±3.16 (range: 2-21)

MED 11.57±10.87 (range: 0–37.50) --------------------------------

Note. TKA = surgery group; NS = non-surgery

44

Table 2-3. Participant Characteristics: MCI versus Non-MCI in Different Groups.

Demographic

MCI-TKA (n =

13) TKA (n = 56)

MCI-NS (n =

10)

CN-NS (n =

55)

Mean ± SD Mean ± SD Mean ± SD Mean ± SD

Age 72.38±8.22

(range: 60–85)

68.64±6.73

(range: 60–85)

66.6±5.72

(range: 61–81)

68.9±5.61

(range: 60–83)

Education 13.08±2.36*

(range: 10–17)

15.76±2.73

(range: 12–23)

14.85±3.06

(range:12–22)

16.30±2.55

(range: 9–24)

Sex

(M:F) 7:6 26:30 5:5 22:33

Race

(W:NW) 12:1 49:7 8:2 53:2

TICS 33.38±3.57*

(range: 26–38)

37.48±4.01

(range: 27–47)

35.90±2.38

(range: 32–40)

38.84±3.13

(range: 30–44)

(range:

1.37x106–

1.87x106)

(range:

1.30x106–

1.89x106)

(range:

1.29x106–

1.67x106)

(range:

1.27x106–

1.89x106)

PreMRI Pain 14.62±22.50

(range: 0–75)

12.09±19.40

(range: 0–75)

8.30±11.84

(range: 0–30)

7.56±15.27

(range: 0–70)

PostMRI

Pain

46.69±23.09*

(range: 0–80)

38.57±22.89

(range: 2-100)

10.90±13.76

(range: 0–40)

6.34±9.2

(range: 0–40)

PrePost MRI

day span

8.54±5.38

(range: 3-21)

8.82±6.07

(range: 3-41)

6.90±2.38

(range: 3-11)

7.45±3.29

(range: 2–21)

MED 14.23±12.05

(range: 0–30)

10.96±10.60

(range: 0–

37.50)

--------------------- -------------------

Note. * denotes significant differences, where MCI-TKA group is significantly lower on education and TICS and higher on PostMRI pain. MCI-TKA = MCI in surgery group; TKA = non-MCI in surgery group; MCI-NS = MCI in non-surgery group; CN-NS = non-MCI in non-surgery group.

45

Table 2-4. Mixed Repeated ANOVA between Surgery and Non-Surgery

df F p partial η2

DMN

Time point 1,132 9.288 0.003** 0.066

Group* Time point 1,132 20.856 <.001*** 0.136

CEN

Time point 1,132 1.146 0.286 0.009

Group* Time point 1,132 6.851 0.010* 0.049

SN

Time point 1,132 6.906 0.010* 0.05

Group* Time point 1,132 15.3 <.001*** 0.104

VN

Time point 1,132 1.298 0.257 0.01

Group* Time point 1,132 0.183 0.67 0.001 Note. * = p<.05, ** = p<.01, *** = p<.001

46

Table 2-5. Changes in Node Strength in Surgery Group

Mean Sum Correlation

(SD)

RSN ROI Pre Post t(df) p CI d

DMN

PCC 1.587(.626) 1.142(.722) 4.442(68) <.000*** .25-.65 .66

rAG 1.530(.662) 1.187(.657) 3.912(68) <.000*** .17-.52 .52

lAG 1.790(.549) 1.323(.641) 5.551(68) <.000*** .30-.64 .78

mPFC 1.056(.878) .547(.814) 4.440(68) <.000*** .28-.74 .62

rLT 1.031(.619) .702(.677) 3.608(68) .001** .09-.15 .51

lLT 1.034(.579) .797(.661) 2.929(68) .005** .08-.08 .38

CEN

lDLPFC .753(.443) .663(.411) 1.645(68) .105 -.02-.20 .21

rDLPFC .844(.446) .716(.416) 2.251(68) .028 .01-.24 .30

lIPL .822(.438) .693(.421) 2.479(68) .016 .03-.23 .30

rIPL .934(.402) .794(.445) 2.340(68) .022 .02-.26 .33

SN

ACC .587(.406) .347(.341) 4.647(68) <.000*** .14-.34 .64

lAI .776(.291) .639(.277) 3.540(68) .001*** .06-.1 .48

rAI .789(.283) .648(.273) 3.444(68) .001*** .06-.22 .51 Note. ** = p<.01, *** = p<.001, d=Cohen's D, CI = Confidence Interval

47

Table 2-6. MCI versus Non-MCI in Surgery Group

MCI (n=13)

Mean Correlation (SD)

Pre Post t(df) P CI d

DMN .286(.136) .165(.100) 2.902(12) 0.013* .03-.21 1.017

CEN .310(.145) .234(.133) 2.155(12) 0.052 -.00-.15 0.550

SN .384(.147) .249(.110) 3.871(12) .002** .06-.21 1.037

VN .263(.094) .196(.083) 1.924(12) 0.078 -.01-.14 0.751

Non-MCI (n=56)

Mean Correlation (SD)

Pre Post t(df) P CI DMN .263(.095) .195(.118) 4.358(55) <.001** .04-.10 0.629

CEN .272(.121) .240(.125) 1.869(55) 0.067 -0-0.07 0.261

SN .353(.153) .278(.140) 3.227(55) .002** .03-.12 0.510

VN .254(.124) .249(.094) .309(55) 0.758 -.03-.04 0.045 Note. * = p<.05, ** = p<.01, *** = p<.001, d=Cohen's D, CI = Confidence Interval

48

Table 2-7. MCI versus Non-MCI in Non-Surgery Group

MCI (n=10)

Mean Correlation (SD)

Pre Post t(df) p CI

DMN .217(.095) .241(.114) -.579(9) 0.467 -.10-.05

CEN .267(.111) .287(.099) -.537(9) 0.604 -.10-.06

SN .363(.127) .346(.136) .599(9) 0.59 -.05-.09

VN .369(.143) .328(.129) .702(9) 0.501 -.09-.17

Non-MCI (n=55)

Mean Correlation (SD)

Pre Post t(df) p CI

DMN .271(.110) .285(.083) -.904(54) 0.37 -.04-.02

CEN .275(.124) .292(.109) -.956(54) 0.343 -.05-.02

SN .394(.133) .417(.133) -1.155(54) 0.253 -.06-.02

VN .278(.102) .277(.111) .101(54) 0.92 -.02-.03 Note. * = p<.05, ** = p<.01, *** = p<.001, CI = Confidence Interval

49

Figure 2-1. Schematic design of parallel surgery and non-surgery participant timelines. TKA = total knee arthroplasty.

50

A

51

B

Figure 2-2. TKA surgery group mean edge functional connectivity changes from pre to post surgery time points. A. Line thickness between nodes is weighted by node-to-node correlation. Lowercase “r” and “l” denote right and left brain hemispheres, respectively. B. The tables of edges for DMN, CEN and SN.

52

Figure 2-3. The connectivity of pre and post-surgery in four resting state network networks. A. The connectivity change in DMN. B. The connectivity change in CEN. C. The connectivity change in SN. D. The connectivity change in VN. Note: * p<0.05. Control=Non-Surgery.

Figure 2-4. The comparison between MCI and non-MCI surgery groups for connectivity of pre and post-surgery in four resting state network networks. A. The connectivity change in DMN. B. The connectivity change in CEN. C. The connectivity change in SN. D. The connectivity change in VN. Note: * p<0.05

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Figure 2-5. The comparison between MCI and non-MCI non-surgery groups for connectivity of pre and post-surgery in four resting state network networks. A. The connectivity change in DMN. B. The connectivity change in CEN. C. The connectivity change in SN. D. The connectivity change in VN. Note: * p<0.05

Figure 2-6. Changes in node strength of functional connectivity pre and post-surgery in four resting state network networks. A. The node strength change in DMN. B. The node strength change in CEN. C. The node strength change in SN. D. The node strength change in VN.

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Figure 2-7. Node strength changes in different groups. A. The node strength change in DMN. B. The node strength change in CEN. C. The node strength change in SN. D. The node strength change in VN. Note: * p<0.05.

Figure 2-8. Nod strength changes in MCI and non-MCI. A. The node strength change in DMN. B. The node strength change in CEN. C. The node strength change in SN. D. The node strength change in VN. Note: * p<0.05

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CHAPTER 3 CHANGES IN INTER-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS

FOLLOWING SURGERY

3.1 Introduction

In Chapter 2 we analyzed the interactions between different nodes within resting

state networks. The interactions between networks in a complex system is another

important property in view of the graph theory. The resting state networks of human

brain include several important networks which can be separated by the Group ICA

(Independent Component Analysis) and these networks can interact with each other

during the resting state as well as the task related activities. Many factors can affect the

interactions between these networks including drug addiction, morphine, pain, diseases,

etc. (Chen et al., 2016; Iadipaolo et al., 2018; Manoliu et al., 2014; Marusak et al.,

2018). Major surgeries such as total knee arthroplasty (TKA) may induce the post-

operative cognitive dysfunction (POCD) caused by surgery itself or anesthesia. The

functional connectivity of the network in surgery group can be impaired by the surgery,

anesthesia, or other risk factors. The latter may provide a neural basis for the former.

The role of inter-network interactions has not been studied.

The three important networks of DMN, CEN and SN as the major resting state

networks have been examined for the declines in intra-network connectivity within 48

hours after surgery compared to pre-surgery. To examine the changes of the interaction

between networks, in this chapter we extended our study to the inter-network

connection to evaluate the changes in connectivity after surgery between each pair of

individual networks among DMN, CEN, and SN at the level of whole network and at the

level of pairs of ROIs.

