Mental-Orientation: A New Approach to Assessing Patients Across theAlzheimer’s Disease Spectrum

Gregory Peters-Founshtein, Michael Peer, Yanai Rein, Shlomzion Kahana Merhavi, Zeev Meiner,and Shahar Arzy

Hadassah Hebrew University Medical Center, Jerusalem, Israel, and Hebrew University of Jerusalem, Jerusalem, Israel

Objective: This study aims to assess the role of mental-orientation in the diagnosis of mild cognitiveimpairment and Alzheimer’s disease using a novel task. Method: A behavioral study (Experiment 1)compared the mental-orientation task to standard neuropsychological tests in patients across the Alzhei-mer’s disease spectrum. A functional MRI study (Experiment 2) in young adults compared activationsevoked by the mental-orientation and standard-orientation tasks as well as their overlap with brainregions susceptible to Alzheimer’s disease pathology. Results: The mental-orientation task differentiatedmild cognitively impaired and healthy controls at 95% accuracy, while the Addenbrooke’s CognitiveExamination, Mini-Mental State Examination and standard-orientation achieved 74%, 70% and 50%accuracy, respectively. Functional MRI revealed the mental-orientation task to preferentially recruit brainregions exhibiting early Alzheimer’s-related atrophy, unlike the standard-orientation test. Conclusions:Mental-orientation is suggested to play a key role in Alzheimer’s disease, and consequently in earlydetection and follow-up of patients along the Alzheimer’s disease spectrum.

General Scientific SummaryIn the current study, we present a new paradigm to test orientation and demonstrate its diagnosticbenefits over standard neuropsychological tests in Alzheimer’s disease (AD) diagnosis, highlightingthe central role of orientation disturbances in AD pathology. Through the use of functional MRI(fMRI), we link these benefits to the overlap between orientation-evoked patterns of activity andAD-susceptible regions.

Keywords: orientation, Alzheimer’s disease, mild cognitive impairment, functional magnetic resonanceimaging

Supplemental materials: http://dx.doi.org/10.1037/neu0000463.supp

Orientation in time, space, and person is a fundamental cogni-tive faculty and the bedrock of neurological and psychiatric clin-ical evaluation. Orientation is defined as the “tuning between thesubject and the internal representation he forms of the correspond-ing public reference system” (Berrios, 1982), and is evaluated inthe domains of time, space and person. While this definitionimplies an extensive cognitive processing of events, places andpeople (Arzy, Adi-Japha, & Blanke, 2009; Peer, Salomon, Gold-

berg, Blanke, & Arzy, 2015; Tavares et al., 2015), current neuro-psychological evaluations of orientation are restricted to testingonly the patient’s knowledge about the present date and currentlocation (Rapoport & Rapoport, 2015). Recently, Peer and col-leagues (Peer et al., 2015) introduced a novel approach to mental-orientation, requesting subjects to determine which of two events,places or people is closer to themselves. Examining the task underfunctional MRI (fMRI) revealed a highly consistent network of

This article was published Online First May 21, 2018.Gregory Peters-Founshtein, Michael Peer, and Yanai Rein, Neuropsy-

chiatry Lab, Department of Neurology, Hadassah Hebrew University Med-ical Center, Jerusalem, Israel and Department of Medical Neurobiology,Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel;Shlomzion Kahana Merhavi, Departments of Neurology and PhysicalMedicine and Rehabilitation, Hadassah Hebrew University Medical Cen-ter, and Hadassah Hebrew University School of Occupational Therapy,Faculty of Medicine, Hebrew University of Jerusalem; Zeev Meiner,Departments of Neurology and Physical Medicine and Rehabilitation,Hadassah Hebrew University Medical Center, and Department of Neuro-

biology, Faculty of Medicine, Hebrew University of Jerusalem; ShaharArzy Neuropsychiatry Lab, Department of Neurology, Hadassah HebrewUniversity Medical Center, and Department of Medical Neurobiology,Faculty of Medicine, Hebrew University of Jerusalem.

We thank Mor Nitzan for guidance in machine-learning analyses, andTamir Ben-Hur and JP Newman for important discussions. The studywas supported by the Israeli Science Foundation and the Alzheimer’sFoundation of America.

Correspondence concerning this article should be addressed to Shahar Arzy,Neuropsychiatry Lab, Department of Neurology, Hadassah Hebrew UniversityMedical Center, Jerusalem, Israel. E-mail: shahar.arzy@ekmd.huji.ac.il

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

Neuropsychology© 2018 American Psychological Association 2018, Vol. 32, No. 6, 690–6990894-4105/18/$12.00 http://dx.doi.org/10.1037/neu0000463

690

brain regions, including the precuneus, posterior cingulate, inferiorparietal, lateral temporal and medial prefrontal cortices, to underlieprocessing of orientation in time, space and person (Peer et al.,2015).

Pathologies of orientation are long-known in the neuropsychi-atric literature and may be further classified according to thedisrupted function or their underlying neuroanatomical basis (Peer,Lyon, & Arzy, 2014). For instance, topographical disorientation isrelated to retrosplenial lesions (Aguirre & D’Esposito, 1999),while timeline disorganization is associated with medial temporaland orbitofrontal lesions (Peer et al., 2014; Schnider, Von Däni-ken, & Gutbrod, 1996). While these are related to specific orien-tation domains, Alzheimer’s disease (AD) includes deficits inautobiographical memory, spatial cognition and identifications ofpeople, comprising the three fundamental orientation domains(Abe et al., 2007; Bruen, McGeown, Shanks, & Venneri, 2008;Cushman, Stein, & Duffy, 2008; delpolyi, Rankin, Mucke, Miller,Gorno-Tempini, 2007; Piolino et al., 2003). Several neuroanatomi-cal evidence support the relations between mental-orientation andAD: the well-defined pattern of neural atrophy in AD, involvingfrontal, medial temporal, and parietal regions (Braak & Braak,1991; Buckner et al., 2005; Desikan et al., 2009), was shown toinclude regions known to manage temporal, spatial and socialprocessing (Svoboda, McKinnon, & Levine, 2006; Tavares et al.,2015; Zhang, Copara, & Ekstrom, 2012). Additionally, a growingbody of evidence is linking AD to disruption of the default net-work (DN; Buckner et al., 2005; Zhou et al., 2010). The DN is anetwork of brain regions active when individuals engage in self-referential tasks such as autobiographical memory retrieval andfuture planning (Buckner, Andrews-Hanna, & Schacter, 2008).Neuroanatomically, the DN starkly overlaps brain regions suscep-tible to AD pathology (Buckner et al., 2005). The mental-orientation system was found to significantly overlap the DN (Peeret al., 2015).

