r Human Brain Mapping 000:000–000 (2011) r

Multimodal Magnetic Resonance Imaging: TheCoordinated Use of Multiple, Mutually

Informative Probes to Understand BrainStructure and Function

Xuejun Hao*, Dongrong Xu, Ravi Bansal, Zhengchao Dong,Jun Liu, Zhishun Wang, Alayar Kangarlu, Feng Liu, Yunsuo Duan,

Satie Shova, Andrew J. Gerber, and Bradley S. Peterson

Columbia College of Physicians and Surgeons and New York State Psychiatric Institute, New York, New York

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Abstract: Differing imaging modalities provide unique channels of information to probe differingaspects of the brain’s structural or functional organization. In combination, differing modalities providecomplementary and mutually informative data about tissue organization that is more than their sum.We acquired and spatially coregistered data in four MRI modalities—anatomical MRI, functional MRI,diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS)—from 20 healthy adultsto understand how interindividual variability in measures from one modality account for variability inmeasures from other modalities at each voxel of the brain. We detected significant correlations of localvolumes with the magnitude of functional activation, suggesting that underlying variation in local vol-umes contributes to individual variability in functional activation. We also detected significant inversecorrelations of NAA (a putative measure of neuronal density and viability) with volumes of white mat-ter in the frontal cortex, with DTI-based measures of tissue organization within the superior longitudi-nal fasciculus, and with the magnitude of functional activation and default-mode activity duringsimple visual and motor tasks, indicating that substantial variance in local volumes, white matter orga-nization, and functional activation derives from an underlying variability in the number or density ofneurons in those regions. Many of these imaging measures correlated with measures of intellectualability within differing brain tissues and differing neural systems, demonstrating that the neural deter-minants of intellectual capacity involve numerous and disparate features of brain tissue organization, aconclusion that could be made with confidence only when imaging the same individuals with multipleMRI modalities. Hum Brain Mapp 00:000–000, 2011. VC 2011 Wiley Periodicals, Inc.

Keywords: multimodal MRI; anatomical MRI; functional MRI; diffusion tensor imaging; magneticresonance spectroscopy; correlation; brain structure; brain function

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

Contract grant sponsor: NIMH; Contract grant number:MH068318, MH089582, 1P50MH090966, MH36197, K02-74677;Contract grant sponsor: NIDA; Contract grant number: DA027100;Contract grant sponsor: NIEHS; Contract grant number: ES015579;Contract grant sponsor: NARSAD Distinguished InvestigatorAward; Contract grant sponsor: NIBIB; Contract grant number:1R03EB008235; Contract grant sponsor: Opening Project of

Shanghai Key Laboratory of Functional Magnetic ResonanceImaging (East China Normal University).

*Correspondence to: Xuejun Hao, 1051 Riverside Drive, Unit 74,New York, NY 10032. E-mail: HaoX@childpsych.columbia.edu

Received for publication 9 November 2010; Revised 31 March2011; Accepted 25 July 2011

DOI: 10.1002/hbm.21440Published online in Wiley Online Library (wileyonlinelibrary.com).

VC 2011 Wiley Periodicals, Inc.

INTRODUCTION

Magnetic resonance imaging (MRI) uses ingeniouslydesigned combinations of pulsing radiofrequency signalsand changing magnetic fields to measure tissue character-istics and their spatial locations in the brain. The energy inthe radiofrequency signals is absorbed by nuclei withinthe atoms and molecules that compose various brain tis-sues. Those tissues then emit that absorbed energy as asecond radiofrequency signal, but now imprinted with themolecular and chemical signatures of the emitting tissues.Because these tissue characteristics are numericallyencoded and displayed in the form of an image, MRI istoo often regarded as producing a ‘‘mere picture,’’ whenin fact the intimate interaction of the radiofrequency signalwith chemical nuclei is more accurately regarded as a non-invasive probe of brain tissue. The specific informationabout the brain that this probe provides depends on thepulse sequences, or the specific combinations of radiofre-quency signals and changing magnetic fields, that areused to interrogate the tissue. Some pulse sequences, usedin anatomical MRI, provide information about the anatom-ical organization of gray matter, white matter, and cere-brospinal fluid in the brain. Other sequences, used infunctional MRI (fMRI), provide information about time-varying levels of deoxyhemoglobin in the tissue, whichcan then be used to identify tissues that change in neuralactivity in response to performance of a behavioral task.Others, used in diffusion tensor imaging (DTI), provide in-formation about the constraints on the diffusion of waterin the brain, which is largely determined by the varyingconcentrations of cell membranes, organelles, and myelinin white and gray matter. Finally, sequences used in mag-netic resonance spectroscopy (MRS) provide informationabout the concentrations of certain molecules in the brain,including one metabolite, N-acetyl aspartate (NAA), that isthought to index the density and viability of neurons inthe brain [Arnold et al., 2001; Baslow, 2003; Edden et al.,2007] and to contribute to signaling between neurons andglia [Lebon et al., 2002]. Although the biological functionsof NAA are the subject of controversy, for the sake of sim-plicity and along with others [Moffett et al., 2006], we willrefer to NAA concentrations as indexing neuronal density.

Individually, each of these probes, or MRI modalities,provides a unique channel of information to view andunderstand one aspect of the brain’s structural or func-tional organization. In combination, however, these modal-ities provide complementary and mutually informativedata about tissue organization that is more than their sum[Filippi, 2009]. Moreover, their use in combination canhelp us understand causal mechanisms between modal-ities. For example, their combined use can help to deter-mine whether increases or decreases in functionalactivation detected with fMRI are related to increases ordecreases in underlying volume of cortical gray matter inthat region and therefore whether abnormal activation is aconsequence of, or a compensation for, an underlying ana-

tomical abnormality. DTI, alternatively, can help determinewhether anatomical or functional disturbances in two dis-tinct brain regions are associated with, and possiblycaused by, underlying disturbances in the anatomical con-nectivity between those two regions. MRS can help todetermine whether regional differences in volume or acti-vation are likely associated with disturbances in the healthor number of neurons in that region. Incorporation of mul-tiple, informative imaging modalities therefore can tell usmuch more about the neural basis of behavior than anysingle imaging modality can alone. Furthermore, multi-modal imaging can aid study of the neurobiologicaldeterminants of disease states. The findings from one mo-dality, for example, can help to constrain the interpreta-tions of findings from another modality, therebyimproving the neurobiological validity of those findingsand interpretations.

We are unaware of any previous studies that haveacquired and correlated imaging measures across all fourMRI modalities. When using two MRI modalities, voxel-wise analyses most often have been used, with rare excep-tions in preliminary studies [Eichler et al., 2002; Goreet al., 2006; Hayasaka et al., 2006; Irwan et al., 2005; Kra-kow et al., 1999, Pell et al., 2008], to segment the brain intodiffering tissues [Devlin et al., 2006; Kabir et al., 2007] orto examine correlations across modalities but within onlyone or several regions of interest [Barkovich et al., 2006;Nitkunan et al., 2008; Olesen et al., 2003; Toosy et al.,2004].

We report the acquisition, processing, and techniquesused for the coregistration of anatomical MRI, fMRI, DTI,and MRS data across the brains of 20 healthy adults. Cor-relation analyses across modalities were performed at eachvoxel of the brain to provide preliminary indications ofthe ways in which interindividual variability in measuresfrom one modality account for variability in measuresfrom other modalities. The imaging measures were alsocorrelated with measures of estimated intelligence andattention to determine which features of brain tissue areassociated with higher-order cognitive functions. Based onour assumptions that cellular composition determines ana-tomical structure and connectivity in the brain, and thatstructure in turn determines function, we hypothesizedthat we would detect significant correlations of neuronaldensity with measures of local brain volumes and struc-tural connectivity, correlations of local brain volumes withmeasures of brain function, correlations of neuronal den-sity with functional measures, correlations of structuralconnectivity with functional measures, and correlations oflocal brain volume with structural connectivity.

METHODS

We acquired data in four MRI modalities (anatomicalMRI, DTI, fMRI, and MRS) from 20 healthy adults andcomputed voxel-wise correlations across the various MRImeasures to study the interrelations among neuronal

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density, diffusion anisotropy, fMRI activity, and indices ofregional volumes.

