Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex

The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex

To exploit the high (millisecond) temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) for measuring neuronal dynamics within well-defined brain regions, it is important to quantitatively assess their localizing ability. Previous modeling studies and empirical data suggest that a combination of MEG and EEG signals should yield the most accurate localization, due to their complementary sensitivities. However, these two modalities have rarely been explicitly combined for source estimation in studies of recorded brain activity, and a quantitative empirical assessment of their abilities, combined and separate, is currently lacking. Here we studied early visual responses to focal Gabor patches flashed during subject fixation. MEG and EEG data were collected simultaneously and were compared with the functional MRI (fMRI) localization produced by identical stimuli in the same subjects. This allowed direct evaluation of the localization accuracy of separate and combined MEG/EEG inverse solutions. We found that the localization accuracy of the combined MEG+EEG solution was consistently better than that of either modality alone, using three different source estimation approaches. Further analysis suggests that this improved localization is due to the different properties of the two imaging modalities rather than simply due to increased total channel number. Thus, combining MEG and EEG data is important for high-resolution spatiotemporal studies of the human brain.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

Geometrically accurate topology-correction of cortical surfaces using nonseparating loops

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.

Cortical atrophy is relevant in multiple sclerosis at clinical onset

INTRODUCTION

Increasing evidence suggests relevant cortical gray matter pathology in patients with Multiple Sclerosis (MS), but how early this pathology begins; its impact on clinical disability and which cortical areas are primarily affected needs to be further elucidated.

METHODS

115 consecutive patients (10 Clinically Isolated Syndrome (CIS), 32 possible MS (p-MS), 42 Relapsing Remitting MS (RR-MS), 31 Secondary Progressive MS (SP-MS)), and 40 age/gender-matched healthy volunteers (HV) underwent a neurological examination and a 1.5 T MRI. Global and regional Cortical Thickness (CTh) measurements, brain parenchyma fraction and T2 lesion load were analyzed.

RESULTS

We found a significant global cortical thinning in p-MS (2.22 +/- 0.09 mm), RR-MS (2.16 +/- 0.10 mm) and SP-MS (1.98 +/- 0.11 mm) compared to CIS (2.51 +/- 0.11 mm) and HV (2.48 +/- 0.08 mm). The correlations between mean CTh and white matter (WM) lesion load was only moderate in MS (r = -0.393, p = 0.03) and absent in p-MS (r = -0.147, p = 0.422). Analysis of regional CTh revealed that the majority of cortical areas were involved not only in MS, but also in p-MS. The type of clinical picture at onset (in particular, pyramidal signs/symptoms and optic neuritis) correlated with atrophy in the corresponding cortical areas.

DISCUSSION

Cortical thinning is a diffuse and early phenomenon in MS already detectable at clinical onset. It correlates with clinical disability and is partially independent from WM inflammatory pathology.

Determination of hemispheric emotional valence in individual subjects: a new approach with research and therapeutic implications

BACKGROUND

Much has been theorized about the emotional properties of the hemispheres. Our review of the dominant hypotheses put forth by Schore, Joseph, Davidson, and Harmon-Jones on hemispheric emotional valences (HEV) shows that none are supported by robust data. Instead, we propose that individual's hemispheres are organized to have differing HEVs that can be lateralized in either direction.

METHODS

Probe auditory evoked potentials (AEP) recorded during a neutral and an upsetting memory were used to assess HEV in 28 (20 F) right-handed subjects who were either victims of childhood maltreatment (N = 12) or healthy controls. In a sub-population, we determined HEV by emotional response to lateral visual field stimulation (LVFS), in which vision is limited to one, then the other hemifield. We compare a number of morphometric and functional brain measures between individuals who have right-negative versus left-negative HEV.

RESULTS

Using AEPs to determine HEV, we found 62% of controls and 67% of maltreated subjects had right negative HEV. There was a strong interaction between HEV-laterality and gender, which together accounted for 60% of individual variability in total grey matter volume (GMV). HEV-laterality was associated with differences in hippocampal volume, amygdala/hippocampal ratios, and measures of verbal, visual and global memory. HEV-laterality was associated also with different constellations of symptoms comparing maltreated subjects to controls. Emotional response to LVFS provided a convenient and complementary measure of HEV-laterality that correlated significantly with the HEVs determined by AEPs.

CONCLUSION

Our findings suggest that HEV-laterality, like handedness or gender, is an important individual difference with significant implications for brain and behavioral research, and for guiding lateralized treatments such as rTMS.

