r Human Brain Mapping 35:1562–1573 (2014) r

Diffusion Weighted Imaging of Prefrontal Cortex inProdromal Huntington’s Disease

Joy T. Matsui,1,2 Jatin G. Vaidya,1 Hans J. Johnson,1 Vincent A. Magnotta,1,3

Jeffrey D. Long,1 James A. Mills,1 Mark J. Lowe,4 Ken E. Sakaie,4

Stephen M. Rao,5 Megan M. Smith,1 and Jane S. Paulsen1*

1Department of Psychiatry, The University of Iowa, Iowa City, Iowa2John A. Burns School of Medicine, The University of Hawaii, Honolulu, Hawaii

3Department of Radiology, The University of Iowa, Iowa City, Iowa4Imaging Institute, Cleveland Clinic, Cleveland, Ohio

5Neurological Institute, Cleveland Clinic, Cleveland, Ohio

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Abstract: Huntington’s disease (HD) is a devastating neurodegenerative disease with no effective dis-ease-modifying treatments. There is considerable interest in finding reliable indicators of disease pro-gression to judge the efficacy of novel treatments that slow or stop disease onset before debilitatingsigns appear. Diffusion-weighted imaging (DWI) may provide a reliable marker of disease progressionby characterizing diffusivity changes in white matter (WM) in individuals with prodromal HD. Theprefrontal cortex (PFC) may play a role in HD progression due to its prominent striatal connectionsand documented role in executive function. This study uses DWI to characterize diffusivity in specificregions of PFC WM defined by FreeSurfer in 53 prodromal HD participants and 34 controls. Prodro-mal HD individuals were separated into three CAG-Age Product (CAP) groups (16 low, 22 medium,15 high) that indexed baseline progression. Statistically significant increases in mean diffusivity (MD)and radial diffusivity (RD) among CAP groups relative to controls were seen in inferior and lateralPFC regions. For MD and RD, differences among controls and HD participants tracked with baselinedisease progression. The smallest difference was for the low group and the largest for the high group.Significant correlations between Trail Making Test B (TMTB) and mean fractional anisotropy (FA)and/or RD paralleled group differences in mean MD and/or RD in several right hemisphere regions.The gradient of effects that tracked with CAP group suggests DWI may provide markers of diseaseprogression in future longitudinal studies as increasing diffusivity abnormalities in the lateral PFC ofprodromal HD individuals. Hum Brain Mapp 35:1562–1573, 2014. VC 2013 Wiley Periodicals, Inc.

Key words: executive function; diffusion tensor imaging; frontal lobe; trail making test

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Additional Supporting Information may be found in the onlineversion of this article.

Contract grant sponsor: National Institutes for Health Cognitiveand Functional Brain Changes in Preclinical Huntington’s Disease(HD) (HD-fMRI); Contract grant number: NS054893; Contractgrant sponsor: Neurobiological Predictors of Huntington’s Disease(PREDICT-HD); Contract grant number: NS40068; Contract grantsponsor: National Alliance for Medical Image Computing(NAMIC); Contract grant number: EB005149-07; Contract grantsponsor: National Institute of Neurological Disorders and Stroke;

Contract grant number: NS040068; Contract grant sponsor: CHDIFoundation, Inc.; Contract grant number: A3917.

*Correspondence to: Jane S. Paulsen, The University of Iowa,Roy J. and Lucille A. Carver College of Medicine, 1-305 MedicalEducation Building, Iowa City, IA 52242-1000, USA.E-mail: predict-publications@uiowa.edu

Received for publication 6 September 2012; Revised 9 November2012; Accepted 28 January 2013

DOI: 10.1002/hbm.22273Published online 9 April 2013 in Wiley Online Library(wileyonlinelibrary.com).

VC 2013 Wiley Periodicals, Inc.

INTRODUCTION

Huntington’s disease (HD) is an autosomal-dominant,progressive disorder characterized by motor, cognitive,and behavioral disturbances. Age of diagnosis variesinversely with the expanded number of polyglutamine(cytosine-adenine-guanine or CAG) repeats in the hunting-tin gene [Huntington’s Disease Collaborative ResearchGroup, 1993]. Manifest motor onset usually occurs in mid-life, with a duration of 15 to 20 years after diagnosis[Harper, 1991; Hayden, 1981]. Currently, HD treatmentsonly target symptoms with no pharmacologic solutionsfor slowing or stopping disease progression [Frank andJankovic, 2010]. To hasten development of novel treat-ments that target disease progression, a number of investi-gators, including the PREDICT-HD group, are focusing onidentifying biomarkers to judge efficacy of new treatments[Paulsen et al., 2006a, 2008].

In the search for a reliable disease marker, volumetricstudies using magnetic resonance imaging (MRI) haverevealed abnormal brain tissue volumes in prodromal HD(or before symptom onset) patients. Volumetric studies firstexamined symptomatic HD patients and found prominentatrophy of the caudate and putamen [Jernigan et al., 1991].HD individuals who had the CAG expansion in the HDgene but no signs/symptoms to warrant a clinical diagnosis(i.e., in the prodrome of HD) demonstrated similar findingsof reduced basal ganglia volumes in comparison to healthycontrols. Degree of striatal atrophy in prodromal HD indi-viduals also correlated with greater neurological impair-ment [Campodonico et al., 1998; Harris et al., 1999], poorerperformance on cognitive assessments [Campodonico et al.,1998], and years to motor sign/symptom onset [Aylwardet al., 1996; Harris et al., 1999]. As for white matter (WM)specifically, cognitive deficits have shown to correlate morewith cerebral WM atrophy than striatal atrophy in sympto-matic HD individuals [Beglinger et al., 2005]. A significantdecrease in total WM volume has also been seen inprodromal HD individuals classified more than 15 yearsfrom diagnosis [Paulsen et al., 2006b, 2010] and a dispropor-tionately greater loss of total frontal lobe WM than overallbrain volume reductions [Aylward et al., 1998].