Participants

Imaging data were acquired from 20 right-handedhealthy adults (10 males and 10 females, ages from 19 to45 years, mean age 29.7 � 7.7 years). The absence of neu-ropsychiatric illness was assured through administrationof the Schedule for Affective Disorders and Schizophrenia[Endicott and Spitzer, 1978]. Written informed consentwas obtained from each participant.

Cognitive Measures

Intelligence Quotient (IQ) was estimated using theWechsler Abbreviated Scale of Intelligence [Wechsler,1981]. We used the Connors Continuous Performance TestCPT II (available at: http://www.devdis.com/index.html)to measure attentional capacity and impulse control [Con-ners, 1994]. Participants were requested to press a buttonwhen any letter appeared, except for the letter ‘‘X’’,thereby priming the motor system to respond incorrectlyin the minority (10%) of trials when the X appeared, dur-ing which time the participant should have inhibited theprepotent inclination to respond. The task effectively iden-tifies errors of impulse control, counting both errors ofomission (failing to respond to the target letter) and com-mission (responding to the nontarget letter ‘‘X’’). A mea-sure of attentiveness was calculated to indicate how wellparticipants discriminated between targets and nontargets.Raw scores were transformed into T-scores for use in cor-relation analyses. Because the Commission and Attentive-ness T-scores were highly intercorrelated (r ¼ 0.91, P <0.001), we report only results using the Attentiveness mea-sure. Lower T-scores indicated better performance. IQ andCPT attention measures were not significantly intercorre-lated in this sample (Pearson’s r < 0.04, P ¼ 0.87).

MRI Acquisition

We collected anatomical T1-weighted images, diffusion-weighted (DW) images, functional images, and Multipla-nar Chemical Shift Imaging (MPCSI) data in a single scan-ning session from all individuals in this study. Imageswere acquired on a GE Signa 3T whole body scanner (Mil-waukee, WI) equipped with a body transmitter coil and an8-channel head receiver coil. Anatomical MRI, DTI, andfMRI data were acquired with the Array Spatial SensitivityEncoding Technique (ASSET), a GE version of the parallelimaging technique.

Anatomical MRI

High-resolution, T1-weighted images of the brain in sag-ittal orientation were acquired using fast spoiled gradientrecall (FSPGR) sequence: inversion time (TI) ¼ 500 ms,repetition time (TR) ¼ 4.7 ms, echo time (TE) ¼ 1.3 ms,

field of view (FOV) ¼ 24 cm, image matrix ¼ 256 � 256,acceleration factor ¼ 2, number of slices ¼ 160, slice thick-ness ¼ 1 mm.

Diffusion Tensor Imaging

DTI slices were acquired in an axial oblique orientationparallel to the AC-PC line using single-shot echo-planarDTI imaging sequence, with TR ¼ 15,700 ms, TE � 74 ms,FOV ¼ 24 cm, flip ¼ 90�, acquisition matrix ¼ 128 � 128(acceleration factor ¼ 2) zero-padded to 256 � 256, slices¼ 60, slices thickness ¼ 2.5 mm. We acquired three base-line images with b ¼ 0 s/mm2, and 25 diffusion weightedimages at b ¼ 1,000 s/mm2 with diffusion gradientsapplied in 25 directions sampling three-dimensional spaceuniformly [Jones et al., 1999].

Multiplanar Chemical Shift Imaging (MPCSI)

Multiplanar chemical shift imaging (MPCSI) data wereprescribed using localizer images acquired with TR ¼ 300ms, TE ¼ 10 ms, FOV ¼ 24 cm, slice thickness ¼ 10.0 mm,spacing ¼ 2.0 mm, acquisition matrix ¼ 256 � 128, imagezero-padded to 256 � 256. The spectral data were acquiredusing six axial oblique slices positioned parallel to the AC-PC line, with the second bottom-most slice containing theAC-PC line. Parameters for the MPCSI sequence [Duynet al., 1993] were: TR ¼ 2,800 ms, TE ¼ 144 ms, spectralwidth ¼ 2,000 Hz, number of complex data points ¼ 512,FOV ¼ 24 � 24 cm2, slice thickness ¼ 10.0 mm, spacing ¼2.0 mm, number of phase encoding steps ¼ 24 � 24. Watersuppression was achieved using the CHESS sequence.Lipid signal was suppressed by placing eight angulatedsaturation bands around the brain.

Functional Magnetic Resonance Imaging (fMRI)

T2*-weighted images were acquired in axial-oblique sli-ces positioned parallel to the AC-PC line using a gradient-recalled single-shot echo-planar pulse sequence with TR ¼2,200 ms, TE ¼ 30 ms, flip angle ¼ 90�, image matrix ¼ 64� 64, FOV ¼ 24 � 24 cm, in-plane resolution ¼ 3.75 � 3.75mm, number of slices ¼ 34, slice thickness ¼ 3.5 mm,number of imaging volumes ¼ 128. A full set of theseimages was acquired as participants performed each ofthree tasks to activate primary motor, visual, and auditorycortices. The order of these tasks was randomized amongthe 20 subjects.

Visual task

The visual stimulus was a checkerboard flashing at afrequency of 60 Hz and simultaneously rotated at a fre-quency of 1 Hz [Smith et al., 1998]. The concentric radialcheckerboard pattern filled half the display window (theother half of the window was blank). The center of thecheckerboard was positioned at the center of the window.The pattern consisted of 60 images per full circle, with

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each image rotating 6 degrees clockwise from the previousimage. The stimulus was programmed and deliveredusing E-Prime software (available at: http://www.pstnet.com/products/e-prime) and presented through fiber-opticgoggles. Visual stimulation was presented over seven on-off cycles in 20-s blocks of flashing checkerboard that alter-nated with 20-s blocks of gaze fixation on a white cross-hair placed in the middle of a black background.

Motor task

The motor task was identical in timing and similar indesign to that of the visual task. The 20-s blocks of activetask consisted of tapping the right index finger as quicklyas possible in response to a cue (a symbol changing from‘‘þ’’ to ‘‘X’’) displayed on the screen. These alternated overseven on-off cycles with 20-s epochs of gaze fixation on awhite cross-hair located in the middle of a blackbackground.

Auditory task

Similarly, an auditory stimulus was presented in 20-sblocks alternating over seven on-off cycles with 20-s blocksof gaze fixation on a white cross-hair located in the middleof a black background. The auditory stimulus consisted ofan ‘‘alarm whistle’’ that played at a frequency of 11.025kHz, delivered using E-Prime software.

Spatial Normalization of Multimodal Images

Voxel-wise correlations require the precise normalizationof data from various modalities into a template space. Spa-tially normalizing images from various MRI modalitiesinto one coordinate space is challenging, however, becauseof differing pixel resolution, image intensity, contrast, andamount of spatial distortion across images. AnatomicalMR images usually have the highest resolution, with voxelsizes of 1 � 1 � 1 mm3. DTI and fMRI images usuallyhave lower spatial resolution, with typical voxel sizes of 1� 1 � 2.5 mm3 and 3 � 3 � 3 mm3, respectively. MRSIimages usually have the lowest resolution, with voxel sizesof 10 � 10 � 10 mm3 and with a 2-mm skip between sli-ces. MRSI data are also typically acquired in only a limitednumber of slices and therefore do not cover the entirebrain.

Spatial Normalization—Overview

Multimodal imaging data were spatially normalized intothe coordinate space defined by the high-resolution, T1-weighted anatomical image of the template brain. To facili-tate voxel-wise statistical analyses across these imageswith differing resolutions, we normalized each of theimaging datasets into the common coordinate space byreslicing the DT and fMRI images to voxels 1 � 1 � 1mm3 in size (for DTI datasets, for example, we first

resliced each DW image to 1 � 1 � 1 mm3 voxels usingtrilinear interpolation and then reconstructed the DTs atthis smaller voxel size). fMRI data were smoothed beforereslicing, whereas anatomical, DTI, and MPCSI data werenot. We then spatially normalized images by first co-regis-tering them using a rigid body similarity transformation,and then nonlinearly warping the images using a methodbased on fluid dynamics [Christensen et al., 1994]. How-ever, unique characteristics of the data in each modalityrequired a unique approach to normalization of eachmodality’s data (Fig. 1a).