Neuroimaging H.M.: a 10-year follow-up examination

In 1997, Corkin et al. described the anatomical boundaries of the amnesic patient H.M.'s surgical resection, based on a comprehensive analysis of magnetic resonance imaging (MRI) scans collected in 1992 and 1993 (Corkin et al. (1997) J Neurosci 17:3964-3979). We subsequently scanned H.M. on several occasions, employing more advanced data acquisition and analysis methods, and now describe additional details about his brain anatomy and pathology. This account combines results from high-resolution T1-weighted scans, which provide measures of cortical and subcortical morphometry, diffusion tensor images, which provide quantitative information about white matter microstructure and the anatomy of major fasciculi, and T2-weighted images, which highlight damage to deep white matter. We applied new MRI analysis techniques to these scans to assess the integrity of areas throughout H.M.'s brain. We documented a number of new changes, including cortical thinning, atrophy of deep gray matter structures, and a large volume of abnormal white matter and deep gray matter signal. Most of these alterations were not apparent in his prior scans, suggesting that they are of recent origin. Advanced age and hypertension likely contributed to these new findings.

Distribution of microstructural damage in the brains of professional boxers: a diffusion MRI study

PURPOSE

To investigate and localize cerebral abnormalities in professional boxers with no history of moderate or severe head trauma.

MATERIALS AND METHODS

Diffusion tensor imaging (DTI) was used to determine the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in the brains of 81 professional male boxers and 12 male control subjects. Voxel-based analysis (VBA) of both the diffusion and anisotropy values was performed using statistical parametric mapping (SPM). From this objective analysis, regions of microstructural abnormalities in the brains of the boxers were located.

RESULTS

Increases in the ADC, and decreases in FA were identified in deep white matter (WM), while decreases in ADC were identified in cortical gray matter (GM). Regions of positive correlation between ADC and age were also found in both the boxer and control groups, although the regions and strength of the correlation were not the same in each group.

CONCLUSION

Using VBA, we localized previously unreported abnormalities in the brains of professional boxers. These abnormalities are assumed to reflect cumulative (chronic) brain injury resulting from nonsevere head trauma.

Neuroanatomical correlates of personality in the elderly

Extraversion and neuroticism are two important and frequently studied dimensions of human personality. They describe individual differences in emotional responding that are quite stable across the adult lifespan. Neuroimaging research has begun to provide evidence that neuroticism and extraversion have specific neuroanatomical correlates within the cerebral cortex and amygdala of young adults. However, these brain areas undergo alterations in size with aging, which may influence the nature of these personality factor-brain structure associations in the elderly. One study in the elderly demonstrated associations between perisylvian cortex structure and measures of self transcendence [Kaasinen, V., Maguire, R.P., Kurki, T., Bruck, A., Rinne, J.O., 2005. Mapping brain structure and personality in late adulthood. NeuroImage 24, 315-322], but the neuroanatomical correlates of extraversion and neuroticism, or other measures of the Five Factor Model of personality have not been explored. The purpose of the present study was to investigate the structural correlates of neuroticism and extraversion in healthy elderly subjects (n=29) using neuroanatomic measures of the cerebral cortex and amygdala. We observed that the thickness of specific lateral prefrontal cortex (PFC) regions, but not amygdala volume, correlates with measures of extraversion and neuroticism. The results suggest differences in the regional neuroanatomic correlates of specific personality traits with aging. We speculate that this relates to the influences of age-related structural changes in the PFC.

Geometry driven volumetric registration

In this paper, we propose a novel method for the registration of volumetric images of the brain that attempts to maximize the overlap of cortical folds. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and is diffused throughout the volume using the Navier operator of elasticity. The result is a volumetric warp that aligns the folding patterns.

Eye dominance predicts fMRI signals in human retinotopic cortex

There have been many attempts to define eye dominance in normal subjects, but limited consensus exists, and relevant physiological data is scarce. In this study, we consider two different behavioral methods for assignment of eye dominance, and how well they predict fMRI signals evoked by monocular stimulation. Sighting eye dominance was assessed with two standard tests, the Porta Test, and a 'hole in hand' variation of the Miles Test. Acuity dominance was tested with a standard eye chart and with a computerized test of grating acuity. We found limited agreement between the sighting and acuity methods for assigning dominance in our individual subjects. We then compared the fMRI response generated by dominant eye stimulation to that generated by non-dominant eye, according to both methods, in 7 normal subjects. The stimulus consisted of a high contrast hemifield stimulus alternating with no stimulus in a blocked paradigm. In separate scans, we used standard techniques to label the borders of visual areas V1, V2, V3, VP, V4v, V3A, and MT. These regions of interest (ROIs) were used to analyze each visual area separately. We found that percent change in fMRI BOLD signal was stronger for the dominant eye as defined by the acuity method, and this effect was significant for areas located in the ventral occipital territory (V1v, V2v, VP, V4v). In contrast, assigning dominance based on sighting produced no significant interocular BOLD differences. We conclude that interocular BOLD differences in normal subjects exist, and may be predicted by acuity measures.