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Past work has shown that SN is in charge of coordinating DMN and CEN to

perform tasks by dynamically switching between the two networks (Goulden et al.,

2014). During the resting state, DMN is activated. During tasks, however, the SN is

activated and the DMN is deactivated. The SN and DMN are anti-correlated to perform

different tasks accordingly. These dynamic changes can be interrupted by external

interferences such as major surgery or anesthesia. Among the inter-network

interactions, the interaction between DMN and SN is more important during the task and

resting state. Bonnelle found that the Integrity of the SN can predict the behavior of

DMN after traumatic brain injury (Bonnelle et al., 2012). Jilka also examined the

interactions between SN and DMN by testing both the motor switching ability and SST

(Jilka et al., 2014). The disrupted interactions between SN and DMN can also be found

in patients with cocaine addiction (Liang et al., 2015). In their study, the alterations of

the decreased connections between bilateral insula and DMN were observed as well as

reduced connections between posterior cingulate and CEN; connection strength

between rostral anterior cingulate and SN and MTL (medial temporal lobe) was also

reduced in addiction patients.

To study the properties of the inter-network connectivity among the DMN, CEN,

and SN before and after surgery, we first examined the inter-network connectivity

between each pair of three networks to see the changes at the level of whole network.

Second, we looked into the interactions for each pair of the ROIs between two networks

to find which ROI plays the most important role in the inter-network relationship that can

maintain the stability of the network, and which ROI is more susceptible to the insult of

the major surgery or the anesthesia. Third, the node strength for each network was

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examined to evaluate the changes of each ROI bearing the connectivity with the rest of

the other ROIs between two networks. Last, the correlation between the connectivity of

the inter-network and connectivity of the intra-network was calculated to provide the

prediction of the changes of network properties before and after surgery for surgery

patients. MCI patients and non-MCI patients in surgery group and non-surgery groups

were also examined to provide the evaluation of the side effects of the surgery trauma

for groups of different cognitive status.

3.2 Methods

3.2.1 FMRI Regions of Interest Selection

The inter-network analysis included three resting state networks (RSNs): default

mode network (DMN), central executive network (CEN), and salience network (SN);

standard coordinates defined by Power and colleagues (Power et al., 2011) and Yeo

and colleagues (Thomas Yeo et al., 2011) were used for these networks. The regions of

interest (ROIs) for the three RSNs were defined as following. DMN consists of six ROIs:

medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), bilateral angular

gyrus (AG), and bilateral temporal (LT); CEN includes bilateral dorsolateral prefrontal

cortex (DLPFC) and bilateral inferior parietal lobule (IPL); and SN consists of dorsal

anterior cingulate cortex (ACC) and bilateral anterior insula (IN). The ROIs representing

each brain region were defined using a 5mm sphere in radius centered at the

coordinates of that region and BOLD signals were extracted from each ROI for further

analysis. Table 3-1 listed coordinates of these regions.

3.2.2 Functional Connectivity Analysis

The inter-network functional connectivity between each pair of ROIs was

quantified using the Pearson cross correlation between each pair of BOLD signals. Age,

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gender, pain, MED, and education were regressed out from the functional connectivity

of each pair of ROIs between networks.

1. At the network level, the BOLD signals were averaged across all pairs of ROIs

in each network. Then the mean functional connectivity between each pair of RSNs was

calculated by calculating the cross correlation between the mean BOLD signals

representing each individual network for each subject. Both preoperative and

postoperative resting state functional connectivity of each subject were calculated to

evaluate the connectivity changes related to surgery.

2. At the node level, the functional connectivity of each pair of nodes between

each pair of networks was calculated to compare the difference between pre-surgery

and post-surgery.

3. The node strength of each ROI which is the sum of the functional connectivity

between this ROI and the rest of other ROIs in each pair of networks was also

calculated at node level to evaluate the importance of the ROI in the communication

between each pair of networks.

4. MCI and non-MCI surgery groups were also analyzed at node level and at the

network level to examine the significant effects of the surgery on cognitive subtypes.

5. The relation between the intra-network connectivity studied in Chapter 2 and

the inter-network connectivity studied in this chapter were examined.

The Pearson correlation coefficient is defined as follows:

𝜌𝑋,𝑌 =𝐸[(𝑋−𝜇𝑋)(𝑌−𝜇𝑌)]

𝜎𝑋𝜎𝑌 (3-1)

Where E is the expectation, µX is the mean of X, µY is the mean of Y, σX is the standard

deviation of X, σY is the standard deviation of Y.

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The inter-network connectivity Cs,t is defined as follows:

𝐶𝑠,𝑡 =∑ 𝜀𝑖,𝑗𝑖∊𝑠,𝑗∈𝑡

𝑁𝑠×𝑁𝑡 (3-2)

Where Ns is the number of nodes within module s, whereas Nt is the number of nodes

within module t, and εi,j is the existing edge between module s and module t. Here,

Ns=Nt=i=j=1 for the inter-network at the network level.

The node strength Si is defined as follows:

𝑆𝑖 = ∑ 𝑤𝑖𝑗𝑁𝑗=1 (3-3)

Where N is the number of nodes, and wij is the weighted connectivity, i∊ s, j∊ t.

3.3 Results

3.3.1 Inter-network Connectivity

The inter-network correlation was calculated for different pairwise combinations

of the three networks: 1) DMN and SN; 2) DMN and CEN; and 3) CEN and SN, as

shown in Figure 3-1. The inter-network correlation between DMN and SN was shown in

Figure 3-2. The comparisons between pre-surgery and post-surgery, surgery group and

non-surgery group were plotted in Figure 3-2 A. At network level, by comparing mean

values of Pearson correlation of pre-surgery and post-surgery data in surgery group, the

post-surgery group had significant decline (absolute value) in functional connectivity

compared to the pre-surgery group (p<0.05); see Figure 3-2 B. The non-surgery group,

as expected, did not show significant changes between pre-pseudo surgery and post-

pseudo surgery data (p>0.05). At the nodal level, the correlation matrix in Figure 3-2 C

displayed each pair of ROIs between DMN and SN in pre-surgery group, post-surgery

group, and the difference between pre and post-surgery groups, with statistical

significance indicated (p<0.05). In the surgery group, several pairs of ROIs had

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significant declines in functional connectivity (p<0.05), such as PCC-dACC, mPFC-

dACC, etc. In the non-surgery group, there was no any significant changes observed in

any pairs of ROIs. The Cohen’s D (absolute values) in surgery group is also larger than

in non-surgery group. Statistic results of all pairs of ROIs were listed in Table 3-2 for

surgery group including functional connectivity of pre-surgery, functional connectivity of

post-surgery, p values of paired t test, FDR corrected p values, and Cohen’s D values.

Inter-network Pearson correlation coefficients of DMN and SN in surgery group also

included MCI and non-MCI subtypes. The MCI group showed more decline in functional

connectivity compared to non-MCI group in surgery group after patients received

surgery. The pairs of ROIs which showed significant drop in MCI group were also

different from the pairs in non-MCI group; the small number of samples is a limitation.

No significant changes were observed in MCI and non-MCI subtypes in non-surgery

group. The detailed information about MCI and non-MCI subtypes in surgery group was

listed in Figure 3-5 A and Table 3-2.

The inter-network correlation between DMN and CEN was shown in Figure 3-3.

The comparison between pre-surgery and post-surgery for surgery group and for non-

surgery group was plotted in Figure 3-3 A. At network level, by comparing mean values

of Pearson correlation of pre-surgery and post-surgery in surgery group, post-surgery

group had significant increase in functional connectivity compared to the pre-surgery

group which is indicated in Figure 3-3 B (p<0.05). Non-surgery group, however, did not

show significant changes between pre-pseudo surgery and post-pseudo surgery

(p>0.05). At nodal level, the correlation matrix in Figure 3-3 C displayed each pair of

ROIs between DMN and CEN in pre-surgery group, post-surgery group, and the

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difference between them, with statistical significance indicated (p<0.05). Several pairs of

ROIs had significant increase (p<0.05, FDR corrected) in surgery group, such as PCC-

lIPL and mPFC-rdLPFC. In non-surgery group, there was no any significant changes

observed in any pairs of ROIs. The Cohen’s D (absolute values) in surgery group was

also bigger than in non-surgery group. The statistic results of all pairs of ROIs were

listed in Table 3-3 for surgery group including functional connectivity of pre-surgery,

functional connectivity of post-surgery, p values of paired t test, FDR corrected p values,

and Cohen’s D values. Inter-network Pearson correlation coefficients of DMN and CEN

in surgery group also included MCI and non-MCI subtypes. Different from correlation

between DMN and SN, the MCI group in inter-network of DMN and CEN showed no

significant changes in functional connectivity compared to non-MCI group in surgery

group. The pairs of ROIs which showed significant changes (increase and decrease in

absolute values) in non-MCI group did not show significant changes in MCI group; again

small sample size may play a role in this. No significant changes were observed in MCI

and non-MCI subtypes in non-surgery group. The detailed information about MCI and

non-MCI subtypes in surgery group was listed in Figure 3-5 B and Table 3-3.

The inter-network correlation between CEN and SN was shown in Figure 3-4.

The comparison between pre-surgery and post-surgery in surgery group and in non-

surgery group were plotted in Figure 3-4 A. At network level, by comparing mean

values of Pearson correlation of pre surgery and post-surgery in surgery group, post-

surgery group had increase in functional connectivity compared to the pre-surgery

group but it’s not significant, which is indicated in Figure 3-4 B (p>0.05). Non-surgery

group, however, did not show changes between pre-pseudo surgery and post-pseudo

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surgery (p>0.05). At nodal level, the correlation matrix in Figure 3-4 C displayed each

pair of ROIs between DMN and CEN in pre-surgery group, post-surgery group, and the

difference between pre and post-surgery. No pairs of ROIs had significant changes

(p<0.05) in surgery group. In non-surgery group, there is no any significant changes

observed in any pairs of ROIs. The Cohen’s D (absolute values) in surgery group has

no obvious difference compared to non-surgery group. The statistic results of all pairs of

ROIs were listed in Table 3-4 for surgery group including functional connectivity of pre

surgery, functional connectivity of post-surgery, p values of paired t test, FDR corrected

p values, and Cohen’s D values. Inter-network Pearson correlation coefficients of CEN

and SN in surgery group was also analyzed for MCI and non-MCI subtypes. Different

from correlation between DMN and SN, the MCI group in inter-network of CEN and SN

showed no significant changes in functional connectivity compared to non-MCI group in

surgery group. The pairs of ROIs which showed no significant changes (increase and

decrease in absolute values) in non-MCI group did not show significance in MCI group.