Spatial Normalization of Anatomical Images

We registered the T1-weighted image from each partici-pant (termed the ‘‘source’’ image) to the T1-weighted tem-plate image as follows: (1) We resampled the image tovoxel dimensions of 1 � 1 � 1 mm3. (2) We then coregis-tered the resampled image to the template image using anaffine transformation with 12 parameters, such that themutual information across images was maximized. Thisstep generated a deformation field (D4) between the twoimages that encoded the transformation mapping eachpoint in the source image into a point in the target image.(3) The coregistered image was then nonlinearly warpedinto the coordinate space of the template image using fluiddynamics [Bansal et al., 2005; Christensen et al., 1994]while maximizing the mutual information across theimages [Maes et al., 1997] to generate a high-dimensional,nonlinear deformation field (D5).

Spatial Normalization of DTI Data

We first warped the FA image from each participant tothe same person’s high-resolution T1-weighted anatomicalimage. We used the FA image for this within-subjectwarping because its contrast, unlike that of DW images,was similar to that of the T1-weighted image. We appliedtwo successive deformation fields for this warping, thefirst (D1) an affine transformation (three rotations, threetranslations, and global scale) and the second (D2) a high-dimensional, nonlinear warping of the images based onthe dynamics of fluid flow [Christensen et al., 1994]. Theapplication of D1 and D2 transformed the source FA mapsin their native space into the coordinate space of the ana-tomical image for each participant. We then successivelyapplied the same two deformation fields, D4 and D5, pre-viously determined for the coregistration of that partici-pant’s anatomical image with the template brain (above).D1, D2, D4, and D5 were then concatenated and appliedto the individual DTI datasets to bring them into the tem-plate space of high resolution anatomical data, using ourseamless procrustean algorithm developed in-house, forpreserving the unique diffusion characteristics encoded inDTI data. The seamless procrustean algorithm estimatesthe rotation matrix that reorients the tensor at a voxel in

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template space through the procrustean fit, based on theprobability distribution of the fiber orientation at thevoxel, which is estimated from the DTI measurementsaround the voxel [Xu et al., 2003, 2008].

Spatial Normalization of MPCSI Data

We normalized MPCSI data using the low-resolutionT1-weighted overlay image—i.e., the localizer image that

Figure 1.

Spatially normalizing multimodal datasets. (a) The normalization

flowchart. First, each participant’s T1-weighted image (‘‘Source’’)

was normalized to the template brain, yielding deformation

fields D4 and D5. Datasets from other modalities were then

normalized independently to the T1-weighted image of the

source brain that had been normalized to the template space.

Finally, deformation fields D4 and D5 were used to normalize

these datasets into the template space. Hi-Res T1 ¼ high-reso-

lution T1-weighted anatomical MR images; DWI ¼ diffusion

weighted imaging datasets; DTI ¼ diffusion tensor imaging data-

sets; FA ¼ fractional anisotropy; functional (FMRI) ¼ functional

imaging datasets; MPCSI ¼ multiplanar chemical shift imaging;

Low-Res T1 ¼ low-resolution T1-weighted overlay for MPCSI;

Def Field ¼ deformation field; D1, : : : ,D5 ¼ deformation fields

estimated in different steps; Compose D4(D5) ¼ single defor-

mation field obtained by composing fields D4 and D5. (b) Exam-

ples: spatial normalization of T1-weighted anatomical images and

FA maps to the template Brain. (i) Template image. (ii) Single

participant T1-weighted anatomical ‘‘source’’ image. (iii) Source

image normalized to the template brain. (iv) Group-averaged co-

registered T1-weighted image of 20 participants in template

space. (v) Single participant’s source FA map. (vi) Spatially nor-

malized FA map in template space. (vii) Group-averaged co-reg-

istered FA map of 20 participants in template space. [Color

figure can be viewed in the online issue, which is available at

wileyonlinelibrary.com.]

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was acquired in nearly ideal alignment with the MPCSIdata. Because the overlay image had a higher in-plane re-solution than that of the MPCSI data, it coregistered pre-cisely the MPCSI data to the high-resolution anatomicalimage of the same person, generating an rigid-body trans-formation (D3). We then successively applied deformationfields D4 and D5, determined previously for the coregis-tration of that participant’s anatomical image with thetemplate brain, to the MRSI data to bring them into thetemplate space using trilinear interpolation.

Spatial Normalization of fMRI Data

We first motion-corrected all functional images of eachparticipant using a rigid transformation to the middle (i.e.,the 65th out of 128) functional images of the same partici-pant. We spatially smoothed the motion-corrected func-tional images using a Gaussian-kernel smoothing functionwith full width at half maximum (FWHM) equal to 8 mmand then we trilinearly interpolated the smoothed imagesto a resolution of 1 � 1 � 1 mm3, and then coregisteredthese resampled functional images to the high-resolutionanatomical image of the same person. Finally, we normal-ized the coregistered functional images to the templatebrain by successively applying the two deformation fields,D4 and D5, determined previously for the coregistration ofthat participant’s anatomical image with the templatebrain.

Choice of Template Brain

The coregistrations and the voxel-wise correlationsbetween imaging datasets conceivably could havedepended on the choice of the participant whose brain isdesignated as the template. We therefore followed a two-step procedure to select a template brain that was mostrepresentative of our cohort by analyzing the morphologyof the brain surface defined with high precision in thehigh-contrast, high-resolution T1-weighted images. First,we identified a brain that was as representative as possibleof the demographics of the sample, i.e., demographicaldata were closest to the mean as possible in terms of age,weight, height, etc. The source brains for all remainingparticipants were coregistered to this preliminary tem-plate. The point correspondences on the surfaces of theircortices were determined, and we computed the distancesof the corresponding points on the cerebral surface of theother participants from the surface of the template brain.Then the brain for which all points across the surface areclosest (in terms of least squares) to the average of thecomputed distances was selected as the final templatebrain for multimodal data analysis. All brains then under-went a second coregistration, this time to this most repre-sentative template. We used a single representative brainas a template rather than an averaged brain because a sin-gle brain has well-defined tissue interfaces, such as theCSF-gray matter or gray-white matter interfaces.

Confirmation of Registration Accuracy

We assessed the accuracy of our methods for image nor-malization by visually comparing the warped sourceimage for each participant with the image for the templateand with the average image computed from normalizedimages from all participants. Our visual checks assessedthe precise matching of anatomical landmarks, includingcentral sulcus, interhemispheric fissure, anterior commis-sure, posterior commissure, and genu and splenium of thecorpus callosum.

Processing of MRI Data

Data from each MRI modality were processed to correctfor spatial distortions, intensity inhomogeneities, motionartifacts, and differing image resolution across imagingmodalities. In addition, NAA values were calibrated acrossparticipants.

Anatomical Data Processing

We used the computerized algorithm ‘‘N3’’ [Sled et al.,1998] to correct inhomogeneities in pixel intensity acrossthe image that were caused by nonuniformities in the Ra-dio Frequency (RF) field. This algorithm estimates itera-tively both the multiplicative bias field and thedistribution of the true tissue intensities to eliminate thedependence of the field estimate on anatomy. Extracere-bral tissues were removed using an automated tool forextracting the brain [Shattuck and Leahy, 2002] combinedwith manual editing. Connecting dura was removed man-ually on each slice in sagittal, coronal, and axial views.Finally, the brainstem was transected at the pontomedul-lary junction. We resliced all images into a standard orien-tation using anterior- and posterior-commissure landmarks[Talairach and Tournoux, 1988] to remove residual headflexion/extension and standard midline landmarks toremove head rotation and tilt. We have found that stand-ardizing orientation improves the accuracy of coregistra-tion to the template brain.

DTI Data Processing

We visually inspected all diffusion weighted images(DWIs) and discarded those having motion larger than 2to 3 mm or susceptibility artifacts. We corrected eddy-cur-rent spatial distortions along the phase-encoding direction[Haselgrove and Moore, 1996]. We computed the diffusiontensor at each voxel by fitting an ellipsoid to the DWI dataacquired along 25 gradient directions and three baselineimages [Xu et al., 2007]. To ensure that a tensor D waspositive definite, we first decomposed it into the productD ¼ A � AT, estimated the matrix A, and computed thetensor D from the product A � AT. We reconstructed ten-sors using DWIs in the original space. Then for each ten-sor we calculated its fractional anisotropy (FA) value,which expresses the degree of directionality of water

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diffusion, for use in spatial normalization of the DWI data-set to the template brain.