To support the role of mental-orientation in AD we have con-ducted two separate experiments: In a clinical study (Experiment1) we tested the mental-orientation task in patients across the ADspectrum, and compared it to the standard-orientation test, Mini-Mental State Examination and Addenbrooke’s Cognitive Exami-nation. In a neuroimaging study (Experiment 2) we compared theneural basis of the mental-orientation task to that of the standardorientation test, as well as their overlap with brain regions suscep-tible to AD pathology, using fMRI. Healthy young participantwere recruited, as patients with MCI/AD cannot complete the long(90 min) protocol required.

Method

Experiment 1: A Clinical Study of Orientation Alongthe AD Spectrum

Participants. 60 individuals (26 males, mean age: 77.95 �7.56, for detailed demographical data see Table 1) participated inthe study: 40 patients (20 with AD and 20 with MCI) and 20age-matched healthy control subjects. Participants underwent a fullneurological examination as well as neuropsychological evaluationthat included the Addenbrooke’s Cognitive Examination (ACE;Mathuranath, Nestor, Berrios, Rakowicz, & Hodges, 2000) and theFrontal Assessment Battery (FAB; Dubois, Slachevsky, Litvan, &

Pillon, 2000). Patients were recruited from the memory disordersclinic in Hadassah Medical Center and met the National Instituteon Aging and the Alzheimer’s Association clinical criteria for ADand MCI (Albert et al., 2011; McKhann et al., 2011). All individ-uals with MCI met the research criteria for amnestic MCI (Gau-thier et al., 2006; Petersen et al., 1999). In addition, patients andcontrol subjects were longitudinally monitored to validate theirdiagnosis for 11.5 � 1.25 months (see Table 1). All participantsprovided written informed consent, and the study was approved bythe ethics committee of the Hadassah Hebrew University MedicalCenter.

Stimuli and experimental procedure. In the mental-orientation task participants were presented with pairs of stimuliconsisting of names of either two cities, two events, or two people,and were asked to determine which of the two is closer to them-selves (See Fig. S1 for study design): spatially closer to theircurrent location (which location is physically closer to me: “Tel-Aviv” or “Jerusalem”), chronologically closer to the present time(which event occurred more recently: “Retirement party” or“Silver wedding”), or personally closer to themselves (which per-son do you feel closer to: name of brother or name of boss).Stimuli were presented in a randomized three-block design, witheach block containing 11 consecutive trials. Participants wereinstructed to respond accurately but as fast as possible. Successrates (SRs) and response times (RTs) were recorded. The first trialserved as training to ensure all participants understood the instruc-tions. To obtain stimuli, prior to performing the task, participantswere presented with a list of potential stimuli and regarding eachwere asked to approximate either its location (for space stimuli) oryear (for time stimuli). Failing to reference both the relevant regionof the country and at least one nearby landmark (space), ormisevaluating by more than five years (time), resulted in thespecific stimuli to be removed from further testing. In addition,participants were requested to generate a list of five family mem-bers, five acquaintances, and five public figures (for additionaldetails see supplementary materials). In the standard-orientationtest, SRs were recorded for the 10 orientation items included in theMini-mental state examination (MMSE; Folstein, Folstein, &McHugh, 1975; five regarding the present time and five regardingthe current location), as well as for the complete MMSE and ACE.

Table 1Demographic Data

Clinical group HC MCI AD

ParametersMale | Female 6 | 14 9 | 11 11 | 9Age (years) 75.3 � 1.93 78.55 � 1.39 80 � 1.61Education (years)a 15.57 � .81 14.05 � .76 11.7 � .97MMSEa 29.4 � .19 27.85 � .31 22.7 � .73ACEa,b,c 91.74 � 1.15 80.37 � 2.16 60.84 � 3.4HISa 1.75 � .29 2.9 � .42 3.25 � .37Follow-up duration (months) 12.32 � 1.83 11.78 � 2.12 10.4 � 2.57

Note. MMSE � Mini-mental state examination; ACE � Addenbrooke’scognitive examination; HIS � Hachinski Ischemic Scale; AD � Alzhei-mer’s disease; MCI � mild cognitive impairment; HC � healthy control.a Statistically significance (p � .05) between HC and AD. b Statisticallysignificance (p � .05) between HC and MCI. c Statistically significance(p � .05) between MCI and AD.