No significant changes were observed in MCI and non-MCI subtypes in non-surgery

group. The detailed information about MCI and non-MCI subtypes in surgery group was

listed in Figure 3-5 C and Table 3-4.

3.3.2 Node Strength in Inter-network Connectivity

The previous analysis of functional connectivity between pairs of ROIs was about

the change of edge weight according to the network analysis. For each ROI, the node

strength can be used to evaluate the importance of the ROI in each individual network

or pairs of networks. In the inter-network analysis, the node strength of one ROI can be

calculated by summing all functional connectivity between this ROI and all the ROIs in

the other network. Here we carried out such an analysis for DMN and SN in which the

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node strength for each ROI in DMN and SN was calculated before and after surgery

and their values were compared. After surgery, the node strength in DMN showed

significant decline compared to pre-surgery, as shown in Figure 3-6 A. The non-surgery

group did not show significant changes. The statistical analysis was conducted to test

the significance for each ROI in DMN. All ROIs except rLT showed significant decline

after surgery (p<0.05) in Figure 3-6 B, and no significance was observed in non-surgery

group. The same analysis was done in SN too. After surgery, the node strength in SN

also showed significant decline compared to pre-surgery, as shown in Figure 3-7 A. The

non-surgery group did not show significant changes. The statistical analysis was

conducted to test the significance for each ROI in SN. All three ROIs in SN showed

significant decline after surgery (p<0.05) in Figure 3-7 B, and no significant changes

were observed in non-surgery group. The statistic results of all pairs of ROIs were listed

in Table 3-5 for surgery group including node strength of pre-surgery, node strength of

post-surgery, p values of paired t test, FDR corrected p values, and Cohen’s D values.

Inter-network node strength of DMN and SN also included MCI and non-MCI subtypes.

The MCI group showed more decline in node strength compared to non-MCI group in

surgery group. The ROIs which showed significant drop in MCI group are the same as

the ROIs in non-MCI group except rAG in DMN and dACC in SN. No significant

changes were observed in MCI and non-MCI subtypes in non-surgery group. The

detailed information about MCI and non-MCI subtypes in surgery group was listed in

Table 3-5.

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3.3.3 Correlation between Changes in Intra-network Connectivity and in Inter-network Connectivity

The results in Chapter 2 provided us information about the changes of the intra-

network connectivity in each network; the changes of the inter-network connectivity

were analyzed in this chapter. Their relationship is considered next. We found the

following results. First, pre to post DMN-SN connectivity change and SN connectivity

change had a positive correlation for the whole group, MCI subtypes and non-MCI

subtypes in non-surgery group, as shown in Figure 3-9. It means more drop in SN intra-

network connectivity, more anti-correlation inter-network between DMN and SN. In

surgery group, no such significant correlation was found in these groups. Second, pre to

post DMN-SN connectivity change and DMN connectivity change had a negative

correlation for the whole group, MCI group and non-MCI group in both surgery group

and non-surgery group, except the MCI non-surgery group. It means that more drop in

DMN intra-network connectivity, less anti-correlation between DMN and SN, as shown

in Figure 3-10 A. This correlation is especially significant in MCI surgery group. It

indicated that DMN intra-network connectivity decrease might be related to lost

interaction between DMN and SN. Third, in the relation between pre to post DMN-SN

connectivity change and DMN pre-surgery, the negative correlation had significance for

the whole group, the MCI group and the non-MCI group in surgery group and non-

surgery group, except the MCI non-surgery group. This correlation is also especially

significant in the MCI surgery group with no significance in MCI non-surgery group. It

means more intra-network connectivity in DMN before surgery, more decline in anti-

correlation between DMN and SN after surgery, as shown in Figure 3-10 B. In other

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words, the DMN-SN interaction is more susceptible to anesthesia and/or surgery when

DMN connectivity is high especially in the MCI group pre surgery.

3.4 Discussion

The main finding of this chapter is that inter-network functional connectivity

showed acute changes within 48 hours following the TKA surgery. Significant decline

was observed in inter-network connectivity of DMN-SN. Significant increase was seen in

inter-network connectivity of DMN-CEN. No significant changes were found in inter-

network connectivity of CEN-SN. This indicated that the surgery-related changes in

inter-network functional connectivity were selective and the effects may be opposite at

network level. At the node level, the changes were not evenly distributed among all

pairs of the inter-network connectivity. These results also indicated that the effects of

the surgery were specific to certain connectivity and the patterns of changes were

different from network to network. In DMN-SN, the mean value of the anti-correlation

became less negatively in post-surgery group and this trend was also shown in each

pair of the connectivity. In DMN-CEN, the mean post-surgery correlation increased to a

more positive value than the one in pre-surgery. However, this trend was not observed

in all pairs of the DMN-CEN connectivity. Some pairs of connectivity positively

correlated and others anti-correlated. The connectivity in CEN-SN did not show

significant changes in mean values of all pairs of ROIs, and also no significant changes

were found in any pair of the ROIs.

Inter-network Changes in DMN-SN. The connectivity between DMN and SN

are important among all resting state network related to the cognitive functions. The

anti-correlated connectivity between these two networks are important to coordinate the

normal functions during cognitive tasks as well as during resting state. Although SN is

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more activated and in charge of coordinating different networks to perform tasks, the SN

cannot control the whole process without the facilitation of DMN. The anti-correlated

coupling between DMN and SN has been demonstrated in predicting the cognition

performance in healthy aging and Parkinson’s disease (Putcha et al., 2016). Low

performance is related to more positive DMN-SN coupling. In the stop-signal task

(SST), the efficient inhibition of inappropriate responses is required to perform well in

the task, which needs the rapid deactivation of DMN. If impairment of inhibitory control

occurs, the SST performance is affected significantly. This abnormality of DMN function

was predicted by the amount of the white matter damage in the SN fiber tracts

(Bonnelle et al., 2012). It has been proposed that the SST performance is captured by

the horse race model: a race between an excitatory and an inhibitory process. In terms

of the brain network, the efficient stopping is associated with activation within a right

lateral part of the SN and the deactivation within the DMN.

Besides inhibition, task switching also needs the coordination between DMN and

SN. The switching ability during motor tasks is weaker if FA (fractional anisotropy) is

lower in the connection between rAI and dACC, which leads to longer reaction time. The

coupling between the rAI and DMN is enhanced with increased cognitive control. The

impairments in SN especially rAI inhibit the dynamic network interactions (Jilka et al.,

2014). Interactions between DMN and SN can also be disrupted in patients with cocaine

addiction. At the module level, the intermodule connectivity decreased between DMN

and SN in cocaine dependent patients. At the node level, the bilateral insula has

decreased connections with DMN (Liang et al., 2015).

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In this chapter, we found that the anti-correlation became less negative after

surgery, which indicated that the surgery or anesthesia weakened the interactions

between DMN and SN. The decline in this inter-network connection may inhibit the

ability to response to tasks quickly and accurately, such as memory tasks. This impaired

symptom can be observed in major depressive disorders with DMN-SN changed to less

negative correlation (Manoliu et al., 2014). Considering the age of the surgery group,

this impaired connection can take longer time to recover from this dysfunction, which

could be a neural mechanism of POCD.

Inter-network Changes in SN-CEN and DMN-CEN. The SN is in charge of

dynamically switching between DMN and CEN (Goulden et al., 2014). The coupling

between SN and CEN or DMN and CEN in resting state network can be impaired in

many cases. The connectivity between SN and CEN is normally positive. In the hepatic

encephalopathy, the connections between aberrant SN and CEN are significantly

different among the healthy controls, patients without minimal hepatic encephalopathy,

and patients with minimal hepatic encephalopathy (Chen et al., 2016). The dynamic

connectivity of SN and CEN can be altered in mindfulness, which was observed in

children and adolescents (Marusak et al., 2018). This finding may suggest possible

interventions that may be applied before surgery to enhance the ability to resist the

detrimental effects of surgery.

While the connectivity between SN and DMN is negatively correlated, the CEN

and DMN does not necessarily hold negative correlations with each other, despite some

suggestions of the literature (Iadipaolo et al., 2018). The DMN-CEN (right side)

connection is more positively correlated in Parkinson’s disease compared to healthy

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control (Putchaa et al., 2015).The aberrant inter-network connectivity can be found in

major depressive disorders. The insular dysfunction within the salience network is

associated with severe symptoms and the DMN-CEN connection shifted from positive

value to negative value or from negative value to less negative value in major

depression disorders (Manoliu et al., 2014). This chapter reported increased positive

connectivity between DMN-CEN after surgery in older adults; this could reflect a

compensatory response. This interaction between DMN and CEN may also be impaired

by the dysfunction of activation in DMN or modulation of SN, as discussed below.

Relationship between intra-network connectivity and inter-network

connectivity. The connectivity within the network and the connection between networks

are not completely independent. Each ROI has two roles in the network: it needs to

coordinate the interactions between networks as well as it holds the interactions inside

the network. The impairment of the network itself may also affect the interactions

between networks. SN has a causal influence on activity within DMN in cognitive tasks

(Chiong et al., 2013; Jilka et al., 2014; Sridharan et al., 2008). This indicates that the SN

is able to impact the modulation of DMN activity through inter-network interactions. In

Parkinson’s disease, SN dysfunction due to striatal disruptions may alter the

interactions between DMN and SN (Putcha et al., 2016). In this chapter, it was found

that the significant correlation between the changes of SN (pre-post surgery) and the

changes of DMN-SN (pre-post surgery) in non-surgery group was compromised in

surgery group. It indicated that the role of SN in interaction between DMN and SN could

be significantly reduced by surgery and/or anesthesia with the weakened internal

connectivity in SN, which leads to less interactions.