MPCSI Data Processing

MPCSI data were preprocessed using the software3DiCSI (available at: http://hatch.cpmc.columbia.edu/software.html) operating on a SUN workstation. The datawere spatially filtered using a Hamming window before2D Fourier transformation. Residual water signal was sup-pressed using a high pass filter. The data were subjectedto 4 Hz Gaussian line broadening and a 1D Fourier Trans-form to transfer them to the frequency domain. We thenselected an ROI in each slice and saved the spectral dataof the ROI to a file for further analysis using home-builtsoftware. We used model-based spectral fitting to modelthe spectrum in each voxel with a curve, identifying peaksin the spectrum for NAA, Cr, Cho, and lipid metabolites.The area under the peaks for each of the three metaboliteswas obtained by integrating the corresponding lines (seeFig. 2). To account for variations in receiver gain (RG), weconducted a series of phantom scans with increasing RGsand calculated the ratio of peak area to the noise level foreach RG. We used these ratios as correction factors to com-pensate for the effects of varying RGs on peak areas.Effects of transmitter gains (TG) were also correctedagainst an arbitrary value of TG0 ¼ 15.6 dB according to S¼ S0 10(TG-TG0)/20 [Soher et al., 1996]. Background noisewas calculated as the standard deviation of the real part ofthe complex data in the regions free of signal from metab-olites. The average SNR of NAA, defined as the peakheight of NAA to the standard deviation of data in the sig-nal-free region of the spectrum, was greater than 120, an

excellent SNR attributable to use of the multichannel coil.We normalized the peak areas by the noise level and usedthese normalized areas to reconstruct spectroscopic images(SIs) of the metabolites.

In addition to these standard MPCSI data processingsteps, we performed partial volume correction on theNAA values. Partial volume effects are pronounced inMRSI data and would limit the accuracy of correlationswith data in other modalities. Two sources contribute toMRSI partial volume effects. A large spectroscopic imagingvoxel usually consists of varying proportions of gray mat-ter, white matter, and CSF. In addition, the limited num-ber of k-space sampling points in MRSI produces signalbleeding across voxels, termed a ‘‘point-spread-function’’(PSF) effect, which must be taken into account when cor-recting for partial volume effects. The PSF of an MRSI sig-nal is a complex function that describes how the MRsignal from one voxel spreads to other voxels over theentire field of view. The PSF is determined in part by thek-space trajectory during data acquisition. It is also deter-mined by the window function for spatial filteringemployed prior to Fourier reconstruction to suppress longrange signal bleeding but at the price of increasing signalcontamination across adjacent voxels.

We calculated the PSF by simulating the MRSI acquisi-tion in an inscribed circle of 24 � 24 grids in k-space andsubsequently spatial filtering the data with a Hammingwindow function. The resulting 24 � 24 complex arraywas interpolated to 256 � 256 to match the high resolutionMR images. To obtain the compartment images with thesame resolution and PSF effect as the MRSI, we segmentedthe high-resolution MR images that were coregistered tothe MRSI slices into components of gray matter, white

Figure 2.

Examples of 1H MRS spectra. Left, spectra from white matter.

Right, spectra from the periphery of the brain showing the peak

of residual lipid. The blue curve represents the measured spec-

tra, whereas the other colored curves represent fitted spectra,

and the black curve shows the residual spectra. The lipid signal

is well separated from the NAA peak, supporting the validity of

our NAA measures throughout the brain, including at its periph-

ery. [Color figure can be viewed in the online issue, which is

available at wileyonlinelibrary.com.]

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matter, and CSF. We then convolved them with the PSF.From these low-resolution compartment images, weretrieved the NAA concentrations from the gray and whitematter in the i-th voxel using a linear regression model[Pfefferbaum et al., 1999]:

Si ¼ cgMg þ cwMw

�� ��þ n

where Si is the measured data for the metabolite, cg and cware the point spread structural representation of the grayand white matter, Mg and Mw are the gray and white mattercontributions to the metabolite signal, and n is noise. As Mg

can be expressed by a product of gray to white mater metab-olite ratio rgw andMw [Pfefferbaum et al., 1999]:

Si ¼ Mw cgrgw þ cw�� ��þ n

One can find the best gray-to-white matter metabolite ratioby first minimizing the error induced by the above regres-sion for the entire data volume and then applying this ra-tio to locally fit the gray and white matter metabolitecontributions using the regression model [Pfefferbaumet al., 1999]. The partial volume-corrected gray (or white)matter metabolite concentrations at the resolution of theMRS data are then resampled trilinearly into gray (orwhite) matter metabolite concentrations at a spatial resolu-tion of 1 � 1 � 1 mm3 to permit correlation analyses ofMPCSI data at the higher anatomical resolution.

fMRI Data Processing

We processed and analyzed functional images usingSPM2 (available at: http://www.fil.ion.ucl.ac.uk/spm/).We first visually inspected images to ensure the absence ofmotion artifacts. We then used automated procedures inSPM2 to correct motion. All motion estimates were <1mm displacement and <2 degree rotation along any axis.We removed intensity drifts in the images across timeusing a sixth order, Butterworth-type high-pass filter witha cutoff frequency equal to [3/4] of the task frequency. Af-ter the fMRI images were spatially normalized into stand-ard space (above), we computed activations via voxel-wiseGeneral Linear Modeling (GLM) under SPM2, in whichthe hypothesized hemodynamic response function (HRF)was derived from the task stimulus, which was of an alter-nating block-design, with 20 s of stimulus On and 20 s ofstimulus Off. The activations were derived from BOLDsignal amplitude (i.e., SPM contrast beta images).

Correlation Analyses

We calculated the Pearson’s correlation coefficient r tomeasure the strength of the pair-wise linear association oftwo imaging measures at each voxel. These imaging meas-ures included (1) the concentration of NAA (a marker ofneuronal density) from MPCSI data, (2) fractional anisot-ropy (FA, a measure of the directional constraint on thediffusion of water) from DTI data, (3) the BOLD signal

amplitude of brain activations (a measure of task-inducedneural responsivity) from fMRI data, and (4) an index oflocal volume expansion or compression from anatomicalMRI data, calculated using volume-preserved-warping(VPW) [Xu et al., 2007]. VPW preserves during spatial nor-malization the intensity weighted volume (i.e., intensity �volume of the voxel) of each voxel. Spatial normalizationusing VPW condensed relatively larger volumes so thatthey appeared as voxels of relatively higher signal inten-sity, and it expanded smaller volumes so that theyappeared as voxels of relatively lower signal intensity.

Across all voxels of the brain in template space, we cal-culated Pearson’s correlation coefficient r, and its associ-ated P value [Pagano, 2000], for the correlations of (1)NAA with fMRI activation, (2) FA with NAA, (3) local vol-umes with NAA, and (4) local volumes with fMRI activa-tions. Here we present only a small number ofrepresentative slices for all findings. A complete set of sli-ces showing correlations throughout the brain can befound in the Supporting Information.

Our null hypothesis, H0, for each analysis was that thecorrelation between brain measures would equal 0, andthe alternate hypothesis, H1, was that the correlationwould differ significantly from 0. To test these hypotheses,we calculated the test statistic (using n � 2 ¼ 18 degreesof freedom) t ¼ r

ffiffiffiffiffiffiffiffin�21�r2

q, and computed its P value. We

report voxels identified using a P value threshold <0.05for the correlation coefficient as significantly correlated, to-gether with the requirement that the correlation at thatthreshold occurred in a spatial cluster of at least 25 adja-cent pixels. All correlation analyses were conducted beforeapplication of this conjoint statistical threshold. Based onan approximation formula [Poline et al., 1997], this con-joint requirement yielded an effective P < 0.000005, reduc-ing substantially the false-positive identification of voxelswith significantly correlated imaging measures. This loweffective P value enabled us to perform all the combina-tion of tests that we report herein, such that the P valuesremained much smaller than 0.05 even after consideringthe multiple statistical comparisons.