WM volume has been shown to correlate with features ofdisease progression, but volume information alone does notreflect altered white matter integrity. Researchers have thusturned to diffusion-weighted imaging (DWI) to detect vary-ing levels of anisotropic diffusion that could representaltered WM integrity in diseased tissue [Basser, 1995;Basser and Pierpaoli, 1996; Jones et al., 2012]. A tensor rep-resentation is often used to model the diffusion process ateach voxel. Rotationally invariant scalars are generatedfrom the resulting eigenvalue decomposition to describethe diffusion anisotropy and magnitude [Basser, 1995;Basser and Pierpaoli, 1996]. Four scalars often used include:fractional anisotropy (FA), mean diffusivity (MD, units ¼mm2/sec), axial diffusivity (AD, units ¼ mm2/sec), and ra-dial diffusivity (RD, units ¼ mm2/sec). FA reflects anisot-

ropy of the diffusion tensor and is dimensionless, rangingfrom 0 (isotropic diffusion) to 1 (high anisotropy) [Basserand Pierpaoli, 1996]. MD is the average diffusion magni-tude along three principal directions into which diffusion isdecomposed [Basser and Pierpaoli, 1996]. AD is the magni-tude of diffusion parallel to the principal direction of diffu-sion, where changes correlate with axonal injury [Songet al., 2003]. Radial diffusivity (RD) is the magnitude of dif-fusion perpendicular to the principal direction of diffusion,where increases correlate with incomplete myelination[Song et al., 2002] and myelin injury [Song et al., 2003,2005]. Scalar measures have been used to examine normal-appearing WM that contains abnormalities (i.e. multiplesclerosis) [e.g. Pagani et al., 2005] and developmental stud-ies to characterize changes associated with aging [e.g. Bucuret al., 2008; Dubois et al., 2008].

As for DWI studies involving HD participants, manyhave focused on WM of the motor loop [Bohanna et al.,2011; Della Nave et al., 2010; Rosas et al., 2006; Stofferset al., 2010], periventricular region [Mascalchi et al., 2004],corpus callosum [Bohanna et al., 2011; Della Nave et al.,2010; Di Paola et al., 2012; Dumas et al., 2012; Mulleret al., 2011; Rosas et al., 2006, 2010; Sritharan et al., 2010;Weaver et al., 2009], corona radiata [Bohanna et al., 2011;Della Nave et al., 2010; Stoffers et al., 2010; Weaver et al.,2009], and whole brain [Mascalchi et al., 2004; Rosas et al.,2006]. Overall, scalar studies on prodromal and sympto-matic HD participants demonstrate diffusivity changes inwhite matter to explain increased motor signs with diseaseprogression.

Another region of interest in HD disease progression isthe prefrontal cortex (PFC) due to its prominent striatalconnections. The dorsolateral PFC projects to the central todorsal caudate (dorsal loop), while the orbital PFC androstral anterior cingulate cortex projects to the ventrome-dial caudate and ventral striatum (ventral loop)[Alexander et al., 1986; Arikuni and Kubota, 1986]. Basedon the striatal dorsal-to-ventral progression of cell death[Hedreen and Folstein, 1995], Lawrence et al. hypothesizedthat functions associated with the dorsal PFC-striatal loopmay be impaired before motor sign/symptom onset, fol-lowed by impairment of functions associated with the ven-tral loop as neuronal loss increases [Lawrence et al., 1998].Despite possibly containing valuable information on dis-ease progression, PFC diffusivity in HD individuals hasyet to be explored in great detail. Most diffusion tensorscalar studies in the frontal lobe report findings in voxelclusters in regions of frontal lobe WM [Magnotta et al.,2009; Reading et al., 2005; Rosas et al., 2006] or associatedmajor WM bundles [Della Nave et al., 2010]. However, dif-fusion properties of voxel clusters may not be representa-tive of the larger surrounding region. The only study thathas examined diffusivity in an entire sub-region of PFCWM did so in the superior frontal cortex [Dumas et al.,2012]. Dumas et al. found decreased mean FA andincreased mean MD in WM fibers running through thesuperior frontal cortex in early HD participants [Dumas

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et al., 2012]. These diffusion findings were supported bythe average MD in the WM fibers running through thesuperior frontal cortex negatively correlating with Stroopword reading task performance [Dumas et al., 2012], a testthat is sensitive to cognitive deficits in prodromal HD par-ticipants [Stout et al., 2011].

This study strives to build upon past prodromal HDstudies by examining WM diffusivity in sub-regions of thePFC. It was hypothesized that diffusivity differences wouldbe seen in PFC WM regions among CAP groups relative tocontrols. It was also hypothesized that the difference rela-tive to controls would be a function of CAP group with thehigh group showing the greatest difference.

METHODS

Participants

This analysis used structural images, diffusion-weightedimages, and clinical data from the first time point of a largerlongitudinal functional MRI study, ‘‘Cognitive and Func-tional Brain Changes in Preclinical Huntington’s Disease’’(HD-fMRI; NS054893: P.I. J.S. Paulsen). This was a two-sitecollaboration whose goal is to utilize neurobiological andclinical markers to understand the progression of HD beforediagnosis and to provide candidate disease markers toassist future preventive HD clinical trials. Consent wasobtained in accordance with the Institutional Review Boardat each site. Controls were participants from HD familiesbut who were free of the CAG-expansion (i.e., CAG � 35).Thirty-four healthy controls (11 male/23 female, mean age49.1, SD ¼ 10.4) and 53 prodromal CAG-expanded individ-uals were recruited from the HD Registries at theUniversity of Iowa and the Cleveland Clinic. ProdromalCAG-expanded individuals were stratified into low (n ¼ 16;CAP < 287.16), medium (n ¼ 22; 287.16 < CAP < 367.12),and high (n ¼ 15; CAP > 367.12) groups based on theirCAG-Age Product or CAP designation, as previouslydescribed [Zhang et al., 2011]. CAP groups are used toreflect the individual’s progression through the disease pro-cess, from presymptomatic through manifest HD, based onCAG and age. It is meant to encompass terms such as ‘‘dis-ease burden’’ and ‘‘genetic burden’’ that have been used inprevious literature. The formula for CAP is as follows:

CAP ¼ Age0 � ðCAG� 33:6600Þ

where Age0 represents age of the participant at the time ofscan for this study (i.e., baseline) [Zhang et al., 2011].