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To summarize, this study concerned the interactions between networks and how

surgery affected such interactions. The anti-correlation between DMN and SN is

important in coordinating the resting state functions and task processing. The declined

anti-correlation can significantly reduce the brain’s ability to carry out such coordination,

thus leading to impaired task performance, especially when activation of dACC in SN

and deactivation of PCC in DMN are required simultaneously. This impairment can be

further exaggerated in older adults with MCI. The dysfunction and the functional

changes beyond the 48 hours after surgery may need to be investigated in future

studies to evaluate the longer term impact on brain connectivity change and related

cognitive impairment.

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Table 3-1. The MNI Coordinates of the Regions of Interest (ROI)

NETWORK ROI MNI

BA X Y Z

DMN PCC 1 -51 29 23

mPFC -1 61 22 10 lAG -48 -66 34 39

lLT -65 -23 -9 21

rAG 53 -61 35 39

rLT 61 -21 -12 21

SN ACC -1 10 46 6 lIN -38 14 5 13

rIN 37 18 5 13

CEN lDLPFC -44 27 33 9

lIPL -53 -50 39 39

rDLPFC 46 28 31 9

rIPL 54 -44 43 40

71

Table 3-2. Inter-network Pearson correlation coefficients of DMN and SN in surgery group including MCI and non-MCI subtypes

Patient Group (n=69)

Node pair Pre Post p FDR

corrected p

Cohen's D

PCC-dACC -0.2406 -0.1332 0.0007 0.0017 ** -0.5310

PCC-lIN -0.2417 -0.0465 1.1E-07 1.46E-06 *** -0.9499

PCC-rIN -0.3055 -0.1108 1.6E-07 1.46E-06 *** -0.9748

rAG-dACC -0.1690 -0.1399 0.3410 0.3610 -0.1492

rAG-lIN -0.0886 -0.0029 0.0046 0.0104 * -0.4295

rAG-rIN -0.1366 -0.0257 0.0005 0.0015 ** -0.5526

lAG-dACC -0.2040 -0.1597 0.2289 0.2575 -0.2277

lAG-lIN -0.1404 -0.0223 0.0004 0.0015 ** -0.5494

lAG-rIN -0.2422 -0.1035 0.0001 0.0004 *** -0.6855

mPFC-dACC -0.1206 0.0160 0.0001 0.0003 *** -0.5695

mPFC-lIN -0.1226 -0.0851 0.2138 0.2568 -0.1931

mPFC-rIN -0.1934 -0.1059 0.0062 0.0123 * -0.4272

rLT-dACC -0.1916 -0.1357 0.0635 0.1040 -0.3017

rLT-lIN -0.0896 -0.0997 0.7032 0.7032 0.0581

rLT-rIN -0.1254 -0.0630 0.0725 0.1088 -0.3371

lLT-dACC -0.1398 -0.1063 0.2140 0.2568 -0.2050

lLT-lIN -0.1144 -0.0595 0.0605 0.1040 -0.3069

lLT-rIN -0.1271 -0.0781 0.0940 0.1302 -0.2812 Note: *p<0.05, **p<0.01, ***p<0.001

72

Table 3-2. Continued

MCI Group (n=13)

Node pair Pre Post p FDR

corrected p

Cohen's D

PCC-dACC -0.2896 -0.1985 0.1842 0.3316 -0.3740

PCC-lIN -0.2390 -0.0850 0.0022 0.0097 ** -0.9255

PCC-rIN -0.3793 -0.1400 0.0002 0.0033 ** -1.5817

rAG-dACC -0.2481 -0.1781 0.4025 0.4385 -0.2800

rAG-lIN -0.1517 -0.0624 0.2909 0.3741 -0.4617

rAG-rIN -0.2166 -0.0367 0.0357 0.0919 -0.8470

lAG-dACC -0.2746 -0.1457 0.3163 0.3796 -0.4744

lAG-lIN -0.1994 0.0122 0.0143 0.0430 * -0.9319

lAG-rIN -0.3731 -0.0666 0.0007 0.0039 ** -1.3995

mPFC-dACC -0.1588 0.0629 0.0042 0.0150 * -0.8314

mPFC-lIN -0.1652 -0.1144 0.4385 0.4385 -0.2575

mPFC-rIN -0.3221 -0.1281 0.0004 0.0039 ** -1.4093

rLT-dACC -0.2527 -0.1157 0.1146 0.2292 -0.6160

rLT-lIN -0.1205 -0.0801 0.4233 0.4385 -0.2528

rLT-rIN -0.1920 -0.0142 0.0425 0.0956 -1.0371

lLT-dACC -0.1591 -0.0932 0.2201 0.3376 -0.4732

lLT-lIN -0.0776 -0.0483 0.2656 0.3677 -0.1896

lLT-rIN -0.1239 -0.0522 0.2250 0.3376 -0.4349

73

Table 3-2. Continued

Non-MCI Group (n=56)

Node pair Pre Post p FDR corrected p

Cohen's D

PCC-dACC -0.2293 -0.1180 0.0019 0.0087 ** -0.5805

PCC-lIN -0.2423 -0.0375 3.1E-06 5.65E-05 *** -0.9552

PCC-rIN -0.2883 -0.1041 2.2E-05 0.0002 *** -0.8847

rAG-dACC -0.1507 -0.1311 0.5501 0.5501 -0.1092

rAG-lIN -0.0739 0.0109 0.0087 0.0195 * -0.4244

rAG-rIN -0.1180 -0.0231 0.0055 0.0195 * -0.4804

lAG-dACC -0.1876 -0.1630 0.4835 0.5120 -0.1424

lAG-lIN -0.1267 -0.0303 0.0082 0.0195 * -0.4533

lAG-rIN -0.2118 -0.1121 0.0073 0.0195 * -0.5154

mPFC-dACC -0.1117 0.0051 0.0019 0.0087 ** -0.4978

mPFC-lIN -0.1127 -0.0782 0.3158 0.4373 -0.1771

mPFC-rIN -0.1636 -0.1008 0.0892 0.1606 -0.2951

rLT-dACC -0.1774 -0.1404 0.2404 0.3607 -0.2103

rLT-lIN -0.0825 -0.1043 0.4759 0.5120 0.1230

rLT-rIN -0.1099 -0.0744 0.3465 0.4455 -0.1902

lLT-dACC -0.1354 -0.1094 0.4028 0.4833 -0.1533

lLT-lIN -0.1230 -0.0621 0.0878 0.1606 -0.3293

lLT-rIN -0.1279 -0.0842 0.1939 0.3173 -0.2464

74

Table 3-3. Inter-network Pearson correlation coefficients of DMN and CEN in surgery group including MCI and non-MCI subtypes

Patient Group (n=69)

Node pair Pre Post p FDR corrected p

Cohen's D

PCC-ldLPFC -0.0390 -0.0290 0.6964 0.8452 -0.0542

PCC-lIPL 0.0391 0.1386 0.0006 0.0076 ** -0.4335

PCC-rdLPFC -0.0522 -0.0669 0.6334 0.8452 0.0677

PCC-rIPL -0.1250 -0.0269 0.0006 0.0076 ** -0.4535

rAG-ldLPFC 0.0593 0.0514 0.7889 0.8452 0.0391

rAG-lIPL 0.3130 0.3344 0.4409 0.8140 -0.1051

rAG-rdLPFC 0.1611 0.1073 0.0757 0.2020 0.2536

rAG-rIPL 0.2534 0.2461 0.7988 0.8452 0.0345

lAG-ldLPFC 0.0800 0.0712 0.7513 0.8452 0.0374

lAG-lIPL 0.2724 0.3595 0.0017 0.0138 * -0.3954

lAG-rdLPFC 0.0024 0.0099 0.8100 0.8452 -0.0371

lAG-rIPL 0.0073 0.0191 0.7014 0.8452 -0.0517

mPFC-ldLPFC

-0.0319 0.0507 0.0184 0.0632 -0.3981

mPFC-lIPL 0.0304 -0.0473 0.0084 0.0338 * 0.3860

mPFC-rdLPFC

-0.0290 0.0692 0.0045 0.0216 * -0.4033

mPFC-rIPL -0.1286 -0.0543 0.0273 0.0818 -0.3574

rLT-ldLPFC -0.0866 -0.0084 0.0035 0.0210 * -0.4320

rLT-lIPL 0.1026 0.0733 0.3478 0.7989 0.1489

rLT-rdLPFC -0.0007 0.0165 0.5766 0.8452 -0.0902

rLT-rIPL 0.0379 0.0360 0.9466 0.9466 0.0091

lLT-ldLPFC 0.0000 -0.0179 0.5132 0.8452 0.0957

lLT-lIPL 0.0591 0.0365 0.3902 0.7989 0.1066

lLT-rdLPFC -0.0010 -0.0114 0.7174 0.8452 0.0555

lLT-rIPL -0.0527 -0.0300 0.3994 0.7989 -0.1177 Note: *=p<0.05, **=p<0.01, ***=p<0.001

75

Table 3-3. Continued

MCI Group (n=13)