RESULTS

Spatially normalized images from each participantmatched the template image well. The accuracy of our cor-egistration algorithm is evident in the image showing theaveraged data over all 20 subjects (Fig. 1b), in that the av-erage brain maintains the sharpness of tissue boundariespresent in the template image. A noneffective coregistra-tion algorithm would blur these boundaries.

Local Volumes and Functional Activation

We detected statistically significant inverse correlationsof VPW values with BOLD signal amplitude of fMRI acti-vation in primary visual cortex (Fig. 3a), indicating thatvolume compression in this area was associated with astronger BOLD response.

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Figure 3.

Correlation maps of VPW values with visual activation and NAA

concentrations, and of NAA concentrations with FA values. Cor-

relation coefficients with P value <0.5 are color encoded and dis-

played, and those with P value <0.05 are significant correlations.

The color bar used to encode correlations is at the bottom right

of the figure. (a) Correlation maps of VPW values with visual acti-

vation. Activation is in response to stimulation with a flashing

checkerboard in 20 healthy participants. Correlation analyses

were conducted before application of the fMRI statistical thresh-

old. First row: Group-averaged T1-weighted image. Second row:

Color-coded correlation coefficients overlaid onto the average

T1-weighted image. Third row: Group-averaged visual activation

BOLD signal amplitude map. The color bar used to encode activa-

tions is at the bottom right of the figure. On the right: Scatter-

plots for significantly inverse correlated region within visual cor-

tex (top: r ¼ �0.73, P ¼ �0.0004; bottom: r ¼ �0.57, P ¼�0.009). (b) Correlations of NAA concentrations with VPW val-

ues. The anterior limb of the internal capsule (ALIC) and poste-

rior limb of internal capsule (PLIC) are labeled. First row: Group-

averaged T1-weighted image. Second row: Color-coded correla-

tion coefficients overlaid onto the average T1-weighted image.

The extent of NAA data is marked by the white boundary curve.

Third row: Group-averaged VPW values. On the left: Scatter-plot

for an inverse correlation within white matter of the internal cap-

sule (r ¼ �0.68, P ¼ �0.001). On the right: Scatter-plots for

selected significantly inverse correlated regions within prefrontal

white matter and white matter (top: r ¼ �0.58, P ¼ �0.007; bot-

tom: r ¼ �0.62, P ¼ �0.003). (c) Correlations of NAA concen-

trations with FA values. The superior longitudinal fasciculus (SLF)

and region of crossing fibers (CF) are labeled. First row: Group-

averaged T1-weighted image. Second row: Color-coded correla-

tion coefficients overlaid on the average T1-weighted image. The

extent of NAA data is marked by the white boundary curve. Third

row: Group-average FA maps. The darker bands within the white

matter of the FA maps are areas containing possible crossing

fibers (from the superior corona radiate), not gray matter. On the

left: Scatter-plots for selected significantly inverse correlated

regions within thalamus and the cortical mantle (top: r ¼ �0.57, P

¼ �0.009; bottom: r ¼ �0.55, P ¼ �0.012). On the right: Scat-

ter-plots for selected significantly correlated regions within supe-

rior longitudinal fasciculus of white matter (top: r ¼ 0.70, P ¼0.0006; bottom: r ¼ 0.69, P ¼ 0.0007). [Color figure can be

viewed in the online issue, which is available at

wileyonlinelibrary.com.]

r Multimodal Magnetic Resonance Imaging r

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Neuronal Density and Local Volumes

We detected statistically significant inverse correlationsof NAA with VPW values in white matter regions (Fig.3b), particularly in the frontal cortex and internal capsulebilaterally, indicating that volume expansion in this areawas associated with lower NAA values.

Neuronal Density and White

Matter Organization

We detected significant positive correlations of NAAwith FA within the superior longitudinal fasciculus ofwhite matter bilaterally (Fig. 3c), indicating that greaterneuronal density in these regions was associated with amore constrained diffusion of water and, presumably,with a greater structural organization of white matter tis-sue. These correlations were not significant in regionswhere white matter fibers cross one another (regionswithin white matter that appear darker on the FA map).In contrast, significant inverse associations of NAA withFA were evident in the gray matter of the cortical mantle.

Neuronal Density and Functional Activation

We detected significant positive correlations of NAAwith BOLD signal amplitude for fMRI activation in the cu-neate and visual association cortices obtained during vis-ual stimulation (Fig. 4a), indicating that greater neuronaldensity was associated with greater functional activation.Correlations in primary visual cortex, at the center of thepeak activation during visual stimulation, were not them-selves statistically significant, although the available NAAdata extended only into a portion of the area of significantactivation because MPCSI data in the posterior corticalmantle were eliminated by placement of the saturationbands needed to suppress lipid signal from the scalp.

We also detected positive correlations of NAA withBOLD signal amplitude for fMRI activations in posteriorand anterior cingulate cortices during stimulation with theauditory task (Fig. 4b). These were regions that tended todecrease in signal intensity (to deactivate) during auditorystimulation compared with stimulation by only the back-ground scanner noise. In addition, significant differenceswere detected between men and women in the correlationof fMRI deactivations with NAA values (i.e., a significantactivation-by-sex interaction) in the anterior and dorsalposterior cingulate cortices during the auditory task. Inboth regions, NAA correlated positively with the magni-tude of deactivation in men but not in women (Fig. 4c).

A similar positive correlation of NAA with BOLD signalamplitude for fMRI deactivation was also detected in theposterior cingulate cortex during motor stimulation (Fig.4d). Within primary motor cortex itself, however, wedetected significant inverse correlations of NAA withfMRI BOLD signal amplitude (Fig. 4d).

Gray Matter Tissue Organization

and Functional Activation

FA values in the gray matter of the anterior and poste-rior cingulate correlated inversely with the magnitude ofdefault-mode activation during both the auditory andmotor tasks (Fig. 5a,b). FA correlated positively with themagnitude of activation of primary visual cortex but inver-sely with activation of the retrosplenial cortex during thevisual task (Fig. 5c).

Correlations with Higher Cognitive Functions

The magnitude of default-mode deactivation during au-ditory, motor, and visual stimulation correlated inverselywith measures of attentional capacity (Fig. 6a,b) and IQ(Fig. 7a,b). Activation of visual association cortices, how-ever, correlated positively with IQ scores (Fig. 7a). In addi-tion, VPW measures correlated inversely with measures ofattentional capacity in the anterior and posterior cingulatecortices (Fig. 6c) and positively with IQ scores in inferiorprefrontal white matter, the basal ganglia and thalamus,and cortical gray matter throughout the cerebrum (Fig. 7c).FA correlated positively with IQ scores in multiple whitematter regions, including the prefrontal cortex, parietalcortex, and cingulate bundle bilaterally (Fig. 7d). Correla-tions with attentional scores were not affected by covary-ing for IQ scores, and vice versa. The correlation of VPWvalues with FA values did not survive correction for mul-tiple comparisons and thus were not included here.

DISCUSSION

We were able to spatially coregister data from all fourMRI modalities to a single template brain and then con-firm that interindividual variability in measures from onemodality frequently was associated with interindividualvariability in measures from another modality. In inter-preting significant correlations between measures obtainedusing differing imaging modalities, we assume that cellu-lar composition determines anatomical structure and con-nectivity in the brain, and that structure in turndetermines function, to infer causal mechanisms thatunderlie the intercorrelations detected between imagingmeasures. We note each of the causal mechanisms that wepropose for the findings of this study, although based onthe principles of contemporary neuroscience, constitutesonly one of several possible alternative interpretations.

Local Volumes and fMRI Activation

VPW data in template space quantifies local volumechanges when warping an image from native space to thetemplate. The output is a voxel-wise map of the ratio ofthe volume of a region in native space to the volume ofthe corresponding region in the template space. A VPW

r Hao et al. r

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Figure 4.