Measures

Participants were evaluated by clinicians experienced inthe administration of the Unified Huntington’s DiseaseRating Scale (UHDRS) and certified by the HuntingtonStudy Group (HSG). Formal diagnosis of HD was based

on the Diagnostic Confidence Level rating of four indicat-ing the examining clinician felt the participant showed‘‘unequivocal presence of an otherwise unexplainedextrapyramidal movement disorder’’ with �99% confi-dence [Huntington Study Group, 1996]. Participants with arating of DCL ¼ 4 were excluded to restrict this particularanalysis to prodromal HD subjects. The sum of all theindividual motor ratings from the UHDRS (total motorimpairment score) is reported as well [Huntington StudyGroup, 1996]. Several cognitive measures were assessedalongside the imaging measures and included the SymbolDigit Modalities Test (SDMT), the Stroop Color Word Test,and the Trail Making Test (TMT). The SDMT measurespsychomotor speed and working memory by counting thenumber of correct matches between numbers to their des-ignated symbol based on a key [Smith, 1991]. The StroopColor Word Test measures processing speed and executivefunctions by counting the number of correct responses inthree conditions: color-naming (name colors), word-read-ing (read color names), and interference (inhibition ofdominant reading response while naming color) [Stroop,1935]. The TMT measures psychomotor speed and execu-tive function by recording the time it takes participants toconnect numbers alone (TMT Part A, TMTA) and connectalternating numbers and letters (TMT Part B, TMTB) bothin ascending order [Reitan, 1958]. A greater time requiredto complete the TMT results in a higher score, which indi-cates worse performance or poorer function. A summaryof participant characteristics is provided in Table I.

Imaging

Imaging data were collected at two large medical researchuniversities (University of Iowa and Cleveland Clinic). Bothsites used a Siemens 3T TIM Trio scanner. Structural imag-ing consisted of T1- and T2-weighted images both collectedin the coronal plane. T1-weighted images had the followingparameters: TI ¼ 900 ms, TE ¼ 3.09 ms TR ¼ 2,530 ms, flipangle ¼ 10�, NEX ¼ 1, bandwidth ¼ 220 Hz/pixel, FOV ¼256 � 256 � 220 mm, matrix ¼ 256 � 256 � 220. T2-weigh-ted images had the following range of parameters: TE �440 ms, TR ¼ 4800 ms, bandwidth ¼ 590 Hz/pixel, FOV ¼220 � 256 � 224 mm, matrix ¼ 214 � 256 � 160 mm. A dif-fusion-weighted sequence (71 noncollinear diffusion-weight-ing gradients with diffusion-weighting of b ¼ 1,000 sec/mm2 and eight b ¼ 0 sec/mm2 acquisitions, 256 � 256 mmFOV, 128 � 128 matrix, 50 2-mm thick axial slices with zerogap, TE ¼ 92 ms, TR ¼ 7,700 ms (CCF) or 8,000 ms (Iowa),and bandwidth ¼ 1,562 Hz/pixel (CCF) or 1,565 Hz/pixel(Iowa)) was acquired three times. All scans were transferredto The University of Iowa for processing and analysis.

Structural Image Preprocessing

Structural image preprocessing was performed using aderivative of the fully-automated BRAINS (Brain Research:

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Analysis of Image, Networks, and Systems) AutoWorkupsoftware package [Pierson et al., 2011]. T1- and T2-weightedimages for each subject were anterior commissure (AC)-posterior commissure (PC) aligned. The AC-PC-alignedimages were then bias-field corrected using an atlas-basedclassification algorithm. The preprocessed T1-weightedimages were used in FreeSurfer (version 5.1.0) image analy-sis suite (documented and freely available for downloadonline at http://surfer.nmr.mgh.harvard.edu/) for volumet-ric segmentation of the cortical WM regions. An illustrationof the WM labels used in this study is provided in Figure 1.

Diffusion-Weighted Image Preprocessing

Each DWI scan was visually inspected individually toidentify artifacts. Repeat DWI scans from the same subjectfrom a single scan session were concatenated (resulting in3� redundancy of each gradient directions) before qualitycontrol checking with DTIPrep [Liu et al., 2010]. DTIPrepperforms several quality assurance steps and removes vol-umes within a scan that do not meet its minimal quality cri-teria. DTIPrep first checked the diffusion imagingparameters to ensure the diffusion-weighted gradients werein the directions as expected, along with image size andspacing. Intensity artifacts were then detected by comparingnormalized correlation values of corresponding neighboringslices across all volumes within a scan. If a pair of slices pos-sessed a normalized correlation value outside the desig-nated number of standard deviations from the averagenormalized correlation value, the volume containing thoseslices was removed. Interlace artifacts were detected in asimilar manner where a single normalized correlation valuewas calculated between interleaving slices for each volume.Motion between multiple baseline volumes was removed byrigidly registering and signal-to-noise ratio was maximizedby averaging the registered baselines. The averaged baselineserved as a reference for eddy-current and head motion arti-fact correction through estimation of affine transformsbetween each diffusion-weighted image and the averagedbaseline. Directions of diffusion weighting were updated

based on the rotational component of the affine transforma-tion. The final quality assurance step removed any volumesthat possessed residual motion or translation relative to theaveraged baseline. The final dataset contained an averagedbaseline image and only those diffusion-weighted imagesthat passed all quality assurance tests [Liu et al., 2010].

