Yale UniversitySchool of Medicine

Alan Anticevic, Ph.D.Department of Psychiatry

The Baby and the Bathwater:

Signal and Noise in Psychiatric Neuroimaging

Challenges Facing Clinical Neuroimaging

SequenceSequence Methodological Conceptual

T1 / T2 weighted ‘structural’ scans • Motion

• What aspect of ‘structure’?

• E.g. sulcal depth vs. myelin content

EchoPlanar SpinEcho Images • Motion (less so) • Reconstruct ‘ground truth’ geometry

across modalities

T2* weighted images

sensitive to BOLD

• Motion • Breathing• Cardiac pulsation • Fatigue• Performance• Other

• What is BOLD signal?

• Separating ‘signal’ / ‘noise’ / ‘artifact’

EPI Diffusion Weighted Images

(DWI)• Motion

• Inherently resolution-starved

• Resolving crossing fibers

• Mono- vs. Poly-synaptic

Methodological vs. Conceptual Challenges

Example of a Major Conceptual Challenge

What is the BOLD Signal?Indirect measureof neuronal signal

Heeger & Ress (2002). Nature. Logothetis (2008). Nature.

Aggregate measureof total synaptic activity?

A typical fMRI voxel of 55 ml in size contains: • 5.5 million neurons • 2.2–5.5 x 1010 synapses• 22 km of dendrites• 220 km of axons

Baby vs. Bathwater - It’s Complicated

“SIGNAL”

ARTIFACT

What is fMRI Designed To ‘See’?

7A MST VIP LIP DP PIT

TF AIT

V4

V3A V4-VA

V3 VP

V2

MT

V1

CNS

Systems

Maps

Columns

Neurons

Synapses

Molecules

Sejnowski, T.J., et al. (2014). Nature Neuro.

What is fMRI Designed To ‘See’?

7A MST VIP LIP DP PIT

TF AIT

V4

V3A V4-VA

V3 VP

V2

MT

V1

CNS

Systems

Maps

Columns

Neurons

Synapses

Molecules

Sejnowski, T.J., et al. (2014). Nature Neuro.

What is fMRI Designed To ‘See’?

7A MST VIP LIP DP PIT

TF AIT

V4

V3A V4-VA

V3 VP

V2

MT

V1

CNS

Systems

Maps

Columns

Neurons

Synapses

Molecules

Sejnowski, T.J., et al. (2014). Nature Neuro.

Have We Made Any Progress?

1995Biswal, B., et al. Mag Reson Med.

Identification of so-called ‘resting-state’ phenomenon

2011Yeo, A., et al. J Neurophys.

Identification of major functional networks in 1000s

of people

2016Glasser, A., et al. (In Press). Nature.

Comprehensive parcellation of the human cerebral cortex

across modalities

YES !

2015 NIMH Strategic Plan & The Connectome

Strategy 1.3 Map the connectomes for mental illnesses

So What’s the Key Problem Here?

Clinical effects could be driven by artifact that is unrelated to the underlying neurobiology!

Replication is NOT enough in this case

Example of Clinical Neuroimaging Progress?

• Anatomically segregated

• Readily defined using

neuroimaging tools

• Connected with entire cortical

mantle in an organized way

• Anatomically similar in most

individuals

Starting with the Thalamus

Even if Effects Do Replicate Are They ‘Real’?

Klingner et al. (2014)

Woodward et al. (2013)Welsh et al. (2010)

Thalamic Dysconnectivity in Schizophrenia (N=90)

6-6

A B

scz>concon>sczaxial view

thalamus seed

lateral - Rlateral - L

medial - L medial - R

dorsal - Ldorsal - R

scz > concon > scz 6-6 Z value

Z value

z=37:52

z=13:28

z=-10:4

z=-34:-19

Anticevic et al. (2013)

Anticevic et al. (2015)

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

1

2

3

4

5

6

-0.15 -0.10 -0.05 0.00 0.05 0.10

Group

CHR-C n=21

CHR n=222

HCS n=154

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

2

4

6

8

-0.10 -0.05 0.00 0.05 0.10

Group

CHR-C n=21

CHR n=222

HCS n=154

Regions Showing Between-group Differences in Thalamic Connectivity

c ROI 1 - Left Cerebellum

b

1

3

4

2

5

Over-connectivityUnder-connectivity

medial - L medial - R

lateral - Rlateral - L

a

-0.05

-0.03

-0.01

0.01

0.03

0.05

-0.1

-0.07

-0.04

-0.01

0.02

0.05

0

0.02

0.04

0.06

0.08

0.1

0.12

-0.07

-0.04

-0.01

0.02

0.05

0.08

-0.07

-0.04

-0.01

0.02

0.05

0.08

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

ROI 2 - Right Lateral PFC

Healthy Control Subjects (HCS) N=154

Clinically High Risk Subjects - Converted (CHR-C) N=21Clinically High Risk Subjects (CHR) N=222