Node pair Pre Post p FDR corrected p

Cohen's D

PCC-ldLPFC 0.0231 -0.0627 0.1767 0.4711 0.4011

PCC-lIPL -0.0600 0.0856 0.0345 0.2953 -0.6151

PCC-rdLPFC 0.0174 -0.1134 0.0615 0.2953 0.5198

PCC-rIPL -0.1265 -0.0205 0.1099 0.3298 -0.4680

rAG-ldLPFC 0.0886 -0.0173 0.0774 0.3018 0.4665

rAG-lIPL 0.2406 0.3087 0.3883 0.6557 -0.3143

rAG-rdLPFC 0.1686 0.0896 0.0880 0.3018 0.3107

rAG-rIPL 0.2059 0.1834 0.6845 0.8646 0.1112

lAG-ldLPFC 0.0819 0.0696 0.8392 0.8757 0.0409

lAG-lIPL 0.1571 0.3444 0.0520 0.2953 -0.7077

lAG-rdLPFC 0.0146 0.0309 0.8127 0.8757 -0.0620

lAG-rIPL 0.0184 -0.0272 0.5867 0.7822 0.1767

mPFC-ldLPFC 0.0101 0.1149 0.2937 0.6408 -0.4408

mPFC-lIPL -0.0693 -0.0632 0.9196 0.9196 -0.0328

mPFC-rdLPFC -0.0198 0.1521 0.0484 0.2953 -0.7648

mPFC-rIPL -0.1347 -0.0718 0.4098 0.6557 -0.2689

rLT-ldLPFC -0.0711 -0.0553 0.7889 0.8757 -0.0874

rLT-lIPL 0.0417 0.1075 0.3978 0.6557 -0.3244

rLT-rdLPFC 0.0566 -0.0377 0.0469 0.2953 0.5801

rLT-rIPL 0.0241 0.0415 0.7421 0.8757 -0.0782

lLT-ldLPFC 0.0622 0.0017 0.2257 0.5416 0.3828

lLT-lIPL 0.0077 0.0551 0.5264 0.7432 -0.2039

lLT-rdLPFC 0.0171 -0.0378 0.3811 0.6557 0.3328

lLT-rIPL -0.0562 -0.0083 0.5113 0.7432 -0.2052

76

Table 3-3. Continued

Non-MCI Group (n=56)

Node pair Pre Post p FDR corrected p

Cohen's D

PCC-ldLPFC -0.0534 -0.0212 0.2488 0.4594 -0.1819

PCC-lIPL 0.0621 0.1509 0.0057 0.0341 * -0.3930

PCC-rdLPFC -0.0684 -0.0562 0.7221 0.9501 -0.0584

PCC-rIPL -0.1247 -0.0283 0.0028 0.0308 * -0.4457

rAG-ldLPFC 0.0526 0.0673 0.6634 0.9501 -0.0743

rAG-lIPL 0.3299 0.3404 0.7198 0.9501 -0.0526

rAG-rdLPFC 0.1593 0.1114 0.1823 0.3978 0.2354

rAG-rIPL 0.2644 0.2607 0.9105 0.9501 0.0175

lAG-ldLPFC 0.0795 0.0715 0.8002 0.9501 0.0362

lAG-lIPL 0.2991 0.3630 0.0154 0.0739 -0.3088

lAG-rdLPFC -0.0004 0.0051 0.8771 0.9501 -0.0290

lAG-rIPL 0.0047 0.0298 0.4470 0.7664 -0.1128

mPFC-ldLPFC -0.0416 0.0359 0.0374 0.1282 -0.3870

mPFC-lIPL 0.0536 -0.0437 0.0039 0.0308 * 0.4794

mPFC-rdLPFC -0.0311 0.0500 0.0323 0.1282 -0.3276

mPFC-rIPL -0.1272 -0.0502 0.0428 0.1284 -0.3779

rLT-ldLPFC -0.0902 0.0024 0.0021 0.0308 * -0.5103

rLT-lIPL 0.1168 0.0653 0.1328 0.3541 0.2627

rLT-rdLPFC -0.0140 0.0290 0.2323 0.4594 -0.2204

rLT-rIPL 0.0411 0.0348 0.8426 0.9501 0.0317

lLT-ldLPFC -0.0144 -0.0225 0.8007 0.9501 0.0417

lLT-lIPL 0.0711 0.0322 0.1604 0.3849 0.1869

lLT-rdLPFC -0.0052 -0.0053 0.9982 0.9982 0.0004

lLT-rIPL -0.0520 -0.0350 0.5617 0.8987 -0.0914

77

Table 3-4. Inter-network Pearson correlation coefficients of CEN and SN in surgery group including MCI and non-MCI subtypes

Patient Group (n=69)

Node pair Pre Post p FDR corrected p

Cohen's D

ldLPFC-dACC 0.1403 0.2024 0.0806 0.4253 -0.2803

ldLPFC-lIN 0.0657 0.0421 0.4272 0.8059 0.1274

ldLPFC-rIN 0.0214 -0.0048 0.3476 0.8059 0.1435

lIPL-dACC 0.0818 0.0470 0.2534 0.7601 0.1818

lIPL-lIN 0.1753 0.1880 0.6365 0.8714 -0.0747

lIPL-rIN 0.0888 0.0919 0.9138 0.9138 -0.0171

rdLPFC-dACC 0.1032 0.1627 0.0627 0.4253 -0.2469

rdLPFC-lIN -0.0151 -0.0016 0.6535 0.8714 -0.0738

rdLPFC-rIN 0.0635 0.0705 0.8054 0.8868 -0.0371

rIPL-dACC 0.1170 0.1233 0.8129 0.8868 -0.0324

rIPL-lIN 0.0975 0.1414 0.1063 0.4253 -0.2418

rIPL-rIN 0.1684 0.1902 0.4701 0.8059 -0.1087 Note: *=p<0.05, **=p<0.01, ***=p<0.001

78

Table 3-4. Continued

MCI Group (n=13)

Node pair Pre Post p FDR corrected p

Cohen's D

ldLPFC-dACC 0.0753 0.2590 0.1091 0.5395 -0.6690

ldLPFC-lIN 0.0601 0.1065 0.3494 0.8387 -0.2565

ldLPFC-rIN 0.0534 0.0414 0.8630 0.9672 0.0590

lIPL-dACC 0.1320 0.0127 0.1349 0.5395 0.4964

lIPL-lIN 0.1954 0.1897 0.9294 0.9672 0.0237

lIPL-rIN 0.0951 0.1106 0.7969 0.9672 -0.0663

rdLPFC-dACC -0.0224 0.2262 0.0034 0.0409 * -0.9795

rdLPFC-lIN 0.0155 0.0120 0.9651 0.9672 0.0187

rdLPFC-rIN 0.0824 0.0639 0.8137 0.9672 0.1011

rIPL-dACC 0.1137 0.1661 0.3200 0.8387 -0.2118

rIPL-lIN 0.1072 0.1017 0.9270 0.9672 0.0269

rIPL-rIN 0.2038 0.2004 0.9672 0.9672 0.0157

79

Table 3-4. Continued

Non-MCI Group (n=56)

Node pair Pre Post p FDR corrected p

Cohen's D

ldLPFC-dACC 0.1554 0.1892 0.3364 0.8078 -0.1630

ldLPFC-lIN 0.0670 0.0272 0.2527 0.8078 0.2140

ldLPFC-rIN 0.0139 -0.0155 0.3379 0.8078 0.1659

lIPL-dACC 0.0702 0.0550 0.6445 0.8078 0.0847

lIPL-lIN 0.1707 0.1876 0.5714 0.8078 -0.1116

lIPL-rIN 0.0873 0.0876 0.9926 0.9926 -0.0018

rdLPFC-dACC 0.1324 0.1479 0.6378 0.8078 -0.0665

rdLPFC-lIN -0.0222 -0.0048 0.5938 0.8078 -0.0952

rdLPFC-rIN 0.0591 0.0721 0.6732 0.8078 -0.0673

rIPL-dACC 0.1178 0.1133 0.8843 0.9647 0.0245

rIPL-lIN 0.0953 0.1506 0.0716 0.8078 -0.3125

rIPL-rIN 0.1602 0.1878 0.3981 0.8078 -0.1387

80

Table 3-5. Node strength of DMN and SN of the inter-network in surgery group including MCI and non-MCI subtypes

Patient Group (n=69)

Node Strength of DMN

Node pair Pre Post p FDR corrected p

Cohen's D

PCC -0.7878 -0.2905 2.39E-08 1.43E-07 *** -1.0325

rAG -0.3942 -0.1685 0.0015 0.0023 ** -0.4654

lAG -0.5866 -0.2855 0.0004 0.0008 *** -0.6170

mPFC -0.4366 -0.1750 0.0002 0.0007 *** -0.5176

rLT -0.4066 -0.2985 0.1208 0.1208 -0.2609

lLT -0.3814 -0.2440 0.0202 0.0242 * -0.3814

Patient Group (n=69)

Node Strength of SN

Node pair Pre Post p FDR corrected p

Cohen's D

dACC -1.0657 -0.6589 0.0013 0.0013 ** -0.5310

lIN -0.7973 -0.3160 1.75E-05 2.63E-05 *** -0.6920

rIN -1.1302 -0.4871 4.14E-06 1.24E-05 *** -0.8466

Note: *=p<0.05, **=p<0.01, ***=p<0.001

81

Table 3-5. Continued

MCI Group (n=13)

Node Strength of DMN

Node pair Pre Post p FDR corrected p

Cohen's D

PCC -0.9079 -0.4235 0.0002 0.0006 *** -1.2855

rAG -0.6163 -0.2772 0.1013 0.1013 -0.6090

lAG -0.8470 -0.2000 0.0141 0.0282 * -1.1309

mPFC -0.6461 -0.1795 0.0002 0.0006 *** -1.1621

rLT -0.5652 -0.2100 0.0521 0.0782 -0.7819

lLT -0.3605 -0.1937 0.0770 0.0924 -0.4862

MCI Group (n=13)

Node Strength of SN

Node pair Pre Post p FDR corrected p

Cohen's D

dACC -1.3828 -0.6683 0.0741 0.0741 -0.7269

lIN -0.9533 -0.3779 0.0035 0.0053 ** -0.8908

rIN -1.6070 -0.4377 0.0004 0.0013 ** -1.6761

82

Table 3-5. Continued

Non-MCI Group (n=56)

Node Strength of DMN

Node pair Pre Post p FDR corrected p

Cohen's D

PCC -0.7600 -0.2596 2.42E-06 1.45E-05 *** -0.9982

rAG -0.3426 -0.1433 0.0077 0.0145 * -0.4307

lAG -0.5261 -0.3054 0.0097 0.0145 * -0.4795

mPFC -0.3880 -0.1739 0.0092 0.0145 * -0.4087

rLT -0.3698 -0.3190 0.4974 0.4974 -0.1259

lLT -0.3862 -0.2556 0.0621 0.0745 -0.3562

Non-MCI Group (n=56)

Node Strength of SN

Node pair Pre Post p FDR corrected p

Cohen's D

dACC -0.9921 -0.6567 0.0085 0.0085 ** -0.4742

lIN -0.7611 -0.3016 0.0005 0.0009 *** -0.6478

rIN -1.0196 -0.4986 0.0006 0.0009 *** -0.6878

83

Figure 3-1. Schematic diagram of the functional interactions between three resting state networks: DMN, CEN and SN.