Correlations of NAA concentrations with visual, auditory, and

motor activation. Correlation coefficients with P < 0.5 are color

encoded and displayed, and those with P < 0.05 are significant cor-

relations. The outermost extent of NAA data is marked by the

white boundary curve. The anterior cingulate cortex (ACC) and

posterior cingulate cortex (PCC) are labeled. (a) Correlations of

NAA concentrations with visual activation during flashing checker-

board task performed by 20 healthy subjects. First row: Color-

coded correlation coefficients overlaid onto the average T1-

weighted image. Second row: Group-averaged visual activation

BOLD signal amplitude map. On the right: Scatter-plot for signifi-

cantly correlated region within primary visual cortex (r ¼ 0.68, P

¼ 0.001). (b) Correlations of NAA concentrations with auditory

activation. First row: Color-coded correlation coefficients overlaid

onto the average T1-weighted image. The ‘‘þ’’ sign marks the cen-

ter of peak activation in auditory cortex. Second row: Group-aver-

aged auditory activation BOLD signal amplitude map. On the left:

Scatter-plot for a strong positively correlated region within poste-

rior cingulate cortex (top: r ¼ 0.66, P ¼ 0.002; bottom, r ¼ 0.65, P

¼ 0.002). (c) Differences across men and women in correlations

of NAA with default-mode deactivation. The functional activation

was generated by auditory stimulation. The statistical model

included NAA value and the covariate of sex: Auditory Activation

¼ NAA þ sex þ NAA � sex. First row: Color-coded P values

associated with the interaction term overlaid on the average T1-

weighted image. The color bar used to encode P values is shown at

the top right of the figure. Second row: Group-averaged auditory

activation BOLD signal amplitude map. On the left: Scatter-plots

for NAA � sex interactions in selected significant regions within

the anterior cingulate cortex and posterior cingulate cortex (top:

P ¼ 0.009; bottom: P ¼ 0.003). (d) Correlations of NAA concen-

trations with motor activation. First row: Color-coded correlation

coefficients overlaid onto the average T1-weighted image. Second

row: Group-averaged motor activation BOLD signal amplitude

map. On the left: Scatter-plot for strong positively correlated

region within cuneate cortex (top: r ¼ 0.61, P ¼ 0.004) and strong

inversely correlated region within primary motor cortex (bottom:

r ¼ �0.51, P ¼ �0.022). [Color figure can be viewed in the online

issue, which is available at wileyonlinelibrary.com.]

r Multimodal Magnetic Resonance Imaging r

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value >1 indicates that the region in native space waswarped to a smaller region in template space and, con-versely, a VPW value <1 indicates that a region in nativespace was warped to a larger region in the template. Incontrast, data from other imaging modalities (fMRI, MRS,and DTI) were trilinearly interpolated from native space tothe template using the deformation field that mapped the

native coordinates to those of the template. Therefore, thevalues for data in these other modalities were identical innative and template space.

We detected significant inverse correlations of VPWmeasures with functional activation in the primary visualcortex (Fig. 3a), with smaller values in template space, orlarger volumes in native space, accompanying more

Figure 5.

Correlations of FA values with auditory, motor, and visual activa-

tion. Correlation coefficients with P < 0.5 are color encoded and

displayed, and those with P < 0.05 are significant correlations. The

anterior cingulate cortex (ACC) and posterior cingulate cortex

(PCC) are labeled. (a) Correlations of FA values with auditory

activation. First row: Color-coded correlation coefficients overlaid

onto the average T1-weighted image. Second row: Group-aver-

aged auditory activation BOLD signal amplitude map. On the left:

Scatter-plot for strong inversely correlated region within primary

auditory cortex (top: r ¼ �0.63, P ¼ �0.003; bottom: r ¼ �0.55,

P ¼ �0.012). On the right: Scatter-plot for strong inversely corre-

lated region within anterior cingulate cortex (r ¼ �0.55, P ¼�0.012). (b) Correlations of FA values with motor activation. First

row: Color-coded correlation coefficients overlaid onto the aver-

age T1-weighted image. Second row: Group-averaged motor acti-

vation BOLD signal amplitude map. On the left: Scatter-plot for

strong inversely correlated region within the anterior cingulate

cortex (top: r ¼ �0.67, P ¼ �0.001), posterior cingulate cortex

(middle: r ¼ �0.60, P ¼ �0.005), and cuneate cortex (bottom: r ¼�0.59, P ¼ �0.006). (c) Correlations of FA values with visual acti-

vation during flashing checkerboard task performed by 20 healthy

subjects. First row: Color-coded correlation coefficients overlaid

onto the average T1-weighted image. Second row: Group-aver-

aged visual activation BOLD signal amplitude map. On the left:

Scatter-plots for strong inversely correlated region within primary

visual cortex (top: r ¼ �0.54, P ¼ �0.014) and strong positively

correlated region within cuneate cortex (bottom: r ¼ 0.74, P ¼0.0003). [Color figure can be viewed in the online issue, which is

available at wileyonlinelibrary.com.]

r Hao et al. r

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functional activation. These findings suggest that visualactivation is closely related to individual variability in vol-ume of the visual cortex. Although MRI is unable to dis-cern the microscopic determinants of local volume

expansion in native space, neuroanatomical studies in ani-mal models of development suggest strongly that localcortical volumes are determined by lateral expansion ofthe cortex, and that this lateral expansion is determined by

Figure 6.

Correlations of CPT attention T-score with auditory activation,

visual activation, and VPW values. Correlation coefficients with

P < 0.5 are color encoded and displayed, and those with P <0.05 are significant correlations. The color bars used to encode

correlations and activations are at the bottom right of the fig-

ure. (a) Correlations of CPT attention t-score with auditory

activation. Correlation analyses were conducted before applica-

tion of the fMRI statistical threshold. First row: Color-coded

correlation coefficients overlaid onto the average T1-weighted

image. Second row: Group-averaged auditory activation BOLD

signal amplitude map. On the right: Scatter-plot for strong inver-

sely correlated region within primary auditory cortex (top: r ¼�0.46, P ¼ �0.04) and posterior cingulate cortex (bottom: r ¼�0.57, P ¼ �0.009). (b) Correlations of CPT attention t-score

with visual activation during flashing checkerboard task per-

formed by 20 healthy subjects. Correlation analyses were con-

ducted before application of the fMRI statistical threshold. First

row: Color-coded correlation coefficients overlaid onto the av-

erage T1-weighted image. Second row: Group-averaged visual

activation BOLD signal amplitude map. On the left: Scatter-plot

for strong inversely correlated region within superior temporal

lobe (top: r ¼ �0.51, P ¼ �0.022), and cuneate cortex (bottom:

r ¼ �0.79, P ¼ �0.00004). On the right: Scatter-plot for strong

inversely correlated region within superior frontal cortex (top: r

¼ �0.65, P ¼ �0.002), and posterior cingulate cortex (bottom:

r ¼ �0.54, P ¼ �0.014). (c) Correlations of CPT attention t-

score with VPW values. First row: Color-coded correlation

coefficients overlaid onto the average T1-weighted image. Sec-

ond row: Group-averaged VPW values. On the right: Scatter-

plot for strong inversely correlated region within anterior cingu-

late cortex (top: r ¼ �0.58, P ¼ �0.007) and posterior cingu-

late cortex (bottom: r ¼ �0.62, P ¼ �0.004). [Color figure can

be viewed in the online issue, which is available at

wileyonlinelibrary.com.]

r Multimodal Magnetic Resonance Imaging r

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Figure 7.

Correlations of full scale IQ with visual and motor activation,

VPW values, and FA values. Correlation coefficients with P <0.5

are color encoded and displayed, and those with P < 0.05 are

significant correlations. (a) Correlations of full scale IQ with vis-

ual activation during flashing checkerboard task performed by 20

healthy subjects. Correlation analyses were conducted before

application of the fMRI statistical threshold. First row: Color-

coded correlation coefficients overlaid onto the average T1-

weighted image. Second row: Group-averaged visual activation

BOLD signal amplitude map. On the right: Scatter-plot for

strong inversely correlated region within superior temporal lobe

(top: r ¼ �0.56, P ¼ �0.011) and positively correlated region

within occipital cortex (top: r ¼ 0.60, P ¼ 0.005). (b) Correla-

tions of full scale IQ with motor activation. Correlation analyses

were conducted before application of the fMRI statistical thresh-

old. First row: Color-coded correlation coefficients overlaid

onto the average T1-weighted image. Second row: Group-aver-

aged motor activation BOLD signal amplitude map. On the left:

Scatter-plot for strong inversely correlated region within the an-

terior cingulate cortex (top: r ¼ �0.67, P ¼ �0.001) and middle

occipital gyrus (bottom: r ¼ �0.54, P ¼ �0.014). On the right:

Scatter-plot for strong inversely correlated region within poste-

rior cingulate cortex (r ¼ �0.62, P ¼ �0.003). (c) Correlations

of full scale IQ with VPW values. First row: Color-coded corre-

lation coefficients overlaid onto the average T1-weighted image.