Imaging Variables in Regions of Interest

The output files from DTIPrep were used to estimate thetensor images, and subsequently the fractional anisotropy(FA), mean diffusivity (MD), axial diffusivity (AD), and ra-dial diffusivity (RD) images were computed from the tensorimages using components of the GTRACT software [Chenget al., 2006]. A visual inspection of all FreeSurfer labelsrevealed that voxels posterior to the caudate were oftenincluded in the segmentation of both left and right medialorbitofrontal WM regions. To create a consistent regionaldefinition, the medial orbitofrontal WM segmentation foreach subject was edited by removing all voxels posterior tothe centroid of the ipsilateral caudate. The FreeSurfer WMlabels and brain masks were both resampled into DWIspace using a B-Spline transformation from the T2-weighted image to the averaged b0 baseline image fromthe output DTIPrep file and visual inspections of registra-tion quality were performed. The resampled FreeSurferWM labels were used to obtain measurements of volumeand mean rotationally invariant scalar measures. The ratioof label volume to intracranial volume will be referred toas the WM volume throughout the remainder of this manu-script. Mean FA, MD, AD, and RD values were computedin FreeSurfer WM labels intersected with the thresholdedFA binary mask (subject’s FA image containing FA valuesabove 0.1) using components of SimpleITK (http://www.itk.org/Wiki/ITK/Release_4/SimpleITK).

Statistical Analysis

Statistical analyses were performed using general linearmodels (GLM) with PROC GLM in SAS 9.2. For each

TABLE I. Summary of demographic and clinical data for study participants

Controls (mean; SD (N)) Low (mean; SD (N)) Medium (mean; SD (N)) High (mean; SD (N))

Age 49.1; 10.4 (34) 32.1; 8.8 (16) 39.4; 10.8 (22) 47.8; 12.2 (15)Education (yr) 15.6; 2.0 (34) 14.8; 2.5 (16) 15.0; 2.2 (22) 13.7; 2.7 (15)Gender 11M/23F (34) 3M/13F (16) 6M/16F (22) 2M/13F (15)UHDRS Total Motor 4.7; 3.3 (32) 3.7; 2.7 (16) 7.1; 9.3 (22) 15.2; 10.5 (14)SDMT 54.0; 11.0 (31) 55.1; 9.8 (15) 54.7; 12.5 (21) 46.2; 9.7 (15)Stroop color 84.4; 11.7 (31) 84.1; 11.1 (15) 83.4; 13.8 (20) 64.8; 15.6 (14)Stroop word 106.1; 18.1 (31) 106.9; 13.5 (15) 99.3; 18.3 (20) 77.6; 19.2 (14)Stroop interference 49.2; 9.4 (31) 52.4; 13.4 (15) 50.5; 12.8 (20) 36.5; 10.9 (14)TMTA 22.1; 5.6 (30) 20.9; 6.5 (15) 21.5; 7.5 (21) 27.6; 9.1 (15)TMTB 55.2; 24.4 (30) 46.9; 16.6 (15) 52.4; 24.7 (21) 78.6; 38.3 (14)

UHDRS Total Motor Score ¼ sum of all items of the Motor Assessment scale; SDMT ¼ Symbol Digit Modalities Test; TMTA ¼ TrailMaking Test A; TMTB ¼ Trail Making Test B.

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Figure 1.

(A) WM labels generated by FreeSurfer on T1-weighted images

shown in sagittal and axial views. Radiologic convention is used for

the axial view (left is right, right is left). Each color represents the

same region in both hemispheres: caudal middle frontal (white),

frontal pole (dark brown), lateral orbitofrontal (light brown),

medial orbitofrontal (red), pars opercularis (pink), pars orbitalis

(yellow), pars triangularis (green), rostral middle frontal (blue),

superior frontal (purple). (B) Significant CAP group differences in

mean diffusivity (MD) in the left rostral middle frontal and right lat-

eral orbitofrontal regions in comparison to controls (top). Signifi-

cant CAP group differences in radial diffusivity (RD) in the left

lateral orbitofrontal, left pars opercularis, left pars triangularis, left

rostral middle frontal, right lateral orbitofrontal, right pars opercu-

laris, right pars orbitalis, and right pars triangularis regions in com-

parison to controls (bottom). *P < 0.05, **P < 0.01, ***P <0.005, ****P < 0.001, *****P < 0.0005.

FreeSurfer defined PFC region, differences in WM volumeand mean FA, mean MD, mean AD, and mean RD amonggroups determined by CAP designation were investigatedusing analysis of covariance models with age, years of edu-cation, gender, and site of data collection as covariates. Par-tial Pearson correlations were computed between regionalWM volume, mean FA, mean MD, mean AD, and mean RDand SDMT, Stroop Word, Stroop Color, Stroop Interference,TMTA, and TMTB scores for prodromal HD subjects onlywith age, years of education, gender, and site of data collec-tion as covariates. In the GLM and correlation analyses, afalse-discovery rate (FDR) correction was performed toadjust for multiple comparisons across ROIs using the pro-cedures of Benjamini and Hochberg [1995] as implementedin PROC MULTTEST. FDR correction was used for theGLM omnibus test of any group difference. A criterion ofq < 0.05 was used to elevate omnibus statistical significance,with q being the FDR-adjusted P value. For each significantresult based on the q value, unadjusted P values were usedto evaluate pair-wise group differences. A criterion of P <0.05 was used to evaluate pair-wise statistical significance.