84

Figure 3-2. The inter-network correlation between DMN and SN. A) Pre-surgery patient

group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Mean values of Pearson correlation of pre-surgery and post-surgery in surgery group; Mean values of Pearson correlation of pre-surgery and post-surgery in non-surgery group. C) Correlation matrix of each pair of ROIs between DMN and SN in pre and post-surgery group; Correlation matrix of each pair of ROIs between DMN and SN in pre and post non-surgery group. Note: *p<0.05

85

Figure 3-3. The inter-network correlation between DMN and CEN. A) Pre-surgery

patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Mean values of Pearson correlation of pre-surgery and post-surgery in surgery group; Mean values of Pearson correlation of pre-surgery and post-surgery in non-surgery group. C) Correlation matrix of each pair of ROIs between DMN and CEN in pre and post-surgery group; Correlation matrix of each pair of ROIs between DMN and CEN in pre and post non-surgery group. Note: *p<0.05

86

Figure 3-4. The inter-network correlation between CEN and SN. A) Pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Mean values of Pearson correlation of pre-surgery and post-surgery in surgery group; Mean values of Pearson correlation of pre-surgery and post-surgery in non-surgery group. C) Correlation matrix of each pair of ROIs between CEN and SN in pre and post-surgery group; Correlation matrix of each pair of ROIs between CEN and SN in pre and post non-surgery group. Note: *p<0.05

87

Figure 3-5. The comparison between MCI and non-MCI groups in inter-network correlation. A: The DMN-SN correlation of MCI and non-MCI in surgery and non-surgery groups. B: The DMN-CEN correlation of MCI and non-MCI in surgery and non-surgery groups. C: The CEN-SN correlation of MCI and non-MCI in surgery and non-surgery groups. Note: *=p<0.05

88

Figure 3-6. The node strength of DMN in inter-network correlation between DMN and SN. A) The changes of node strength of each ROI in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Node strength of Pearson correlation of pre-surgery and post-surgery in surgery group; pre-surgery and post-surgery in non-surgery group. Note: *p<0.05

89

Figure 3-7. The node strength of SN in inter-network correlation between DMN and SN. A) The changes of node strength of each ROI in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Node strength of Pearson correlation of pre-surgery and post-surgery in surgery group; pre-surgery and post-surgery in non-surgery group. Note: *p<0.05

90

Figure 3-8. The comparison of node strength between MCI and non-MCI in inter-network correlation of DMN-SN. A: The DMN node strength of MCI and non-MCI in surgery and non-surgery groups. B: The SN strength of MCI and non-MCI in surgery and non-surgery groups. Note: *p<0.05

91

Figure 3-9. The correlation between intra-network connectivity changes of SN pre-post surgery and inter-network connectivity pre-post surgery of DMN-SN in whole group, MCI, and non-MCI groups: MCI surgery patient group; non-MCI surgery patient group; MCI non-surgery control group; MCI non-surgery control group.

92

Figure 3-10. The correlation between intra-network connectivity and inter-network connectivity. A: DMN-SN pre-post surgery and DMN pre surgery. B: DMN-SN pre-post surgery and DMN pre surgery in whole group, MCI, and non-MCI group: MCI surgery patient group; non-MCI surgery patient group; MCI non-surgery control group; MCI non-surgery control group.

93

CHAPTER 4 CHANGES IN FUNCTIONAL BRAIN CONNECTOME FOLLOWING SURGERY

4.1 Introduction

Brain connectome analysis is the analysis in which the whole brain is treated as

one large network. Each individual brain area needs to coordinate with other brain areas

to maintain the normal functions for various tasks. The whole brain analysis can provide

us the view about the interactions or changes in interactions across the whole brain

functionally and structurally (Sha et al., 2017; Sporns, 2013; Xia & He, 2017; T. Xu et

al., 2016; Zhang et al., 2016). When the analysis comes to the whole brain, there is

increased complexity. For the purpose of this chapter, from the perspective of network

analysis, each brain area is treated as a node in a network. The whole brain can be

taken as a network consisting of hundreds of functional units with each being a brain

area with unique functions. Recent work has shown that graph theory can be used as

an effective tool to analyze the complex connections in the human brain (Achard et al.,

2006; Bullmore & Sporns, 2009; Lo et al., 2015).

In the human brain, the functional network is finely balanced to conduct

complicated and delicate functions. In order to look into the complex brain system as a

graph, several parameters were developed to evaluate the properties of the network,

including degree, clustering coefficient, global coefficient, modularity, small world,

robustness or resilience (Aerts et al., 2016; Bassett & Bullmore, 2006; Latora &

Marchiori, 2001; Watts & Strogatz, 1998; Rubinov & Sporns, 2010). How do these

parameters change after the brain undergoes a traumatic event? Much remains to be

learned.

94

Many diseases can induce changes in brain networks. Significant randomization

of the brain network was demonstrated after disease onset. Some have suggested that

such randomization might be protective. In addition, hubs of the network are more

resilient to the targeted attacks; this has been shown in patients with schizophrenia,

which is a disorder of brain organization and network dysfunction (Lo et al., 2015).

Because the hubs of the brain functional networks are important to form the main

backbone of the networks and process all the communications across all brain areas,

understanding how they are reorganized after traumatic events is important. Past work

has shown that these hubs can be reorganized in some situations such as comatose

patients (Achard et al., 2012). Whether randomization and hub reorganization occurs

after major surgery has not been demonstrated. Resilience analysis at the network level

is a way to get at this issue.

This chapter analyzed the whole brain’s functional connections using graph

theory analysis and resilience analysis to examine pre and post changes in patients

undergoing TKA surgery. First, the whole brain was parcellated into 234 components.

The general properties of the network were analyzed to compare the difference

between pre-surgery and post-surgery. Second, the resilience or robustness of the

network was evaluated to examine how the network is resistant to the insult of the

surgery. The targeted attack and randomized attack of the network were compared to

find the importance of different brain areas according to the node degree. Third, the

significant differences between pre-surgery and post-surgery in node strength was

analyzed for each brain area to compare surgery group and non-surgery group, and the

brain areas which were significantly impacted by the surgery were identified.

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4.2 Methods

4.2.1 FMRI Regions of Brain Areas

The whole brain was divided into 234 brain areas (ROIs) using the standard

mask created by Hagmann and colleagues (Hagmann et al., 2008). The BOLD signals

were extracted from each ROI by averaging all the signals from the voxels in the ROI.

4.2.2 Functional Connectivity Analysis

The functional connectivity between each pair of ROIs was quantified using the

Pearson cross correlation. Age, gender, pain, MED, and education were regressed out

from the functional connectivity. After regression, 234*234 connectivity matrices were

created for further analysis. Both preoperative and postoperative resting state functional

connectivity of each subject were calculated to evaluate changes in the whole brain

functional connectome related to surgery.

4.2.3 Graph Theoretical Analysis

A schematic diagram was shown in Figure 4-1 to illustrate the analytical

framework of this chapter. The functional connectivity matrix was created for each

subject in Figure 4-1 A. The adjacency matrix was generated using the 40% threshold

to remove the weak connectivity and keep the top 40% strong connectivity (positive

correlation) Figure 4-1 B.

The resilience or the robustness of the network was analyzed with simulating the

attack to the network by removing the node from the network one at a time to check the

changes of the network properties. Two attacks were simulated: targeted attack and

random attack. In the targeted attack, the global efficiency of the network was

calculated by removing the node in the network according to the descending order of

the node degree one at a time. In the random attack, the global efficiency of the network

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was calculated by randomly removing the node in the same network one at a time

without considering the order of the node degree. The continuous changes of the global

efficiency of the network was represented by the dynamic curves and compared

between pre-surgery and post-surgery.

We defined the parameters used to characterize a network next (Fornito et al.,

2016). The network properties were calculated using the functions provided by Brain

Connectivity Toolbox (Rubinov & Sporns, 2010). Global efficiency of a network is the

inverse of the mean of the shortest path length between each pair of nodes within the

network. It is a measure of integrated network topology. It is calculated according to:

𝐸𝑔𝑙𝑜𝑏 =1

𝑁(𝑁−1)∑ ∑

1

𝐿𝑚𝑖𝑛(𝑖,𝑗)

𝑁𝑗≠𝑖

𝑁𝑖=1 (4-1)

where Lmin denotes the shortest path length between node i and node j, N is the number

of the nodes.

The degree of a node i is the number of connection it has

𝑘𝑖 = ∑ 𝑎𝑖𝑗𝑁𝑗=1 (4-2)

where N is the number of connections, aij is the connection between i and j.

The node strength Si is defined as follows:

𝑆𝑖 = ∑ 𝑤𝑖𝑗𝑁𝑗=1 (4-3)

Where N is the number of connections, wij is the weighted connectivity between i and j.

Connection density was calculated to examine changes of network density when

the network was attacked. Connection density was analyzed by removing the node

according to the descending order of the node degree one at a time when the network

had the targeted attack. The mean functional connectivity was also calculated using the

same strategy as connection density. The mean functional connectivity was calculated

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by dividing the sum of the connectivity by the number of all possible connections in the

network. Connection density is defined as follows:

𝐾𝑑𝑒𝑛𝑠 =𝐾

(𝑁2−𝑁)/2 (4-4)

where N is the number of vertices, K is the number of edges.

The mean functional connectivity is defined as:

𝐶𝑚𝑒𝑎𝑛 =1

𝑁(𝑁−1)∑ ∑ 𝐶𝑖𝑗

𝑁𝑗≠𝑖

𝑁𝑖=1 (4-5)

where N is the number of nodes, Cij is the connectivity of node i and node j.