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

691MENTAL-ORIENTATION IN ALZHEIMER’S DISEASE

Statistical analysis. For the mental-orientation task, effi-ciency scores (Townsend & Ashby, 1983; ES) were computed foreach subject and domain separately by calculating the ratio be-tween the SR and mean RT. A global ES was calculated for eachsubject by averaging the ESs across the three domains. Subse-quently, mean ESs were compared across the three groups (AD,MCI, HC) using ANOVA and Scheffé post hoc tests. For thestandard-orientation test, MMSE and ACE, SR scores were re-corded according to the ACE testing guidelines (Mathuranath etal., 2000). To better evaluate the predictive power in the individualparticipant level, we performed a multivariable ordinal cumulativelogistic regression (Ananth & Kleinbaum, 1997). In logistic re-gression, the probability of a binary outcome P (Y � 1), here ADand MCI, is estimated using the logit of the sum of multipleindependent predictor variables (X1, X2, . . . Xk), here RTs and SRs,weighted by confidents (�, �1, �2, . . . �k):

P(Y � 1 |X1, X2, . . . Xk) � 11 � e�(����1X1� �2X2�. . .��kXk)

Ordinal logistic regression expands the standard logistic regres-sion model to account for ordered category outcomes, as in thepresent study design (Ananth & Kleinbaum, 1997). Adhering tothe fact that classification is clinically relevant between every twoconsecutive outcomes (HC-MCI and MCI-AD), we constructedeight separate logistic regression models, alternately consideringmental-orientation SRs and RTs as well as standard-orientation,MMSE and ACE SRs as predictor variables, to estimate theprobability of an MCI or AD outcome.

To further determine the diagnostic value of the model producedby the logistic regression, receiver operating characteristic (ROC)curves were plotted (see Figure 2; for ROC procedure see supple-mentary materials). Additionally, an optimal threshold, maximiz-ing sensitivity and specificity, was determined by calculating theYouden’s index (Youden, 1950), to determine classification accu-racy.

In order to test the observed data set for multicollinearity,variance inflation factor (VIF) was calculated for each of thepredictor variables (Dormann et al., 2013). To control for overfit-ting of the model to the data, a leave-1-out cross-validation testwas conducted. Finally, to further support the classification results,a permutation test (Good, 1994) was performed (for additionaldetails see supplementary materials).

Experiment 2: Neuroimaging of the Mental-Orientation and Standard-Orientation Tasks inHealthy, Young Adults

We recorded 16 healthy participants (9 males, mean age: 28 �3.42 years, all right-handed) performing adapted versions of bothorientation tasks, as well as a lexical control task, while undergo-ing fMRI. Healthy young participant were recruited, as patientswith MCI/AD cannot complete the long (90 min) protocol requiredto produce the sufficient number of repetitions in all conditions.

The mental-orientation task was performed as described above,requiring subjects to compare proximity of places, events andpeople to themselves. In the fMRI-adapted standard-orientationtask participants were required to determine which of the twostimuli indicates their current location (e.g., “Tel Aviv” vs. “Jeru-salem”), present time (e.g., “2017” vs. “2012”), or personal iden-

tity (e.g., first name Abby vs. first name Leah), corresponding torequirements of the orientation section in the MMSE and ACE. Ina lexical control task, participants were presented with stimulipairs from the same sets but were instructed to indicate which ofthe words contains the letter “A”.

MRI data acquisition. For details regarding MRI data acqui-sition please refer to supplementary materials.

MRI preprocessing. For details regarding MRI data prepro-cessing please refer to supplementary materials.

Identification of domain-specific and domain-generalactivity. To assess the selective activations elicited by differentexperimental tasks, we applied a group-level random-effects gen-eral linear model (GLM) analysis (Friston et al., 1995). In order toidentify the full extent of activation for each domain, we contrasteddomain-specific activations separately for mental-orientation andstandard-orientation with the lexical control task. To directly com-pare brain regions recruited during each of the two tasks, wecontrasted mental-orientation and standard-orientation evoked ac-tivations with each other across all domains. Finally, for eachsubject, we compared mental-orientation and standard-orientationactivity (above lexical control) across the entire brain by quanti-fying the number of suprathreshold voxels (p � .05, FDR cor-rected) active for all domains and separately for the space, timeand person conditions. These were further compared in two re-gions of interest: (a) the DN, independently identified in eachsubject from a resting-state fMRI scan using independent-components analysis (ICA); (b) brain regions susceptible to earlyAD-related atrophy (Desikan et al., 2009), including the entorhi-nal, parahippocampal, superior-temporal and temporal pole corti-ces as well as the amygdala and hippocampus.

Results

Experiment 1: Mental-Orientation DistinguishesBetween MCI and HC Unlike Standard-Orientation

Mental-orientation ES showed significant differences between all 3clinical groups (p � .05, bonferroni corrected). Patients with ADscored significantly lower than patients with MCI, and the latter—lower than HCs (mean � SEM: 0.096 � 0.007[sec�1], 0.159 �0.011[sec�1], 0.252 � 0.013[sec�1], respectively; Figure 1A). Withrespect to the standard-orientation and MMSE scores, patients withAD scored significantly lower (7.12 � 0.43 and 22.70 � 0.73,respectively) than patients with MCI (9.60 � 0.15, 27.8 � 0.31,p’s�0.05, Bonferroni corrected, Figures 1B, F). However, the lattershowed comparable results to these of HC (10 � 0 and 29.40 � 0.19,p � .05; Figure 1B, F). ACE scores showed significant differencesbetween HC, MCI and AD (mean � SEM: 91.74 � 1.12, 80.37 �2.16, 60.85 � 3.4, respectively; p � .05, bonferroni corrected, Figure1C).

Ordinal cumulative logistic regression allowed us to classify ourresults on a single patient level. To quantify and compare the discrim-inative ability of the mental-orientation, standard-orientation, MMSEand ACE, we constructed a ROC curve for each classification, cal-culated the area under the curve (AUC), and determined classificationaccuracy relative to an optimal threshold. The mental-orientation taskwas significantly superior to standard-orientation, MMSE, and ACE,performing the HC-MCI distinction at 95% accuracy (AUC � 0.98,

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

692 PETERS-FOUNSHTEIN ET AL.

Figures 2A, C), and the MCI-AD distinction at 90% accuracy(AUC � 0.95, Figures 2B, D). ACE, MMSE and standard-orientationproduced 74%, 70% and 50% accuracy for the HC-MCI distinction(AUC � 0.86, 0.81, 0.65, respectively, Figure 2C), and 81%, 85%and 77.5% accuracy for the MCI-AD distinction (AUC � 0.89, 0.94,0.875, respectively, Figure 2D).