RESULTS

GLM Groups Analysis

The results for the GLM group analysis are listed inTables II through VI. In each table, the omnibus results arepresented in three columns for mean FA (Table II), MD(Table III), RD (Table IV), AD (Table V), and WM vol-

ume (Table VI). As the tables show, differences amonggroups that remained significant after FDR correctionincluded those measuring diffusivity (Tables III and IV)

TABLE II. Summary of general linear model results,

regional FA findings

Region F valuea Raw P value FDR q value

Left caudal middle frontal 1.106 0.352 0.463Left frontal pole 1.350 0.264 0.416Left lateral orbitofrontal 2.520 0.064 0.230Left medial orbitofrontal 2.372 0.077 0.230Left pars opercularis 1.309 0.277 0.416Left pars orbitalis 1.451 0.234 0.416Left pars triangularis 3.244 0.026 0.158Left rostral middle frontal 1.840 0.147 0.369Left superior frontal 1.490 0.224 0.416Right caudal middle frontal 0.509 0.677 0.677Right frontal pole 0.901 0.445 0.500Right lateral orbitofrontal 1.748 0.164 0.369Right medial orbitofrontal 1.085 0.360 0.463Right pars opercularis 3.333 0.024 0.158Right pars orbitalis 2.991 0.036 0.161Right pars triangularis 4.447 0.006 0.110Right rostral middle frontal 1.001 0.397 0.476Right superior frontal 0.704 0.553 0.585

aThe F-test reported in the table represents the main effect of CAPgroup from an analysis of covariance model that includes fourgroups (controls, low, medium, and high CAP groups) and age,years of education, gender, and site as covariates. df1 ¼ 3 and df2¼ 79 for all F-tests.

TABLE III. Summary of general linear model results,

regional MD findings

Region F valuea Raw P value FDR q value

Left caudal middle frontal 2.370 0.077 0.126Left frontal pole 0.598 0.618 0.655Left lateral orbitofrontal 4.181 0.008 0.051Left medial orbitofrontal 2.553 0.061 0.118Left pars opercularis 2.911 0.040 0.089Left pars orbitalis 1.560 0.206 0.265Left pars triangularis 3.647 0.016 0.055Left rostral middle frontal 4.883 0.004 0.033Left superior frontal 1.312 0.276 0.332Right caudal middle frontal 2.971 0.037 0.089Right frontal pole 0.102 0.959 0.959Right lateral orbitofrontal 4.949 0.003 0.033Right medial orbitofrontal 1.255 0.296 0.333Right pars opercularis 3.933 0.011 0.051Right pars orbitalis 2.498 0.066 0.118Right pars triangularis 1.926 0.132 0.198Right rostral middle frontal 3.537 0.018 0.055Right superior frontal 1.847 0.145 0.201

aThe F-test reported in the table represents the main effect of CAPgroup from an analysis of covariance model that includes fourgroups (controls, low, medium, and high CAP groups) and age,years of education, gender, and site as covariates. df1 ¼ 3 and df2 ¼79 for all F-tests.

TABLE IV. Summary of general linear model results,

regional RD findings

Region F valuea Raw P value FDR q value

Left caudal middle frontal 2.889 0.041 0.067Left frontal pole 0.436 0.728 0.771Left lateral orbitofrontal 4.430 0.006 0.022Left medial orbitofrontal 3.142 0.030 0.054Left pars opercularis 4.186 0.008 0.025Left pars orbitalis 2.024 0.117 0.176Left pars triangularis 4.550 0.005 0.022Left rostral middle frontal 4.657 0.005 0.022Left superior frontal 1.793 0.155 0.186Right caudal middle frontal 1.901 0.136 0.182Right frontal pole 0.207 0.891 0.891Right lateral orbitofrontal 5.091 0.003 0.022Right medial orbitofrontal 1.162 0.330 0.371Right pars opercularis 6.345 0.001 0.012Right pars orbitalis 3.594 0.017 0.039Right pars triangularis 3.788 0.014 0.035Right rostral middle frontal 3.200 0.028 0.054Right superior frontal 1.868 0.142 0.182

aThe F-test reported in the table represents the main effect of CAPgroup from an analysis of covariance model that includes fourgroups (controls, low, medium, and high CAP groups) and age,years of education, gender, and site as covariates. df1 ¼ 3 and df2¼ 79 for all F-tests.

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as opposed to volume (Table VI). Model-based groupmeans (adjusted for covariates) for regions whose differ-ences among groups that remained significant after FDR

correction are plotted in Figure 1. Figure 1 illustrates thatdifferences among groups that remained significant afterFDR correction were mainly in regions of the inferiorand lateral frontal lobe.

As seen in Table III, there were statistically significantdifferences in MD among groups in the left rostral middlefrontal (q ¼ 0.033) and right lateral orbitofrontal (q ¼0.033) regions. Figure 1 (upper right) illustrates mean MDincreased with CAP group, as shown by significantlyhigher MD values in the left rostral middle frontal regionfor both medium (P < 0.01) and high CAP (P < 0.005)groups and in the right lateral orbitofrontal region for thehigh CAP (P < 0.005) group in comparison to controls. Asseen in Table IV, the left rostral middle frontal (q ¼ 0.022)and right lateral orbitofrontal (q ¼ 0.022) regions also hadstatistically significant differences in RD among groups,along with the left lateral orbitofrontal (q ¼ 0.022) and allinferior frontal lobe regions (left pars opercularis, q ¼0.025; left pars triangularis, q ¼ 0.022; right pars opercula-ris, q ¼ 0.012; right pars orbitalis, q ¼ 0.039; right pars tri-angularis, q ¼ 0.035) bilaterally except for the left parsorbitalis. RD also increased with progression. Most regionshad significantly higher RD values for both medium (P <0.01–0.05) and high CAP (P < 0.0005–0.01) groups incomparison to controls, except for the left pars opercularis(P < 0.005), right lateral orbitofrontal (P < 0.001), andright pars orbitalis (P < 0.01) regions that had higher RDvalues for the high CAP group only (Fig. 1).