Second row: Group-averaged VPW map. On the left: Scatter-

plot for strong positively correlated region within left anterior

corona radiata (top: r ¼ 0.59, P ¼ 0.006) and putamen (bottom:

r ¼ 0.65, P ¼ 0.002). On the right: Scatter-plot for strong posi-

tively correlated region within left superior corona radiata (r ¼0.60, P ¼ 0.005). (d) Correlations of full scale IQ with FA val-

ues. First row: Color-coded correlation coefficients overlaid

onto the average T1-weighted image. Second row: Group-aver-

aged FA map onto which the boundaries of strong positively

correlated regions were overlaid for better localization of the

significant findings. On the left: Scatter-plot for strong positively

correlated region within right anterior corona radiata (r ¼ 0.64,

P ¼ 0.002). On the right: Scatter-plot for strong positively cor-

related region within right cingulum (top: r ¼ 0.57, P ¼ 0.009)

and left superior longitudinal fasciculus (bottom: r ¼ 0.61, P ¼0.004). [Color figure can be viewed in the online issue, which is

available at wileyonlinelibrary.com.]

r Hao et al. r

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the number of radial columnar units in that cortical region[Rakic, 1988]. This determination of local cortical volumesby lateral expansion is particularly strong in primary sen-sory cortices, where the laminar organization that deter-mines cortical thickness is highly invariant acrossindividuals. We therefore speculate that the number of ra-dial columnar units, the primordial anatomical buildingblocks within cortical gray matter, is relatively constantper unit tissue volume of primary visual cortex [Rakic,1988]. A larger volume in native space will thereby containa greater overall number of columnar units which, if eachcolumnar unit generated a similar degree of task-relatedactivation, would sum to a larger overall magnitude ofactivation. Our finding that VPW correlated inversely withfunctional activation has important implications for under-standing findings from fMRI studies, as it suggests thatinterindividual variability in functional activation mayderive, at least in part, from differences in underlying ana-tomical features of the brain. In particular, our findingssuggest that persons who activate more strongly in tem-plate space may have an underlying expansion of that ana-tomical region in native space, prior to normalization tothe spatial template.

Neuronal Density and Local Volumes

We detected significant inverse correlations of NAA (aputative measure of neuronal density and viability) withlocal volumes (VPW measures) in white matter regions,particularly in the frontal cortex and internal capsule bilat-erally (Fig. 3b). Thus larger volumes were associated withlower neuronal density in these regions. If we assume thataxonal densities in frontal regions and the internal capsuleare relatively similar across people in template space, thendistributing the same number of axons across a larger vol-ume in native imaging space would reduce the density ofthose axons. These considerations suggest that the numberof axons in these regions may be similar in correspondingvoxels across individuals, but that the differing anatomyacross people distributes those axons differently. This dif-ferential volumetric distribution of similarly numberedwhite matter axons therefore would produce the inversecorrelations of neuronal density with local volumes thatwe observed.

An alternative explanation is that this inverse correlationis an artifact that derives from differing spatial resolutionsof the anatomical and MRS datasets. These differing reso-lutions would produce differing partial volume effects inregions where white matter, gray matter, and cerebrospi-nal fluid are in close proximity. Persons who have moreCSF from a larger lateral ventricle in a corresponding MRSvoxel, for example, would have less NAA in that voxelthan would someone who has a smaller lateral ventricle inthat voxel. Coregistering the corresponding anatomicalimages to the template brain in that same person with thelarger ventricle would require expansion of white mattersurrounding the lateral ventricle to match the smaller tem-

plate ventricle, thereby ensuring that larger VPW valuesaccompany reduced NAA. Correlations of NAA valueswith VPW measures, however, were mainly inside gray orwhite matter, with few significant correlations present atthe boundary between tissue types. Moreover, we usedthe most rigorous and sophisticated methods available tocorrect NAA values for partial volume effects, therebysubstantially reducing the likelihood that the observedcorrelations of NAA with VPW values were an artifact ofdiffering partial volume effects across these imagingmodalities.

Neuronal Density and Tissue Organization

We detected positive correlations of neuronal density(NAA) with DTI-based measures of tissue organization(FA) within the superior longitudinal fasciculus (Fig. 3c),consistent with positive correlations detected in thesesame regions in two previous preliminary studies thatused chemical shift imaging in healthy human volunteers[Eichler et al., 2002; Irwan et al., 2005]. This finding indi-cates that greater neuronal density is associated with amore constrained diffusion of water and with a greaterstructural organization of white matter. A greater numberof neurons per unit tissue volume, measured as a greaterdensity, would also increase the density of cell mem-branes, organelles, and myelin, which in turn would pro-portionally restrict the diffusion of water in that tissue.Correlations were not significant in regions where whitematter fibers cross one another, likely because the fibersthere were roughly orthogonal to one another. This cross-ing of fibers would cause the net FA values within eachvoxel to equal nearly 0 (and therefore to appear dark onthe FA map) because of partial volume averaging. Thefinding of a significant correlation of neuronal densitywith FA in regions where the directions of fiber pathwaysare more coherent, however, has important implicationsfor the study of white matter using DTI. The findingshows that a substantial portion of variance in FA meas-ures likely derives from an underlying variability in thenumber of neurons in those regions, rather than necessar-ily from some organizational feature intrinsic to neuronsthat are otherwise erroneously presumed to be similar innumber across people.

We also detected significant inverse correlations of neu-ronal density (NAA) with measures of the degree of tissueorganization (FA) in gray matter of the cortical mantle(Fig. 3c). Cortical tissue consists primarily of nerve cellbodies and neuropil, or arborized dendrites, axons, andsynapses, which tend not to have directional coherencewithin a volume of tissue as large as a DTI or MRS voxel.Greater degrees of arborization of axons and dendriteswould increase NAA while also reducing directional co-herence [Kroenke et al., 2007], thereby producing aninverse association of NAA with FA values. Therefore,imaging studies that compare FA values in cortical graymatter across individuals should consider that lower FA

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values may in fact derive from greater axonal and dendri-tic arborization, and not from less neural tissue per se. Wecannot exclude the possibility that the inverse correlationof NAA with FA derives instead from an artifact of core-gistering imaging modalities that have differing spatialresolutions (analogous to the artifact postulated for theinverse correlation of VPW and NAA measures), althoughthe highly consistent positive correlations of NAA with FAin white matter regions and inverse correlations in corticalgray matter, albeit sometimes at trend levels of statisticalsignificance (Fig. 3c), weighs against this possibility.

Neuronal Density and fMRI Activation

We detected significant positive correlations of neuraldensity (NAA) with the magnitude of functional activationduring visual stimulation in the cuneate cortex and visualassociation cortices in the inferior parietal cortex (Fig. 4a).This correlation suggests that greater neuronal density pro-duces greater functional activation, although this relation-ship seems not to be generally true throughout the brain,given that we detected the opposite relationship, an inversecorrelation, in the primary motor cortex during motorstimulation (Fig. 4d). These findings suggest that sensoryassociation and primary motor cortices are organized in fun-damentally different ways, at least in terms of neuronal den-sity and the local cellular and molecular characteristics thatdrive the BOLD response to sensory or motor stimulation.The differing correlations, however, could also be attribut-able to the nature of the tasks used to generate the activationmaps. The motor task, for example, was an active task inwhich participants generated finger movements upon com-mand. The visual stimulation task, in contrast, required onlypassive participation of our subjects. The passive nature ofthe task may have contributed fundamentally to the positivecorrelation in the visual task compared with the inverse cor-relation in the more active motor task. Consistent with thispossibility is the correlation of NAA with fMRI signalchange in the auditory cortex during auditory activation,which was also generated using a passive task (listening towhite noise ramping up and down).