Concerning the mental-orientation task, variance inflation factorvalues were within acceptable range for all variables (Dormann et al.,2013; VIF �3). Permutation tests showed that the classificationsbased on the mental-orientation task are not compatible with randomclassification of AUCs (HC-MCI: p � .0001, MCI-AD: p � .0004).Finally, leave-1-out analysis revealed 84% success of classification.

Experiment 2: Mental-Orientation Activates AD-susceptible Brain Regions Unlike Standard-Orientation

To identify the brain basis for the increased sensitivity of themental-orientation test, we searched for brain regions with in-creased activity during performance of the mental-orientation taskover the standard-orientation test. This analysis revealed themental-orientation task to preferentially activate a set of brainregions including the precuneus, parieto-occipital sulcus, posterior

cingulate cortices, parahippocampus and hippocampus bilaterally.Compared to mental-orientation, standard-orientation was shownto preferentially activate only the right supramarginal gyrus (FDRcorrected, p � .05, cluster size �20 voxels; Figure 3).

In addition, group average of event-related activity sampledfrom independent experimental runs at the center of each clusterdemonstrated preferential recruitment of precuneus, parieto-occipital sulcus, posterior cingulate cortices, parahippocampus andhippocampus bilaterally by mental-orientation to be highly con-sistent (see Figure 3).

Finally, quantification of suprathreshold voxels (above lexical con-trol task) revealed significantly increased (ANOVA and Scheffé posthoc test, p � .05) overlap between clusters of activity evoked by themental-orientation over standard-orientation with DN regions (Figure4A, B), and AD-susceptible regions (Figure 4C, D).

Discussion

An examination of orientation in patients along the AD-spectrum showed the mental-orientation task to well discriminatebetween AD, MCI and HC patients on both the group and single-subject levels, unlike standard-orientation, MMSE or ACE. Con-

Figure 1. Behavioral results. Mean global and domain-specific mental-orientation, standard-orientation,MMSE and ACE scores were compared between patients with Alzheimer’s disease (AD), mild cognitiveimpairment (MCI) and healthy control (HC) subjects. Efficiency scores (ESs) for all mental-orientationdomains (A) as well as the ACE (C) showed significant differences between the three clinical groups.Looking at each domain separately, (D) time and space ES separated between HC and non-HC, while personES separated between AD and non-AD. MMSE, standard-orientation and its time and space subscores werenot able to distinguish between HC and MCI, but did distinguished between AD and non-AD groups for alldomains (B, E, F). � p � .05. See the online article for the color version of this figure.

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

693MENTAL-ORIENTATION IN ALZHEIMER’S DISEASE

trasting the brain activity underlying mental-orientation andstandard-orientation performance using fMRI revealed mental-orientation to preferentially recruit brain regions identified ashighly susceptible to AD pathology, including the precuneus,posterior cingulate cortex, parieto-occipital sulcus, parahippocam-pal gyrus and hippocampus, unlike the standard-orientation task,thus, providing a possible explanation to the mental-orientationincreased sensitivity. Finally, this test is made freely available forusers and clinicians.

Orientation is the bedrock of neurological and psychiatric ex-amination in the emergency room, outpatient clinic and the ward.Presently, it is tested as the patient’s knowledge about her self-location in space and time. With regard to orientation in person, itwas recently proposed that orientation should be tested with re-

spect to familiar other people, rather than knowledge about one’sown identity (Rapoport & Rapoport, 2015). In Experiment 1 weshowed that mental-orientation is very sensitive to cognitive de-cline if tested with respect to different self-related events, placesand people. Finally, mental-orientation is unique among mostneuropsychological tests and paradigms as it is evaluated onlyregarding personally familiar and significant events, places andpeople. This personalized character probably contributes to thetest’s diagnostic success, and makes it well suited for longitudinalmonitoring of mental status. Notably, although the primary goal ofthe current work was to test mental-orientation with respect to thestandard-orientation test, as is used in most clinical neuropsycho-logical tests, our results were superior to the complete MMSE andACE scores. Furthermore, although in our research, collection of

Figure 2. Machine-learning based analyses. (A, B) Logistic regression. Ordinal cumulative logistic regressionwas applied to predict clinical outcomes in the individual subject’s level. The probability for a specific outcomeof a specific patient was plotted as a function of the logit function of the weighted sum of predictor variables.Mental-orientation classification accuracy for the HC-MCI distinction was 95% (A) and 90% for the MCI-ADdistinction (B). The dashed line marks the optimal threshold, maximizing sensitivity and specificity (maximalYouden’s index) for HC-MCI and MCI-AD classification. (C, D) ROC curves. Comparison of the estimatedoutput to the actual output over a range of threshold values produced ROC curves, with the area under the curves(AUC) indicating overall test discriminability. ROC curves for HC-MCI distinction showed AUC of 0.98 for themental-orientation task, 0.86 for the ACE, 0.81 for the MMSE and 0.65 for the standard-orientation test (C).ROC curves for MCI-AD distinction showed AUC of 0.95 for the mental-orientation task, 0.89 for the ACE, 0.94for the MMSE and 0.875 for the standard-orientation test (D). See the online article for the color version of thisfigure.

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

694 PETERS-FOUNSHTEIN ET AL.

stimuli and task administration required 30–40 min, we havedeveloped an adaptable phone/tablet application capable of col-lecting stimuli autonomously, thereby reducing administrationtime to 10–15 min with minimal administrator intervention.