Cognitive Variable Partial Correlations

After the application of FDR correction to all correla-tions between cognitive and imaging variables, TMTB wasthe only cognitive variable that showed significant partialcorrelation with two imaging variables in several regions.Amongst the regions that demonstrated significant differ-ences in imaging variables among groups, the mean FA intwo regions (right pars opercularis and right pars triangu-laris) in addition to the right medial orbitofrontal regionnegatively correlated with TMTB score (all q ¼ 0.037).TMTB score also positively correlated with mean RD inthe right pars triangularis region (q ¼ 0.044) (SupportingInformation Table III). Complete summaries on the correla-tions between imaging variables and the SDMT (Support-ing Information Table I), TMTA (Supporting InformationTable II), TMTB (Supporting Information Table III), StroopWord (Supporting Information Table IV), Stroop Color(Supporting Information Table V), and Stroop Interference(Supporting Information Table VI) can be found in Sup-porting Information Tables I through VI.

DISCUSSION

The main goal of this study was to build upon past pro-dromal HD studies on the frontal lobe by examiningfocused regions of PFC WM in prodromal HD individuals

TABLE V. Summary of general linear model results,

regional AD findings

Region F valuea Raw P value FDR q value

Left caudal middle frontal 1.323 0.273 0.639Left frontal pole 0.650 0.585 0.676Left lateral orbitofrontal 3.007 0.035 0.131Left medial orbitofrontal 1.082 0.362 0.656Left pars opercularis 0.862 0.465 0.676Left pars orbitalis 0.657 0.581 0.676Left pars triangularis 1.289 0.284 0.639Left rostral middle frontal 3.368 0.023 0.131Left superior frontal 0.625 0.601 0.676Right caudal middle frontal 3.581 0.017 0.131Right frontal pole 0.024 0.995 0.995Right lateral orbitofrontal 2.982 0.036 0.131Right medial orbitofrontal 1.076 0.364 0.656Right pars opercularis 0.793 0.502 0.676Right pars orbitalis 0.858 0.466 0.676Right pars triangularis 0.386 0.763 0.808Right rostral middle frontal 3.161 0.029 0.131Right superior frontal 1.534 0.212 0.637

aThe F-test reported in the table represents the main effect of CAPgroup from an analysis of covariance model that includes fourgroups (controls, low, medium, and high CAP groups) and age,years of education, gender, and site as covariates. df1 ¼ 3 and df2¼ 79 for all F-tests.

TABLE VI. Summary of general linear model results,

regional WM volume findings

Region F valuea Raw P value FDR q-value

Left caudal middle frontal 0.247 0.863 0.971Left frontal pole 0.321 0.810 0.971Left lateral orbitofrontal 0.892 0.449 0.850Left medial orbitofrontal 0.847 0.472 0.850Left pars opercularis 2.654 0.054 0.488Left pars orbitalis 0.996 0.399 0.850Left pars triangularis 1.713 0.171 0.770Left rostral middle frontal 0.470 0.704 0.917Left superior frontal 0.490 0.690 0.917Right caudal middle frontal 1.876 0.140 0.770Right frontal pole 0.014 0.998 0.998Right lateral orbitofrontal 0.491 0.690 0.917Right medial orbitofrontal 0.457 0.713 0.917Right pars opercularis 4.034 0.010 0.181Right pars orbitalis 0.953 0.419 0.850Right pars triangularis 1.002 0.397 0.850Right rostral middle frontal 0.155 0.926 0.980Right superior frontal 1.115 0.348 0.850

aThe F-test reported in the table represents the main effect of CAPgroup from an analysis of covariance model that includes fourgroups (controls, low, medium, and high CAP groups) and age,years of education, gender, and site as covariates. df1 ¼ 3 and df2¼ 79 for all F-tests.

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using four commonly used measures of diffusivity (FA,MD, RD, and AD) and WM volume. Mean measures ofdiffusivity and WM volume for each region were com-pared across four groups (controls and three prodromalHD groups) and correlated with several measures of cog-nitive performance. In this study, much like the differencesin cognitive performance seen in prodromal HD subjectsat varying stages before diagnosis [Stout et al., 2011], stat-istically significant increases in MD and RD in CAPgroups relative to controls were seen in inferior and lateralPFC regions. In comparison to controls, a gradient ofeffects was seen in MD and RD, where the smallest effectwas seen in the low group and the largest effect in thehigh group. Significant correlations between TMTB scoreand mean fractional anisotropy (FA) and/or RD paralleledthe group differences in mean MD and/or RD in severalright hemisphere regions. The gradient effect of lower ani-sotropy with CAP group could be explained by largeraxon diameter or lower packing density of axons that bothdiscourage anisotropic diffusion [Takahashi et al., 2002].Specifically, significant differences in RD in the presenceof no findings in AD has been seen in an animal studythat attributed this effect to demyelination [Song et al.,2002]. In addition, changes in diffusivity that reflect a lossof directionality in diffusion seen in two other animalstudies (lower FA, higher RD) [Song et al., 2003, 2005]were demonstrated in the same regions of the right lateralPFC that showed group differences in MD and RD andcorrelated with a poorer performance on one of the cogni-tive tests used in this study (TMTB). In summary, thisstudy detected changes in diffusivity for the first time in aregion that has not been closely examined in the context ofprodromal HD. The meanings of these changes in diffusiv-ity were further supported by correlating with scores on acognitive test (TMTB) that has a documented ability todetect cognitive deficits in prodromal HD subjects. Thegradient of effects suggests DWI can provide reliablemarkers of disease progression in the form of increasingdiffusivity changes in the lateral PFC of prodromal HDindividuals. Therefore, the results of this study suggestthat mean RD in regions of the right lateral PFC couldserve as a reliable biomarker to monitor disease progres-sion in the prodromal HD stage.