We also detected significant positive correlations of neu-ronal density (NAA) with the magnitudes of deactivation inthe posterior and anterior cingulate cortices during auditoryand motor stimulation. Deactivations during the active taskrelative to an easier control condition in these brain regionshas been well documented in previous studies and has beenvariously termed either ‘‘random episodic silent thinking’’[Andreasen et al., 1995] or ‘‘default-mode activity’’ [Gusnardet al., 2001; Marsh et al., 2006; Mason et al., 2007; McKiernanet al., 2006; Simpson et al., 2001]. These positive correlationsindicate that less neuronal density contributes to a greatersuppression of default-mode activity during the active taskrelative to the easier baseline condition. Although the cellu-lar mechanisms that produce or support this correlation areunknown, we speculate that the reduced neuronal density

that accompanies greater suppression of default-mode activ-ity likely represents a proportionally reduced neuropil inthe anterior and posterior cingulate cortices. Reduced neuro-pil, particularly the pruning of synapses and dendrites, is animportant feature of normal development that supportsimproving attentional processing and cognitive control withadvancing age during childhood and adolescence [Gieddet al., 1999; Huttenlocher, 1990; Sowell et al., 2003; Tau andPeterson, 2010]. This interpretation is consistent with ourunderstanding of the inverse correlation of NAA with FA(above), in which we speculated that reduced neuropil likelyproduced greater tissue organization within these same cort-ical gray matter regions.

Gray Matter Tissue Organization

and Functional Activation

The inverse correlations of FA values with the magnitudeof default-mode activation in anterior and posterior cingulatecortices during the auditory and motor tasks (Fig. 5) suggeststhat tissue organization of gray matter in these regions wasan important determinant of the magnitude of default-modeactivity, with greater tissue organization accompanying moreprominent deactivation. We also detected inverse correlationsof NAA with FA in cortical gray matter, and positive corre-lations of NAA with the magnitude of default-mode deacti-vations in the anterior and posterior cingulate cortices. Thusless NAA accompanied more default-mode deactivation; lessNAA accompanied greater FA values; and greater FAaccompanied more default-mode deactivation. Therefore wespeculate that the inverse correlation of FA with default-mode deactivation likely was driven by the underlying influ-ences that NAA values (and the neuronal density that thesevalues are thought to represent) had on FA and default-mode deactivation. Reduced NAA in cortical gray matterregions, as indicated above, likely represented reduced neu-ropil, which would have increased tissue organization andFA values. Greater degrees of synaptic and dendritic prun-ing, the likely cause of reduced neuropil, are thought toenhance attentional processing and cognitive control [Gieddet al., 1999; Huttenlocher, 1990; Sowell et al., 2003], cognitivecapacities that are believed to suppress the mind-wanderingthat is present more during the easier baseline task and thatis suppressed during times of greater attentional engagementto produce default-mode deactivation [Mason et al., 2007;McKiernan et al., 2006].

FA also correlated positively with the magnitude of acti-vation in primary visual cortex but inversely with activa-tion in retrosplenial cortex during the visual task (Fig. 5).These findings, together with those in default-mode corti-ces above, suggest that FA may correlate inversely withfunctional activation in association cortices but positivelywith activation in primary sensory cortices.

Correlations with Higher Cognitive Functions

Higher IQ and better attentional capacity independentlycorrelated strongly with many of our imaging measures,

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and in differing brain regions. Better cognitive functioningwas consistently associated with greater default-modedeactivation during auditory, motor, and visual stimula-tion (Figs. 6 and 7). These findings are consistent with thecommonly held view that greater deactivation in default-mode circuits reflects greater suppression of mind-wander-ing activity during a task that engages attention relative toan easier baseline condition [Mason et al., 2007; McKiernanet al., 2006] and with theories that intelligence dependsheavily on the capacity to control thought and to suppressirrelevant stimuli. Also consistent with this interpretationof default-mode correlations was our finding that betterattentional capacity on the CPT accompanied larger vol-umes of the anterior and posterior cingulate cortices (Fig.6c), the regions most consistently identified as componentsof default-mode circuits. Alternatively, greater deactivationin default-mode regions could also represent greaterdegrees of mind-wandering during the easier baseline con-dition, rather than being determined only by the degree ofsuppression of mind-wandering activity. In that case,greater attentional and intellectual capacity would accom-pany a greater capacity for mind-wandering activity dur-ing the easier baseline condition.

Higher IQ scores accompanied larger volumes of infe-rior prefrontal white matter, larger cortical gray mattervolumes throughout the cerebrum, and larger basal gan-glia and thalamus volumes (Fig. 7c). These prefrontal corti-ces and subcortical nuclei have long been regarded as theneural basis for the cognitive and behavioral control thatsupport better performance on intelligence tests. In addi-tion to these gray matter volumes, higher IQ scores accom-panied higher measures of tissue organization (larger FAvalues) in multiple white matter regions, including theprefrontal cortex, parietal cortex, and cingulate bundlebilaterally (Fig. 7d), consistent with findings from priorDTI studies of IQ correlates [Deary et al., 2006; Schmi-thorst et al., 2005]. FA correlations with measures of whitematter organization likely represent the effects that greatermyelination has on increasing FA in white matter and onimproving the speed of neural transmission in associationfibers that connect gray matter regions across long distan-ces. The correlations could also represent the effects ofgreater neuronal density, which we have shown correlatespositively with FA values in these same white matterregions. Alternatively, the positive correlations in moreposterior brain regions, where fiber crossings producelower FA values, could derive from more fiber crossingsand therefore less directional coherence in those who havelower IQ scores.

Limitations

Despite our overall success in acquiring, processing, cor-egistering, and analyzing multimodal MRI data within asingle template space and demonstrating the utility ofrelating data in one modality to data acquired in anothermodality, this technology, our approach to image analysis,

and our findings have several limitations. Perhaps themost prominent is the differing spatial resolutions of thevarious imaging modalities, which invariably producesdifferences in partial volume effects across modalities andthe possibility that some of the correlations of measuresacross modalities may be an artifact that derives from par-tial volume effects, particularly at the interface of differingcerebral tissues. This kind of artifact seems most plausiblefor those correlations involving NAA, which is the MRImodality in our dataset that has the largest voxel dimen-sions. In addition, NAA measures were missing in someportions of the cortical mantle because of the placement ofsaturation bands needed to prevent contamination of NAAvalues from lipid signals in the scalp.

Another limitation is inherent in the use of subtractionparadigms to generate measures of functional activation inthe fMRI modality. Subtraction paradigms isolate cerebralactivity that supports a limited cognitive or behavioralprocess and therefore measure activity in a limited num-ber of brain regions, leaving many other brain regionsunexplored. Implementing more recent procedures thatmeasure cerebral perfusion using MRI [Detre and Wang,2002; Floyd et al., 2003] would provide a measure of rest-ing functional activity throughout the entire brain thatcould be more versatile and more revealing of biologicalrelationships with other imaging measures in correlationanalyses across the cerebrum.

Our methods for coregistration relied heavily on the useof sophisticated methods of linear and nonlinear warpingof the images to optimize gray scale and surface bounda-ries of the brain. Matching certain pulse sequence parame-ters, including receiver bandwidth, echo spacing, numberof echoes, and spatial resolution at the time of image ac-quisition would have provided identical distortions knownto affect the DTI and fMRI imaging datasets and wouldhave aided the accuracy of coregistration, at least acrossthose two imaging modalities [Mulkern et al., 2006; Wer-ring et al., 1999]. Acquiring DTI, fMRI, and MRS at spa-tially identical resolutions as the anatomical images,however, is not yet possible given current constraints onhardware and pulse sequences that would prohibitivelyincrease scan time and reduce signal-to-noise ratios in theimages to unacceptable levels.

CONCLUSIONS

These various MRI modalities constitute complementary,mutually informative probes of cerebral tissue that, whenused together in the same individuals, improve our under-standing of the organizational and functional characteris-tics of the brain. Findings in this sample of healthyparticipants revealed that local volumes contribute to indi-vidual variability in functional activation and that underly-ing neuronal density in turn contributes to individualvariability in local volumes, functional activation, andcommonly used measures of white matter organization.We anticipate that these relationships between imaging

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measures will be altered in disease states [Eichler et al.,2002]. Although interpreting altered correlations betweenimaging measures will likely prove challenging, acceptingthat challenge is the only way to begin determining whichstructural and functional disturbances are primary, ormost fundamental, to the disease process, and which aresecondary, or derivative, consequences of those primarydisturbances.

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