Despite considerable investigative efforts, the core disorderin AD remains ill-defined, both in the cognitive and the bio-logical levels. Cognitive criteria for AD diagnosis require im-pairment in two out of five potential domains (Dubois et al.,2014), whereas the biological mechanism linking beta amyloidand phosphorylated tau remains a matter for debate (Herrup,2015). Consequently, the diagnosis of AD presents a clinicalchallenge, especially in the disease’s early stages. Notably,

identification at preclinical stages is of much importance, aswithout early diagnosis the ability to develop and provideeffective treatment is highly compromised (Sperling et al.,2011). In the cognitive level, the recognition of spatial disori-entation as a hallmark of AD led to several notable attempts todesign navigational tasks intended for early diagnosis (Chan etal., 2016). Patients’ performance in episodic memory tasks andfuture imagination were also suggested as potential markers forAD-related degeneration (Addis, Sacchetti, Ally, Budson, &Schacter, 2009; Budson et al., 2007). The mental-orientationparadigm generalizes across these different approaches. Wepropose that the core of mental-orientation is disrupted in AD,

Figure 3. Mental-orientation and standard-orientation contrast. A contrast between mental-orientation andstandard-orientation across all domains (time, space, person), revealed mental-orientation to preferentiallyactivate the precuneus, parieto-occipital sulcus, posterior cingulate cortices, parahippocampus and hippocampusbilaterally (purple cluster). Standard-orientation was shown to preferentially activate the right supramarginalgyrus (yellow cluster; FDR corrected, p � .05, cluster size �20 voxels). Group average (n � 16) of event-relatedactivity in independent experimental runs demonstrated these effects to be highly consistent. Lines represent rest(gray), mental-orientation (purple) and standard-orientation (yellow) evoked activity. Error bars represent SEMbetween subjects. See the online article for the color version of this figure.T

his

docu

men

tis

copy

righ

ted

byth

eA

mer

ican

Psyc

holo

gica

lA

ssoc

iatio

nor

one

ofits

allie

dpu

blis

hers

.T

his

artic

leis

inte

nded

sole

lyfo

rth

epe

rson

alus

eof

the

indi

vidu

alus

eran

dis

not

tobe

diss

emin

ated

broa

dly.

695MENTAL-ORIENTATION IN ALZHEIMER’S DISEASE

manifesting in different domains of spatial navigation, episodicmemory, future imagination and thinking about other people.

AD progression is known to follow a temporo-parietal pattern ofdegradation (Braak & Braak, 1991). In recent years several inno-vative studies have successfully associated groups carrying ele-vated risk for AD (MCI, Apo-ε4, PSEN1, elderly) with divergentpatterns of activity and connectivity in temporo-parietal networkssupporting episodic memories, spatial orientation, and social pro-cessing. We turn to briefly discuss select exemplars in the contextof our results:

The disruption of episodic memory is considered among thepillars of AD diagnosis and was traditionally associated withhippocampal dysfunction (Small, Schobel, Buxton, Witter, &Barnes, 2011). More recently, functional imaging studies havesuggested that memory processes are supported by a coordinatedand reciprocal activity between hippocampal nodes and a set ofregions including posterior cingulate, lateral parietal, and medialprefrontal cortices (Vincent et al., 2006), also activated for mental-orientation in time. Applying fMRI to examine memory processingin the abovementioned groups, carrying elevated risk for AD,

revealed alteration in parietal-hippocampal activity (Sperling et al.,2010) and connectivity (Wang et al., 2006), that were shown toprecede neuronal loss and predict future conversion to AD.(O’Brien et al., 2010)

In addition to episodic memory impairments, spatial disori-entation marks the first stages of AD (Cushman et al., 2008).Spatial orientation is commonly addressed via two complemen-tary reference frames (Aguirre & D’Esposito, 1999): an object-to-subject (egocentric) reference frame and an object-to-object(allocentric) reference frame. A converging body of cognitive(Aguirre & D’Esposito, 1999), clinical (Cushman et al., 2008),electrophysiological (Ekstrom et al., 2003), and neuroimaging(Zhang et al., 2012) research has implicated hippocampal struc-tures in allocentric processing and the parietal cortex in ego-centric processing. Intermediary regions, aligned on a “parietal-hippocampal axis”, were suggested to support integration ofallocentric and egocentric information via “mental frame sync-ing” (Byrne, Becker, & Burgess, 2007). Recent works exploringspatial orientation in AD have reported both allocentric-hippocampal and egocentric-parietal hubs (Kunz et al., 2015;

Figure 4. Mental-orientation and standard-orientation overlap with the default network (DN) and AD-associated regions. (A) Group averages of the number of mental-and standard-orientation evoked suprathresholdvoxels (all domains combined, as well as separately for space, time, and person, each contrasted to the lexicalcontrol task: mental-orientation [mean � SEM]: 331 � 91,321 � 102, 211 � 64, 640 � 174; standard-orientation: 84 � 33, 22 � 12, 75 � 34, 223 � 106 for all domains, time, space and person respectively)overlapping with the DN (independently identified in each subject). (B) Group DN pattern of activity (includingvoxels active in individual DN maps in 8 or more of the subjects). (C) Group averages of the number ofmental-and-standard-orientation evoked suprathreshold voxels (all domains combined and separately for space,time, and person, each contrasted to the lexical control task: mental-orientation: 148 � 60, 159 � 36, 93 � 25,258 � 107; standard-orientation [mean � SEM]: 60 � 18, 30 � 13, 35 � 11, 137 � 53) a set of AD-associatedregions (entorhinal, parahippocampal, superior-temporal and temporal pole cortices as well as the amygdala andhippocampus). (D) Projection of the AD-associated regions volume of interest. � p � .05. See the online articlefor the color version of this figure.

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

696 PETERS-FOUNSHTEIN ET AL.

Vlcek & Laczó, 2014) to exhibit AD-related pathology. Neu-roimaging studies focusing on subjects at-risk for AD havesuggested a break in the “mental frame syncing” to cause earlyspatial disorientation (Serino & Riva, 2013). Our findings ofboth parietal and parahippocampal activations suggest themental-orientation task to rely on both egocentric and allocen-tric processing. Future research using fMRI in patients mayshed further light on the relations between these two frame-works supporting orientation.