The clustering coefficients is defined as:

𝐶𝑙𝑤(𝑖) =2

𝑘𝑖(𝑘𝑖−1)∑ (𝑤𝑖𝑗𝑤𝑗ℎ𝑤ℎ𝑖)

1/3𝑗,ℎ (4-6)

where ki is the degree of node i, wij, wjh, and whi are the normalized edge weights, j and

h denote node j and node h.

The modularity is defined as:

𝑄 =1

2𝑚∑ [𝐴𝑖𝑗 −

𝑘𝑖𝑘𝑗

2𝑚]𝑖𝑗 𝛿(𝑐𝑖, 𝑐𝑗) (4-7)

where Aij denotes the weight of the edge connecting nodes i and j, ki and kj are the total

connectivity (node strength) for node i and j, the δ(ci,cj) is 1 only when ci=cj, and

m=(1/2)∑ 𝐴𝑖𝑗𝑖𝑗 is the sum of all edge weights.

Curve difference between pre-surgery and post-surgery was calculated by first

selecting the range of the nodes according to the descending order of the node degree

and then applying paired t test to calculate the p values for significance (p<0.05). The

surgery group and non-surgery group before and after surgery were analyzed for

resilience, connection density, and functional connectivity as well as the curve

difference.

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To evaluate changes in connectivity for each ROI, the node strength was

calculated based on the positive adjacency matrix and negative adjacency matrix in

Figure 4-1 C. The node strength is the sum of the connectivity between this node and

the rest of the 233 nodes in the whole brain to evaluate the importance of the node in

the entire network. The difference between pre-surgery and post-surgery was calculated

by node strength of pre-surgery minus that of post-surgery for each subject. The

significance was compared between the difference of surgery group (pre minus post)

and non-surgery group (pre minus post) using two sample t test. The brain areas with

significant difference between pre-surgery and post-surgery for the surgery group

compared to the non-surgery group were mapped onto the brain and the corresponding

anatomical structures in Figure 4-1 D.

4.3 Results

4.3.1 Changes in Global Network Properties

Modularity, clustering coefficient, global efficiency, and connectivity were

calculated for pre-surgery and post-surgery data. Comparison between pre-surgery and

post-surgery groups revealed that there were no significant changes in these global

network parameters both in the surgery group and the non-surgery group, as shown in

Figure 4-2.

4.3.2 Resilience Analysis of the Whole Brain Network

The resilience or the robustness of the network against two types of simulated

attacks: targeted attack and random attack was analyzed for pre-surgery group, post-

surgery group, pre-pseudo surgery group, and post-pseudo surgery group and the

results were shown in Figure 4-3 A and B, respectively. In targeted attack, the global

efficiency of the network showed no significant difference between pre-surgery group

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and post-surgery before the descending node order 150. The post-surgery group

showed postponed drop in global efficiency between the descending node order 150

and 234 compared to the pre-surgery group. In non-surgery group, there is no

difference between pre-pseudo surgery and post-pseudo surgery across all descending

node order. Only the post-surgery group showed postponed global efficiency when all

four groups were compared together in Figure 4-3 A.

The same network was used in random attack analysis for each subject. There

were no changes in global efficiency in surgery group and non-surgery group when the

node was removed from the network without considering the descending order of the

node degree as shown in Figure 4-3 B.

The changes in the global efficiency curves were statistically analyzed using

paired t test for significance across all subjects in each subtype group with the

descending order of the node degree from 181 to 220 as shown in Figure 4-4 A. When

the removed nodes were in the range of descending order 181-220, the surgery group

showed significant changes between pre-surgery group and post-surgery (p=0.0476).

When the removed nodes were in the range of descending order 201-220, the surgery

group also showed significant changes between pre-surgery group and post-surgery

(p=0.0291). When the removed nodes are in the range of descending order 220-230,

the surgery group did not show significant changes between pre-surgery and post-

surgery groups. These changes in global efficiency or resilience were only observed in

targeted attacks. There was no significant difference found in random attacks in Figure

4-4 B.

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4.3.3 Connection Density and Mean Functional Connectivity

Connection density was analyzed for pre-surgery and post-surgery data (Figure

4-5 A). The connection density showed changes in post-surgery group compared to the

pre-surgery group starting from the descending node order 150. No changes were seen

in non-surgery group. In surgery group, there were changes in post-surgery group

compared to the pre-surgery around node order 200. The non-surgery group, however,

showed no changes between pre-pseudo surgery and post-pseudo surgery. Again, the

surgery group only showed the changes related to targeted attacks after the patients

underwent surgery.

As shown in Figure 4-6, when the removed nodes were in the range of

descending order 181-220, the surgery group showed significant increase between pre-

surgery group and post-surgery (p=0.04930). When the removed nodes are in the range

of descending order 201-220 and 211-220, the surgery group also showed significant

increase between pre-surgery group and post-surgery (p=0.0437 and p=0.0405,

respectively). There is no significant difference found in the non-surgery group.

The mean functional connectivity, indicating overall how strong the connections

are in the defined network, was analyzed for comparison between pre-surgery and post-

surgery in Figure 4-5 B. The surgery group showed changes in post-surgery group

compared to the pre-surgery group starting from the descending node order 150. No

changes were seen in non-surgery group, as shown in Figure 4-5 B. In surgery group,

there were changes in post-surgery group compared to the pre-surgery around node

order 200. The non-surgery group, however, showed no difference between pre-pseudo

surgery and post-pseudo surgery in Figure 4-5 B. The surgery group only showed

changes related to targeted attack after the patients received the surgery.

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Statistically, when the removed nodes were in the range of descending order

181-220 and 201-220, the surgery group did not show significant difference between

pre-surgery group and post-surgery (p>0.05). When the removed nodes were in the

range of descending order 211-220, the surgery group showed significant increase

between pre-surgery group and post-surgery (p=0.0474). There is no significant

difference found in non-surgery group.

4.3.4 Brain Areas with Connectivity Changes

The adjacency matrix of the functional connectivity created using top 40%

connections was shown in Figure 4-7. For the node strength analysis, the adjacency

matrix was separated into positive connectivity matrix for pre-surgery group and post-

surgery group in Figure 4-7 A and B, respectively. Figure 4-7 C showed the difference

of post-surgery minus pre-surgery matrix in surgery group. Figure 4-7 D showed the

difference of post-surgery minus pre-surgery matrix in non-surgery group. The negative

connectivity matrix also included pre-surgery group and post-surgery group in Figure 4-

7 E and F, respectively. Figure 4-7 G showed the difference of post-surgery minus pre-

surgery matrix in surgery group. Figure 4-7 H showed the difference of post-surgery

minus pre-surgery matrix in non-surgery group.

The node strength was analyzed for both positive connectivity matrix and

negative connectivity matrix created from the adjacency matrix. The difference was

calculated between post-surgery and pre-surgery for surgery group and non-surgery

group. The nodes in surgery group with significant changes compared to non-surgery

group were mapped onto the brain with corresponding anatomical structures identified.

For positive connectivity in adjacency matrix, the nodes with significant

decreased node strength were shown in Figure 4-8 A in blue. The nodes with increased

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node strength were shown in Figure 4-8 A in red. For negative connectivity (converted

to absolute values) in adjacency matrix, the nodes with significant decreased node

strength were shown in Figure 4-8 B in blue. The nodes with increased node strength

were shown in Figure 4-8 B in red. The brain areas with decreased node strength and

increased node strength were listed in the table shown in Figure 4-8 for both positive

matrix and negative matrix. In positive connectivity, decreased connectivity were

observed in brain areas related to cognition and memory such as insula, amygdala and

putamen. The increased connectivity were mainly in precuneus, fusiform and parietal. In

negative connectivity, decreased connectivity were observed in brain areas such as

precuneus and parietal. The increased connectivity were mainly in lateral-occipital and

putamen.

The brain areas with the corresponding brain anatomic structures indicated were

displayed in Figure 4-9 for positive connectivity and Figure 4-10 for negative

connectivity. Figure 4-9 A-D showed the brain areas with decreased node strength in

blue. Figure 4-9 E-H show the brain areas with increased node strength in red. Different

brain slices were shown in Figure 4-9 I and J for decreased node strength and

increased node strength, respectively. For negative connectivity, the brain areas with

decreased node strength and increased node strength were shown in the brain

anatomic structures in Figure 4-10. Figure 4-10 A-D show the brain areas with

decreased node strength. Figure 4-10 E-H show the brain areas with increased node

strength. The slices for the brain map also were shown in Figure 4-10 I and J for

decreased node strength and increased node strength, respectively.

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4.4 Discussion

The whole brain connectome analysis was carried out to examine the pattern of

the connectivity matrix change within 48 hours after surgery. The global properties of

the brain network did not show significant changes, which means in general, the whole

brain still maintained a relatively stable and normal functioning after surgery. This is

reasonable when patients can still preform their normal daily tasks after surgery. While

some function networks have become impaired as shown in Chapters 2 and 3, overall,

the properties of the global brain network remain unchanged. Previous work has shown

that the global properties of the brain networks were homeostatically conserved even in

comatose patients (Achard et al., 2012).

The resilience of the whole brain connectome. The resilience analysis

examined the performance of the network by evaluating the global efficiency when the

brain areas were removed one by one according to their importance in connections. The

resilience analysis revealed that the functions of the network have rearranged across all

brain areas to resist the insult of surgery/anesthesia. This reorganization mainly

happened in local brain networks with low node connections or node degree instead of

on the whole brain network scale. The global efficiency in low degree areas after

surgery was relatively high compared to the global efficiency before surgery. This

change indicated that the more randomized organization was formed after surgery and

the importance was distributed to lower levels of the network (Joyce et al., 2013; Lo et

al., 2015). When we tested the resilience in randomized network, the resilience did not

show significant changes in any groups, which means that the reorganization especially

the increased functions was not random and may reflect neural compensation (Lo et al.,

2015).