ROI 4 - Right Sensory / Motor Cortex

ROI 5 - Anterior Cingulate

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

2

4

6

8

-0.10 -0.05 0.00 0.05 0.10 0.15

Group

CHR-C n=21

CHR n=222

HCS n=154

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

1

2

3

4

-0.10 -0.05 0.00 0.05 0.10 0.15

Group

CHR-C n=21

CHR n=222

HCS n=154

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

-0.10 -0.05 0.00 0.05 0.10 0.15

Group

CHR-C n=21

CHR n=222

HCS n=154

Surface view Volume view

Thalamic Connectivity0

8

-0.1 0.1

# of

Vox

els

0

Hg = .6** *** Hg = .7

Hg = .9 Hg = .8

Hg = .5

ROI 3 - Left Sensory / Motor Cortex

Thalamic Connectivity0

6

-0.1 0.1

# of

Vox

els

0

d

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

Thalamic Connectivity0

8

-0.1 0.15

# of

Vox

els

0

# of

Vox

els

Thalamic Connectivity0

8

-0.1 0.150

# of

Vox

els

Thalamic Connectivity0

3

-0.1 0.150

*

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

e f*** ***

g

ROI 2

ROI 5

ROI 3 ROI 4

effectsconverge

in prodrome

Yang et al. (2014)

Höflich et al. (2015)

effectsconvergeon ketamine

Even if Effects Do Replicate Are They ‘Real’?

Klingner et al. (2014)

Woodward et al. (2013)Welsh et al. (2010)

Thalamic Dysconnectivity in Schizophrenia (N=90)

6-6

A B

scz>concon>sczaxial view

thalamus seed

lateral - Rlateral - L

medial - L medial - R

dorsal - Ldorsal - R

scz > concon > scz 6-6 Z value

Z value

z=37:52

z=13:28

z=-10:4

z=-34:-19

Anticevic et al. (2013)

Anticevic et al. (2015)

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

1

2

3

4

5

6

-0.15 -0.10 -0.05 0.00 0.05 0.10

Group

CHR-C n=21

CHR n=222

HCS n=154

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

2

4

6

8

-0.10 -0.05 0.00 0.05 0.10

Group

CHR-C n=21

CHR n=222

HCS n=154

Regions Showing Between-group Differences in Thalamic Connectivity

c ROI 1 - Left Cerebellum

b

1

3

4

2

5

Over-connectivityUnder-connectivity

medial - L medial - R

lateral - Rlateral - L

a

-0.05

-0.03

-0.01

0.01

0.03

0.05

-0.1

-0.07

-0.04

-0.01

0.02

0.05

0

0.02

0.04

0.06

0.08

0.1

0.12

-0.07

-0.04

-0.01

0.02

0.05

0.08

-0.07

-0.04

-0.01

0.02

0.05

0.08

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

ROI 2 - Right Lateral PFC

Healthy Control Subjects (HCS) N=154

Clinically High Risk Subjects - Converted (CHR-C) N=21Clinically High Risk Subjects (CHR) N=222

ROI 4 - Right Sensory / Motor Cortex

ROI 5 - Anterior Cingulate

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

2

4

6

8

-0.10 -0.05 0.00 0.05 0.10 0.15

Group

CHR-C n=21

CHR n=222

HCS n=154

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0

1

2

3

4

-0.10 -0.05 0.00 0.05 0.10 0.15

Group

CHR-C n=21

CHR n=222

HCS n=154

Average connectivity strength [Fz]

# of

vox

els

(acr

oss

subj

ects

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

-0.10 -0.05 0.00 0.05 0.10 0.15

Group

CHR-C n=21

CHR n=222

HCS n=154

Surface view Volume view

Thalamic Connectivity0

8

-0.1 0.1

# of

Vox

els

0

Hg = .6** *** Hg = .7

Hg = .9 Hg = .8

Hg = .5

ROI 3 - Left Sensory / Motor Cortex

Thalamic Connectivity0

6

-0.1 0.1

# of

Vox

els

0

d

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

Thalamic Connectivity0

8

-0.1 0.15

# of

Vox

els

0

# of

Vox

els

Thalamic Connectivity0

8

-0.1 0.150

# of

Vox

els

Thalamic Connectivity0

3

-0.1 0.150

*

Fish

er r-

to-Z

(T

hala

mic

Coup

ling)

e f*** ***

g

ROI 2

ROI 5

ROI 3 ROI 4

effectsconverge

in prodrome

Yang et al. (2014)

Höflich et al. (2015)

effectsconvergeon ketamine

What is the probability that all these effects are spurious and

represent a bonafide translational dead end?

The global brain signal (GS) is the spatial average of the time-varying BOLD signals from all voxels in the brain. It is often regressed out to remove physiological noise.

The Role of Global Signal

Fox, M.D, et al. (2006). PNAS.

Top Priorities - Removing Known Artifact

MOTION• Methods: regression, scrubbing, ICA, de-nosing via global signal removal

BREATHING• Methods: regression, scrubbing, ICA, de-nosing via global signal removal

CARDIAC PULSATION• Methods: regression, scrubbing, ICA, de-nosing via global signal removal

Let’s Combine Complementary Tools & Approaches

Refining the neurobiologically-grounded imaging paradigm• Quantitatively understanding sources of ‘artifact’ in BOLD; namely breathing and motion (e.g. Power)• Integrating concurrent electrophysiology Finding the mechanism: 1-to-Many Mapping Problem• Identify successful examples of reverse-engineering via preclinical models (e.g. Halassa, Small, Arnsten)

Leverage advances in theoretical neuroscience• Generate strong inference experimental predictions about hypothesized clinical effects• Computational models do not move, breathe or have cardiac artifact

Test for convergence via causal pharmacological manipulation• E.g. the NMDAR antagonism model via ketamine (Krystal et al, 1994).

Where Do We Go From Here?

" My therapist set half a glass of water in front of me. He asked me if I was an optimist or a pessimist. So I drank the

water and told him I was a problem solver. "

- Unknown

Thank you for your attention!