The lack of findings for FA and MD in this studyemphasizes the importance of investigating directionalmeasures of diffusivity in addition to rotationally invariantdiffusivity measures. FA and MD are commonly usedmeasures of diffusivity because they summarize generalshape and magnitude of diffusion, respectively, byaccounting for diffusion magnitudes along three orthogo-nal directions at once [Basser and Pierpaoli, 1996]. Thethree orthogonal directions are numbered as eigenvectorsbased on the descending order of their corresponding dif-fusion magnitudes (first, second, and third eigenvalues)[Basser, 1995; Basser and Pierpaoli, 1996]. In comparisonto other summary measures of diffusivity (e.g. volumeratio and relative anisotropy), FA is less susceptible to noise

and provides the highest signal-to-noise ratio (SNR)[Papadakis et al., 1999]. However, when changes in diffu-sion are subtle and in one or two of the orthogonal direc-tions, these changes may not be reflected in summarymeasures that normalize or average across all diffusionmagnitudes. It may be more helpful to examine these subtlechanges using directional measures of diffusivity to see dif-fusion magnitudes perpendicular and parallel to the firsteigenvector. For example, Acosta-Cabronero et al. demon-strated increases in AD, RD, and MD that were morehighly significant and sensitive to white matter changes inearly Alzheimer’s patients than reductions in FA [Acosta-Cabronero et al., 2010]. In addition, the increases in AD,RD, and MD were located in areas where tract degenera-tion was expected to occur based on prior gray matterlesion studies, further challenging the notion that reducedFA alone is able to fully capture changes in axonal integrityin Alzheimer’s disease [Acosta-Cabronero et al., 2010].

When using measures of directional diffusivity, it iscommon to see changes in both RD and AD in a givenregion because the processes that cause changes in thesemeasures (axonal death and demyelination) often occur inclose proximity [Song et al., 2003]. As mentioned earlier,AD describes diffusion along the largest eigenvector[Basser and Pierpaoli, 1996]. Animal studies have demon-strated that a decrease in AD is associated with axonalinjury and degeneration because normal parallel diffusionalong axons is being hindered by dysfunctional tissue[Song et al., 2003]. RD describes diffusion perpendicular tothe first eigenvector. In contrast to AD, an increase in RDis associated with demyelination since diffusion perpen-dicular to the axon is increased when there is less myelina-tion [Song et al., 2003, 2005].

In this study, only increases in RD were seen in the pro-dromal HD individuals. It is important to remember thatsince RD is the mean of two eigenvalues it will be lessnoisy than AD, a measure that consists of a single eigen-value. Therefore, in this study RD may have been moresensitive to tissue changes and AD has yet to reach signifi-cance. An increase in RD with no change in AD has beendocumented in a very specific type of myelin pathologycalled dysmyelination [Song et al., 2002]. Dysmyelinationis the incomplete myelination of functional axons, asopposed to demyelination that is the complete loss of mye-lination [Song et al., 2002]. Song et al. examined diffusivitychanges in the setting of dysmyelination by using Shiverermice. Shiverer mice are homozygous for a recessive auto-somal mutation for myelin basic protein, causing incom-plete myelination in the central nervous system [Inoueet al., 1981; Privat et al., 1979; Rosenbluth, 1980; Shenet al., 1985]. Song et al. showed that major WM tracts inShiverer mice have increased RD but identical AD in com-parison to the same tracts in control mice [Song et al.,2002]. At this point in time, it is not possible to unambigu-ously interpret increased RD in the lateral PFC withoutAD changes in this study as a dysmyelintation process inprodromal HD individuals without longitudinal or

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histological data to pinpoint the exact process affecting dif-fusivity [Jones et al., 2012]. However, it must be empha-sized that this study demonstrated a consistent gradienteffect of increased RD without AD changes throughout thelateral PFC bilaterally.

The lack of WM volume findings in this study was ini-tially surprising, given that previous studies have showndecreases in WM volume in prodromal HD [Aylwardet al., 2011] and correlations between morphologicalabnormalities and cognitive deficits in early HD subjects[Beglinger et al., 2005]. It must be noted that if abnormalWM volume findings in the literature are specific to thefrontal lobe (either prodromal or symptomatic HD), theytend to be in the entire frontal lobe [Aylward et al., 1998,2011; Halliday et al., 1998]. An aspect of this study thatcould have prevented frontal lobe WM volume findingswas that a precentral gyrus region was not included inany part of the analysis. Perhaps WM volume abnormal-ities in prodromal HD individuals are specific to the pre-central gyrus. It was not possible to include the precentralgyrus because the DWI data used here did not consis-tently include the most superior portions of the frontaland parietal lobes. Although the FreeSurfer WM definitionhas been shown to produce similar mean FA values inthe same WM regions defined by other methods [Fjellet al., 2008; Tamnes et al., 2010] and variability withinregions are replicated across groups [Salat et al., 2009], itstill may not be the true volume of WM associated withthe cortical region.