With respect to disorientation to person, several studies havelinked AD and impaired identification of famous and personallyfamiliar people (Abe et al., 2007; Hodges & Greene, 1998).Neuroimaging studies have correlated these misidentificationphenotypes to patterns of atrophy and hypometabolism charac-teristic to AD (Ismail, Nguyen, Fischer, Schweizer, & Mulsant,2012; Mentis et al., 1995). Studies exploring the metrics of the“social space” have implicated hippocampal (Tavares et al.,2015) and DN regions (Peer et al., 2015) in representations ofsocial hierarchy and affiliation. Finally, epidemiological studieshave identified comorbidity between AD and delusional mis-identification syndromes (Ismail et al., 2012), and while theseare mostly regarded as confabulations, they may be also con-sidered in the context of disorientation to person (Peer et al.,2014).

This study has several limitations. In Experiment 1, patientswere diagnosed in a specialized dementia clinic, completing fullneuropsychological evaluation and undergoing longitudinalmonitoring. While these tests carry a limited predictive value,they are used in clinical trials and clinical practice. Addition-ally, adaptation of the task from the clinical setting to the fMRItheater in Experiment 2 entailed several changes compared tothe behavioral version used in Experiment 1, and thereforegeneralizing from one to the other should be done with somecaution. Finally, Experiment 2 demonstrated overlap betweenmental-orientation evoked activity in young healthy adultsand regions susceptible to AD pathology, unlike standard-orientation evoked activity. This finding is indicative, yet astudy in MCI/AD patients under fMRI is needed to confirm thatdisruption in orientation in patients across the AD-spectrum isdirectly related to disrupted activity in regions of mental-orientation activity. Future studies will extend the scope ofmental-orientation testing to include longitudinal measure-ments, functional neuroimaging in patients, and comparisonsto other neuroimaging modalities as well as to other demen-tias.

In conclusion, this study demonstrated the central role ofmental-orientation in AD. Mental-orientation selectively acti-vated brain regions prone to AD-related degeneration. Accord-ingly, a short personalized behavioral test was able to diagnoseAD much better than a lengthy and detailed, yet generic neu-ropsychological test. We speculate that dysregulation betweenDN (egocentric) and medial-temporal (allocentric) activitieslies at the core of a cascade of compensatory neuropathologicalprocesses, leading to AD. Future studies in larger cohorts ofpatients and under different neuroimaging modalities will so-lidify the role of orientation as a core cognitive deficit in ADand illuminate the molecular basis of orientation failure. This,in turn, is crucial for early diagnosis of AD, clinical compre-hension of the disease and, consequently, drug development.

References

Abe, N., Ishii, H., Fujii, T., Ueno, A., Lee, E., Ishioka, T., & Mori, E.(2007). Selective impairment in the retrieval of family relationships inperson identification: A case study of delusional misidentification. Neu-ropsychologia, 45, 2902–2909. http://dx.doi.org/10.1016/j.neuropsycho-logia.2007.06.003

Addis, D. R., Sacchetti, D. C., Ally, B. A., Budson, A. E., & Schacter, D. L.(2009). Episodic simulation of future events is impaired in mild Alzhei-mer’s disease. Neuropsychologia, 47, 2660–2671. http://dx.doi.org/10.1016/j.neuropsychologia.2009.05.018

Aguirre, G. K., & D’Esposito, M. (1999). Topographical disorientation: Asynthesis and taxonomy. Brain: A Journal of Neurology, 122, 1613–1628. http://dx.doi.org/10.1093/brain/122.9.1613

Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H.,Fox, N. C., . . . Phelps, C. H. (2011). The diagnosis of mild cognitiveimpairment due to Alzheimer’s disease: Recommendations from theNational Institute on Aging-Alzheimer’s Association workgroups ondiagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia,7, 270–279. http://dx.doi.org/10.1016/j.jalz.2011.03.008

Ananth, C. V., & Kleinbaum, D. G. (1997). Regression models for ordinalresponses: A review of methods and applications. International Journalof Epidemiology, 26, 1323–1333. http://dx.doi.org/10.1093/ije/26.6.1323

Arzy, S., Adi-Japha, E., & Blanke, O. (2009). The mental time line: Ananalogue of the mental number line in the mapping of life events.Consciousness and Cognition, 18, 781–785. http://dx.doi.org/10.1016/j.concog.2009.05.007

Berrios, G. E. (1982). Disorientation states and psychiatry. ComprehensivePsychiatry, 23, 479 – 491. http://dx.doi.org/10.1016/0010-440X(82)90161-4

Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82, 239–259. http://dx.doi.org/10.1007/BF00308809

Bruen, P. D., McGeown, W. J., Shanks, M. F., & Venneri, A. (2008).Neuroanatomical correlates of neuropsychiatric symptoms in Alzhei-mer’s disease. Brain: A Journal of Neurology, 131, 2455–2463. http://dx.doi.org/10.1093/brain/awn151

Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). Thebrain’s default network: Anatomy, function, and relevance to disease.Annals of the New York Academy of Sciences, 1124, 1–38. http://dx.doi.org/10.1196/annals.1440.011

Buckner, R. L., Snyder, A. Z., Shannon, B. J., LaRossa, G., Sachs, R.,Fotenos, A. F., . . . Mintun, M. A. (2005). Molecular, structural, andfunctional characterization of Alzheimer’s disease: Evidence for a rela-tionship between default activity, amyloid, and memory. The Journal ofNeuroscience, 25, 7709–7717. http://dx.doi.org/10.1523/JNEUROSCI.2177-05.2005

Budson, A. E., Simons, J. S., Waring, J. D., Sullivan, A. L., Hussoin, T.,& Schacter, D. L. (2007). Memory for the September 11, 2001, terroristattacks one year later in patients with Alzheimer’s disease, patients withmild cognitive impairment, and healthy older adults. Cortex, 43, 875–888. http://dx.doi.org/10.1016/S0010-9452(08)70687-7

Byrne, P., Becker, S., & Burgess, N. (2007). Remembering the past andimagining the future: A neural model of spatial memory and imagery.Psychological Review, 114, 340–375. http://dx.doi.org/10.1037/0033-295X.114.2.340

Chan, D., Gallaher, L. M., Moodley, K., Minati, L., Burgess, N., & Hartley,T. (2016). The 4 Mountains Test: A Short Test of Spatial Memory withHigh Sensitivity for the Diagnosis of Pre-dementia Alzheimer’s Disease.Journal of Visualized Experiments, 116, 1–11.