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The changes in connection density and mean functional connectivity were also

found in low hub ranges of brain areas. This indicated that the brain areas with low

connections became more connected after surgery and the strength of the connections

was also significantly increased; this may be another form of neural compensation to

maintain normal functions.

Brain areas with connectivity changes. The connectivity of each brain area

was compared between pre-surgery and post-surgery groups. Strong connectivity of the

ROI is often interpreted to mean that the brain area is more involved in brain functions

and vice versa. The previous results of intra-network connectivity changes and inter-

network connectivity changes in Chapters 2 and 3 suggested that in well-defined

cognitive networks, connectivity is decreased both within and between brain networks

after surgery. In this chapter, we showed that the connectivity of the brain connectome

also showed changes after surgery within 48 hours. Some had less connectivity when

the sum of the connections was compared between these nodes and the rest of the

other nodes in terms of node strength. These areas included bilateral insula, putamen,

temporal pole, amygdala, and superiortemporal cortex. The functions of these brain

areas are directly related to working memory, modulation of memory consolidation,

sensory processing, homeostasis, executive function, and movement. Less connectivity

after surgery may be interpreted to indicate that the brain activity was impaired in these

brain areas. It is known that general anesthesia has significant effects on many brain

areas including reduced and increased connectivity (Hudetz, 2012). The impaired brain

areas in our study match the findings on impaired brain areas as a result of general

anesthesia, especially reduced connectivity in default mode network, sensory cortex,

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and insula (Hudetz, 2012; Martuzzi et al., 2010). Given that in our study the impact of

general anesthesia and surgery itself is difficult to separate, the similarity in brain areas

suffering injuries after surgery suggests a major role of general anesthesia in the

changes of these brain networks.

Increased connectivity was also found in some brain areas including precuneus,

occipital cortex, fusiform, inferior temporal gyrus, and parahippocampal. These brain

areas were involved in diverse processes including working memory, episodic memory

retrieval, attention, visuospatial activity, consciousness, and object recognition. The

increased connectivity in these brain areas may indicate that more activity was needed

to compensate for the lost functions of other brains in order to perform normal brain

functions after surgery. Similar findings have been reported in mild traumatic brain injury

and attributed to neural plasticity (Iraji et al., 2016). In this study the areas showing

increased connectivity after injury included frontal-occipital functional connectivity, PCC

and association areas. In other words, brain regions related to working memory,

executive functions were affected (Hillary et al., 2011, 2014; Nakamura et al., 2009;

Pandit et al., 2013).

Brain connectivity is evaluated using Pearson’s correlation. Correlation can be

positive and negative. The meaning of negative correlation remains debated. Across the

whole brain there are far fewer negative correlation than positive correlations. We found

that there was reduced anti-correlation in precuneus, and superiorparietal but increased

anti-correlation in bilateral putamen, and lateraloccipital, and right fusiform. Further

studies are needed to understand the meaning of these findings.

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In this chapter we considered the properties of the whole brain network. It

represents a natural logical evolution from Chapters 2 and 3. The resilience or

robustness analysis was performed for pre-surgery and post-surgery. In post-surgery,

the resilience was improved so that the network was more robust to further injuries or

perturbations after surgery. Moreover, pre-surgery hub nodes became less hub-like in

the network after surgery, whereas the nodes ranked low in hub-ness pre-surgery

became more important compared to pre-surgery. This phenomenon was also observed

in connection density and mean functional connectivity. More connection was found in

less important nodes and the connectivity also increased for these nodes after surgery,

which may indicate that brain regions with less significance pre-surgery took on

additional functions to compensate for the lost functions of brain networks that suffered

impairment due to injury. It is important to note that, nodes with significant changes in

strength appeared to be located in brain areas related to memory, such as bilateral

amygdala, bilateral insula, and bilateral lateral temporal lobes. Given that memory

dysfunction is a major symptom of POCD, examining the long term consequences of

the observed connectivity changes is an important future task.

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Figure 4-1. Schematic diagram for the whole brain network analysis. A) The mask used

to divide the brain into 234 ROI. B) Adjacency matrix of functional connectivity used for network analysis. C) Network properties calculated include: resilience, node strength, node degree, and clustering coefficient, etc. D: Brain areas showing decreased (blue) and increased (red) connectivity after surgery.

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Figure 4-2. The topological properties of the brain networks in surgery group and non-surgery group before and after surgery. A: comparison of surgery group between pre and post-surgery in functional connectivity, global efficiency, clustering, and modularity. B: comparison of non-surgery group between pre and post-surgery in functional connectivity, global efficiency, clustering, and modularity. *p<0.05

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Figure 4-3. Resilience analysis: global efficiency was calculated after removing the nodes with descending order. A: Targeted attack in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Random attack in pre-surgery patient; post-surgery patient group; pre-surgery control group; post-surgery control group.

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Figure 4-4. Comparison between pre-surgery and post-surgery in descending order range from 181 to 220. A: Targeted attack in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Random attack in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. *p<0.05

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Figure 4-5. Connection density and mean functional connectivity calculated by removing the nodes with descending order. A: Connection density in pre-surgery patien group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Mean functional connectivity in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group.

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Figure 4-6. Comparison between pre-surgery and post-surgery in descending order range from 181 to 220. A: Connection density in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Mean functional connectivity in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group.

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Figure 4-7. Adjacency matrix keeping top 40% of functional connectivity. Positive connectivity matrix (A, B) and negative connectivity matrix (E, F). A: Connectivity matrix of pre surgery patient group; B: connectivity matrix of post-surgery patient group; C: the difference of post-surgery and pre-surgery connectivity matrix in surgery group; D: the difference of post-surgery and pre-surgery matrix in non-surgery group; E: connectivity matrix of pre-surgery patient group; F: connectivity matrix of post-surgery patient group; G: the difference of post-surgery and pre-surgery matrix in surgery group; H: the difference of post-surgery and pre-surgery matrix in non-surgery group.

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Figure 4-8. Brain area showing changes in connectivity. A. Positive connectivity: the brain areas with decreased or less positive node strength shown in blue. The brain areas with increased or more positive node strength shown in red. B. Negative connectivity: the brain areas with decreased or less negative node strength shown in blue. The brain areas with increased node or more negative strength shown in red.

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Figure 4-9. Areas with increased or decreased functional connectivity following surgery (positive adjacency matrix). Brain areas with decreased node strength were shown in A, B, C, and D. Brain areas with increased node strength were shown in E, F, G, H. Threshold at p < 0.05.

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Figure 4-10. Brain areas with increased or decreased functional connectivity following surgery (negative adjacency matrix). Brain areas with decreased node strength were shown in A, B, C, and D. Brain areas with increased node strength were shown in E, F, G, H. Threshold: p<0.05.

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CHAPTER 5 CONCLUSIONS

In this dissertation we carried out systematic research to look into the changes of

functional brain networks in older adults who underwent TKA surgery using the medical

imaging technique of fMRI. The main focus was to evaluate the acute effects of major

surgery (within 48 hours), which could form the foundation for studying long-term

changes in the human brain and related cognitive side effects such as POCD. This

dissertation treated three topics: intra-network connectivity changes following surgery,

inter-network connectivity changes following surgery, and whole brain functional

connectome changes following surgery. Our main findings can be summarized as

follow:

1. Intra-network analysis. (1) The connectivity in three important resting state

networks DMN, SN, and CEN had significant decline after surgery. No significant

changes were found in non-surgery group. (2) MCI surgery group was more susceptible

to the surgery-related functional injury and had more decline in connectivity compared

to non-MCI surgery group. (3) All the nodes in DMN and SN had significant decline in

node strength; a subset of the nodes in CEN had significant functional connectivity

decline. (4) No change was found in VN, indicating that the injury was selective, and the

cognitive networks were more vulnerable.

2. Inter-network analysis. The three cognitive networks were subjected to inter-

network functional connectivity analysis. (1) The anti-correlated functional connectivity

between DMN and SN declined significantly after surgery in surgery patient group.

Significant increased connectivity between DMN and CEN was found, but there was no

significant changes in SN-CEN inter-network functional connectivity. There were no

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significant changes in the inter-network functional connectivity in control group. (2)

Patients with MCI had more pronounced DMN-SN functional decline compared to

patients without MCI. (3) The inter-network connectivity of DMN-SN pre-surgery had

significant correlation with surgery-related changes in DMN or SN intra-network

connectivity; this finding may help with the development of predictive neural markers for

intra-network connectivity change following surgery.

3. Whole brain functional connectome analysis. (1) The resilience of the brain

network had significant increase in brain areas with low functional connections pre-

surgery. (2) The connection density and mean functional connectivity were increased in

brain areas with low functional connections pre-surgery. (3) Brain areas with

significantly decreased connectivity included bilateral insula, putamen, amygdala, and

temporapole, and temporal lobe. Significantly increased connectivity were found in

precuneus, fusiform, parahippocampal, and lateral occipital cortex.

Our findings demonstrated unequivocally that major surgeries such as TKA with

general anesthesia have a significant impact on brain functional network organization in

older adults. There is clear evidence of neural injury to key brain networks such as

DMN, SN, and CEN and to key brain regions such as amygdala, putamen, and insula.

These changes underlie postoperative cognitive decline (POCD) although much more

research is required to draw firm conclusions. Further studies recruiting more subjects

and with longer-term follow up to will provide the needed information about how acute

changes observed here can lead to long-term disability and impairments.

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BIOGRAPHICAL SKETCH

Hua Huang was born in Anhui, China. He received his bachelor’s degree in

electrical engineering in 2002. He graduated from Chongqing University and received

his master’s degree in biomedical engineering focusing on cell mechanics and

hemorheology in 2006. In 2010, he graduated from Fudan University with Ph.D. degree

in microelectronics and solid state electronics focusing on microelectromechanical

systems (MEMS), optics and acoustics at microscale, and lab on a chip. He joined

Ph.D. program in 2013 at University of Florida in biomedical engineering focusing on

neuroimaging and neuroscience and he will graduate in 2018.