In this study, significant group differences relative tocontrols in MD and RD were mostly located in the lateralPFC, specifically in the ventrolateral or lateral left andright inferior regions. Traditionally, the left inferior frontalarea is known to be involved in language, where lesions tothe posterior portion cause Broca’s aphasia [Fuster, 2009].However, the lateral inferior regions are broadly impli-cated in a number of higher-order executive processes[Perry et al., 2011]. Therefore, the significant correlationsbetween FA, RD, and TMTB in most of the same right in-ferior regions containing group differences may furthersuggest a link between the lateral inferior regions andhigher-order executive processes. Overall, the TMT is acognitive measure that has a documented ability to differ-entiate among prodromal HD individuals in the CAPgroups considered in this analysis [O’Rourke et al., 2011].Specifically, TMTB involves subjects connecting alternatingletters and numbers to test cognitive flexibility and work-ing memory, where the score is the time necessary to com-plete the task. Poor performance on the TMTB is reflectedin a longer completion time [O’Rourke et al., 2011]. TMTBscores negatively correlating with FA and positively corre-lating with RD in this study can possibly be interpreted to-gether as prodromal HD individuals experiencing greaterimpairment in executive functioning, processing speed,and working memory with disease progression due to awhite matter disease process that can be detected by meas-ures of diffusivity [Jones et al., 2012].

As for the significant findings in the dorsolateral andorbitofrontal PFC regions, these results may be explainedby the dorsal-to-ventral progression cell death in the stria-tum observed in HD [Hedreen and Folstein, 1995] affect-ing components of corticostriatal loops [Lawrence et al.,1998]. Specifically, Lawrence et al. hypothesized that func-tions associated with the dorsal PFC-striatal loop may beimpaired before motor symptom onset, followed byimpairment of functions associated with the ventral loopas neuronal loss increases with disease progression[Hedreen and Folstein, 1995; Lawrence et al., 1998]. Basedon anatomical studies done by Alexander et al. and Ari-kuni et al., the dorsal PFC striatal loop includes dorsolat-eral PFC projections to the central to dorsal caudate, whilethe ventral loop includes orbitofrontal PFC projections tothe ventromedial caudate [Alexander et al., 1986; Arikuniand Kubota, 1986]. Therefore, the significant findings inthe dorsolateral (increased MD and RD in the left rostralmiddle frontal region) and the orbitofrontal (increased MDand RD in the right lateral orbitofrontal and increased RDin the left lateral orbitofrontal regions) PFC in this studymay be explained by the pattern of cell death in the stria-tum affecting components of the corticostriatal loops asimplied by changes in diffusivity [Jones et al., 2012].

The main limitation of this study was that WM regionsof the PFC were only explored with WM volume and alimited set of scalar diffusivity measures derived from thetensor model. Another metric for detecting differences indiffusivity among tissue types in future studies of whitematter integrity in Huntington’s Disease is diffusional kur-tosis imaging (DKI). Diffusional kurotosis values quantifydiffusional non-Gaussianity as a consequence of tissuestructure creating barriers and compartments [Jensenet al., 2005]. Diffusional kurtosis values may providegreater sensitivity to differences between largely isotropictissues and have been used in ischemic stroke [Helpernet al., 2009; Latt et al., 2009], aging [Falangola et al., 2008],schizophrenia [Ramani et al., 2007], and attention deficitdisorder [Helpern et al., 2007]. Analyzing WM regionsderived from WM fiber tracts that connected cortical graymatter to the striatum instead of WM regions based onproximity to cortical gray matter would have provided ameans for specifically examining corticostriatal tracts.Additionally, using methods more sophisticated than thetensor model, such as high angular resolution diffusionimaging (HARDI) to resolve multiple fiber orientations inwhite matter containing crossing fibers, would be impor-tant to examine in the future with the number diffusion-weighted gradients per scan used in this study [Tuchet al., 2002]. The additional information on multiple fiberorientations per voxel could possibly make scalar diffusiv-ity measures more sensitive to changes in white matterand assist with more reliable fiber tract reconstructions infuture studies [Tuch et al., 2002]. Another limitation of thisstudy was the incomplete coverage of superior frontal andparietal lobes in DWI scans mentioned earlier that led toclipping in the superior frontal, caudal middle frontal, and

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rostral middle frontal regions. The rostral middle frontalareas were the least affected and perhaps that is why find-ings were strongest there. However, it is uncertainwhether the superior frontal and caudal middle frontalregions contain findings in this study as these regionswere visibly substantially clipped.

Future directions include expanding upon these findingsin the PFC in the form of more complex analyses. The nextstep is to perform cross-sectional fiber tracking to obtainrepresentations of the PFC WM that can be analyzed forchanges along each WM region. In addition to WM in thePFC, it may also be useful to examine WM extending tothe PFC from the striatum and beyond to characterize howHD affects corticostriatal loops in their entirety. Ultimately,the above analyses will be expanded to characterizechanges in individual subjects longitudinally.

CONCLUSION

The main goal of this study was to build upon past pro-dromal HD studies on the frontal lobe by examiningfocused regions of PFC WM in three groups of prodromalHD individuals stratified by baseline progression (low, me-dium, and high groups) using four commonly used meas-ures of diffusivity (FA, MD, RD, and AD) and WM volume.In summary, this study was able to detect differences in dif-fusivity based on baseline disease progression for the firsttime in the lateral PFC, a region that has not been closelyexamined in the context of prodromal HD. The meaning ofthese changes in diffusivity were further supported by cor-relating WM measures with scores on a cognitive test thathas a documented ability to detect cognitive deficits in pro-dromal HD. Therefore, the results of this study suggest thatmean RD in regions of the right lateral PFC could serve as areliable biomarker to monitor disease progression in the pro-dromal HD stage in future longitudinal studies.

ACKNOWLEDGMENTS

Image Processing Lab, Department of Psychiatry, Uni-versity of Iowa: Eric D. Axelson, Mark O. Scully, NormanK. Williams. The authors thank the PREDICT-HD sites, thestudy participants, and the National Research Roster forHuntington Disease Patients and Families.

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