Cushman, L. A., Stein, K., & Duffy, C. J. (2008). Detecting navigationaldeficits in cognitive aging and Alzheimer disease using virtual reality.Neurology, 71, 888–895. http://dx.doi.org/10.1212/01.wnl.0000326262.67613.fe

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

697MENTAL-ORIENTATION IN ALZHEIMER’S DISEASE

delpolyi, A. R., Rankin, K. P., Mucke, L., Miller, B. L., & Gorno-Tempini,M. L. (2007). Spatial cognition and the human navigation network in ADand MCI. Neurology, 69, 986–997. http://dx.doi.org/10.1212/01.wnl.0000271376.19515.c6

Desikan, R. S., Cabral, H. J., Hess, C. P., Dillon, W. P., Glastonbury,C. M., Weiner, M. W., . . . the Alzheimer’s Disease NeuroimagingInitiative. (2009). Automated MRI measures identify individuals withmild cognitive impairment and Alzheimer’s disease. Brain: A Journal ofNeurology, 132, 2048–2057. http://dx.doi.org/10.1093/brain/awp123

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., . . .Lautenbach, S. (2013). Collinearity: A review of methods to deal with itand a simulation study evaluating their performance. Ecography, 36,27–46.

Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L.,Blennow, K., . . . Cummings, J. L. (2014). Advancing research diag-nostic criteria for Alzheimer’s disease: The IWG-2 criteria. The LancetNeurology, 13, 614 – 629. http://dx.doi.org/10.1016/S1474-4422(14)70090-0

Dubois, B., Slachevsky, A., Litvan, I., & Pillon, B. (2000). The FAB: AFrontal Assessment Battery at bedside. Neurology, 55, 1621–1626.http://dx.doi.org/10.1212/WNL.55.11.1621

Ekstrom, A. D., Kahana, M. J., Caplan, J. B., Fields, T. A., Isham, E. A.,Newman, E. L., & Fried, I. (2003). Cellular networks underlying humanspatial navigation. Nature, 425, 184–188. http://dx.doi.org/10.1038/nature01964

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mentalstate.” A practical method for grading the cognitive state of patients forthe clinician. Journal of Psychiatric Research, 12, 189–198. http://dx.doi.org/10.1016/0022-3956(75)90026-6

Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C.,Frackowiak, R. S., & Turner, R. (1995). Analysis of fMRI time-seriesrevisited. NeuroImage, 2, 45–53. http://dx.doi.org/10.1006/nimg.1995.1007

Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich,K., . . . the International Psychogeriatric Association Expert Conferenceon mild cognitive impairment. (2006). Mild cognitive impairment. TheLancet, 367, 1262–1270. http://dx.doi.org/10.1016/S0140-6736(06)68542-5

Good, P. (1994). Permutation Tests: A Practical Guide to ResamplingMethods for Testing Hypotheses. New York, NY: Springer. http://dx.doi.org/10.1007/978-1-4757-2346-5

Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the areaunder a receiver operating characteristic (ROC) curve. Radiology, 143,29–36. http://dx.doi.org/10.1148/radiology.143.1.7063747

Herrup, K. (2015). The case for rejecting the amyloid cascade hypothesis.Nature Neuroscience, 18, 794–799. http://dx.doi.org/10.1038/nn.4017

Hodges, J. R., & Greene, J. D. (1998). Knowing about people and namingthem: Can Alzheimer’s disease patients do one without the other? TheQuarterly Journal of Experimental Psychology A: Human ExperimentalPsychology, 51, 121–134. http://dx.doi.org/10.1080/713755753

Ismail, Z., Nguyen, M. Q., Fischer, C. E., Schweizer, T. A., & Mulsant,B. H. (2012). Neuroimaging of delusions in Alzheimer’s disease. Psy-chiatry Research: Neuroimaging, 202, 89–95. http://dx.doi.org/10.1016/j.pscychresns.2012.01.008

Kunz, L., Schröder, T. N., Lee, H., Montag, C., Lachmann, B., Sariyska,R., . . . Axmacher, N. (2015). Reduced grid-cell-like representations inadults at genetic risk for Alzheimer’s disease. Science, 350, 430–433.http://dx.doi.org/10.1126/science.aac8128

Mathuranath, P. S., Nestor, P. J., Berrios, G. E., Rakowicz, W., & Hodges,J. R. (2000). A brief cognitive test battery to differentiate Alzheimer’sdisease and frontotemporal dementia. Neurology, 55, 1613–1620.

McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack,C. R., Jr., Kawas, C. H., . . . Phelps, C. H. (2011). The diagnosis ofdementia due to Alzheimer’s disease: Recommendations from the Na-

tional Institute on Aging-Alzheimer’s Association workgroups on diag-nostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7,263–269. http://dx.doi.org/10.1016/j.jalz.2011.03.005

Mentis, M. J., Weinstein, E. A., Horwitz, B., McIntosh, A. R., Pietrini, P.,Alexander, G. E., . . . Murphy, D. G. M. (1995). Abnormal brain glucosemetabolism in the delusional misidentification syndromes: A positronemission tomography study in Alzheimer disease. Biological Psychiatry,38, 438–449. http://dx.doi.org/10.1016/0006-3223(94)00326-X

O’Brien, J. L., O’Keefe, K. M., LaViolette, P. S., DeLuca, A. N., Blacker,D., Dickerson, B. C., & Sperling, R. A. (2010). Longitudinal fMRI inelderly reveals loss of hippocampal activation with clinical decline.Neurology, 74, 1969 –1976. http://dx.doi.org/10.1212/WNL.0b013e3181e3966e

Peer, M., Lyon, R., & Arzy, S. (2014). Orientation and disorientation:Lessons from patients with epilepsy. Epilepsy & Behavior, 41, 149–157.http://dx.doi.org/10.1016/j.yebeh.2014.09.055

Peer, M., Salomon, R., Goldberg, I., Blanke, O., & Arzy, S. (2015). Brainsystem for mental orientation in space, time, and person. Proceedings ofthe National Academy of Sciences of the United States of America, 112,11072–11077. http://dx.doi.org/10.1073/pnas.1504242112

Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G.,& Kokmen, E. (1999). Mild cognitive impairment: Clinical character-ization and outcome. Archives of Neurology, 56, 303–308. http://dx.doi.org/10.1001/archneur.56.3.303

Piolino, P., Desgranges, B., Belliard, S., Matuszewski, V., Lalevée, C., Dela Sayette, V., & Eustache, F. (2003). Autobiographical memory andautonoetic consciousness: Triple dissociation in neurodegenerative dis-eases. Brain: A Journal of Neurology, 126, 2203–2219. http://dx.doi.org/10.1093/brain/awg222

Rapoport, B. I., & Rapoport, S. (2015). Orientation to person, orientationto self. Neurology, 85, 2072–2074. http://dx.doi.org/10.1212/WNL.0000000000002188

Schnider, A., von Däniken, C., & Gutbrod, K. (1996). Disorientation inamnesia. A confusion of memory traces. Brain: A Journal of Neurology,119, 1627–1632. http://dx.doi.org/10.1093/brain/119.5.1627

Serino, S., & Riva, G. (2013). Getting lost in Alzheimer’s disease: A breakin the mental frame syncing. Medical Hypotheses, 80, 416–421. http://dx.doi.org/10.1016/j.mehy.2012.12.031

Small, S. A., Schobel, S. A., Buxton, R. B., Witter, M. P., & Barnes, C. A.(2011). A pathophysiological framework of hippocampal dysfunction inageing and disease. Nature Reviews Neuroscience, 12, 585–601. http://dx.doi.org/10.1038/nrn3085

Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S.,Fagan, A. M., . . . Phelps, C. H. (2011). Toward defining the preclinicalstages of Alzheimer’s disease: Recommendations from the NationalInstitute on Aging-Alzheimer’s Association workgroups on diagnosticguidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7, 280–292. http://dx.doi.org/10.1016/j.jalz.2011.03.003

Sperling, R. A., Dickerson, B. C., Pihlajamaki, M., Vannini, P., LaViolette,P. S., Vitolo, O. V., . . . Johnson, K. A. (2010). Functional alterations inmemory networks in early Alzheimer’s disease. NeuroMolecular Med-icine, 12, 27–43. http://dx.doi.org/10.1007/s12017-009-8109-7

Svoboda, E., McKinnon, M. C., & Levine, B. (2006). The functionalneuroanatomy of autobiographical memory: A meta-analysis. Neuropsy-chologia, 44, 2189–2208. http://dx.doi.org/10.1016/j.neuropsychologia.2006.05.023

Tavares, R. M., Mendelsohn, A., Grossman, Y., Williams, C. H., Shapiro,M., Trope, Y., & Schiller, D. (2015). A Map for Social Navigation in theHuman Brain. Neuron, 87, 231–243. http://dx.doi.org/10.1016/j.neuron.2015.06.011

Townsend, J. T., & Ashby, F. G. (1983). The stochastic modeling ofelementary psychological processes. Cambridge, UK: Cambridge Uni-versity Press.

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

698 PETERS-FOUNSHTEIN ET AL.

Vincent, J. L., Snyder, A. Z., Fox, M. D., Shannon, B. J., Andrews, J. R.,Raichle, M. E., & Buckner, R. L. (2006). Coherent spontaneous activityidentifies a hippocampal-parietal memory network. Journal of Neuro-physiology, 96, 3517–3531. http://dx.doi.org/10.1152/jn.00048.2006

Vlcek, K., & Laczó, J. (2014). Neural correlates of spatial navigationchanges in mild cognitive impairment and Alzheimer’s disease. Fron-tiers in Behavioral Neuroscience, 8, 89.

Wang, L., Zang, Y., He, Y., Liang, M., Zhang, X., Tian, L., . . . Li, K.(2006). Changes in hippocampal connectivity in the early stages ofAlzheimer’s disease: Evidence from resting state fMRI. NeuroImage,31, 496–504. http://dx.doi.org/10.1016/j.neuroimage.2005.12.033

Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3, 32–35.http://dx.doi.org/10.1002/1097-0142(1950)3:1�32::AID-CNCR2820030106�3.0.CO;2-3

Zhang, H., Copara, M., & Ekstrom, A. D. (2012). Differential recruitmentof brain networks following route and cartographic map learning ofspatial environments. PLoS ONE, 7(9), e44886. http://dx.doi.org/10.1371/journal.pone.0044886

Zhou, J., Greicius, M. D., Gennatas, E. D., Growdon, M. E., Jang, J. Y.,Rabinovici, G. D., . . . Seeley, W. W. (2010). Divergent networkconnectivity changes in behavioural variant frontotemporal dementiaand Alzheimer’s disease. Brain: A Journal of Neurology, 133, 1352–1367. http://dx.doi.org/10.1093/brain/awq075

Received September 11, 2017Revision received December 17, 2017

Accepted January 7, 2018 �

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

699MENTAL-ORIENTATION IN ALZHEIMER’S DISEASE