Making sense of discourse: An fMRI study of causal inferencing across sentences

To build up coherence between sentences (comprehend discourse), we must draw inferences, i.e. activate and integrate information that is not actually stated. We used event-related fMRI to determine the localization and extent of brain activity mediating causal inferencing across short, three-sentence scenarios. Participants read and made causal coherence judgments to sentences that were highly causally related, intermediately related or unrelated to their preceding two-sentence contexts. The highly related and intermediately related scenarios were matched in terms of semantic similarities between their individual component words. A pre-rating study established that causal inferences were generated to the intermediately related but not to the highly related or unrelated scenarios. In the scanner, sentences that were intermediately related (relative to highly related or unrelated) to their preceding contexts were associated with longer judgment reaction times and sustained increases in hemodynamic activity within left lateral temporal/inferior parietal/prefrontal cortices, the right inferior prefrontal gyrus and bilateral superior medial prefrontal cortices. In contrast, sentences that were unrelated (relative to highly related) to their preceding contexts were associated with only transient increases in activity (at, but not after, the peak of the hemodynamic response) within the right lateral temporal cortex and the right inferior prefrontal gyrus. These data suggest that, to make sense of discourse, we activate a large bilateral cortical network in response to what is not explicitly stated. We suggest that this network reflects the activation, retrieval and integration of information from long-term semantic memory into incoming discourse structure during causal inferencing.

Deformable shape finding with models based on kernel methods

In this paper, a new kernel-based deformable model is proposed for detecting deformable shapes. To incorporate valuable information for shape detection, such as edge orientations into the shape representation, a novel scheme based on kernel methods has been utilized. The variation model of a deformable shape is established by a set of training samples of the shape represented in a kernel feature space. The proposed deformable model consists of two parts: a set of basis vectors describing the sample subspace, including the shape representations of the training samples, and a feasibility constraint generated by the one-class support vector machine to describe the feasible region of the training samples in the sample subspace. The aim of the proposed feasibility constraint is to avoid finding some invalid shapes. By using the proposed deformable model, an efficient algorithm without initial solutions is developed for shape detection. The proposed approach was tested against real images. Experimental results show the effectiveness of the proposed deformable model and prove the feasibility of the proposed approach.

Fast Talairach Transformation for magnetic resonance neuroimages

We introduce and validate the Fast Talairach Transformation (FTT). FTT is a rapid version of the Talairach transformation (TT) with the modified Talairach landmarks. Landmark identification is fully automatic and done in 3 steps: calculation of midsagittal plane, computing of anterior commissure (AC) and posterior commissure (PC) landmarks, and calculation of cortical landmarks. To perform these steps, we use fast and anatomy-based algorithms employing simple operations. FTT was validated for 215 diversified T1-weighted and spoiled gradient recalled (SPGR) MRI data sets. It calculates the landmarks and warps the Talairach-Tournoux atlas fully automatically in about 5 sec on a standard computer. The average distance errors in landmark localization are (in mm): 1.16 (AC), 1.49 (PC), 0.08 (left), 0.13 (right), 0.48 (anterior), 0.16 (posterior), 0.35 (superior), and 0.52 (inferior). Extensions to FTT by introducing additional landmarks and applying nonlinear warping against the ventricular system are addressed. Application of FTT to other brain atlases of anatomy, function, tracts, cerebrovasculature, and blood supply territories is discussed. FTT may be useful in a clinical setting and research environment: (1) when the TT is used traditionally, (2) when a global brain structure positioning with quick searching and labeling is required, (3) in urgent cases for quick image interpretation (eg, acute stroke), (4) when the difference between nonlinear and piecewise linear warping is negligible, (5) when automatic processing of a large number of cases is required, (6) as an initial atlas-scan alignment before performing nonlinear warping, and (7) as an initial atlas-guided segmentation of brain structures before further local processing.

Abstract grammatical processing of nouns and verbs in Broca's area: evidence from fMRI

The role of Broca's area in grammatical computation is unclear, because syntactic processing is often confounded with working memory, articulation, or semantic selection. Morphological processing potentially circumvents these problems. Using event-related functional magnetic resonance imaging (fMRI), we had 18 subjects silently inflect words or read them verbatim. Subtracting the activity pattern for reading from that for inflection, which indexes processes involved in inflection (holding constant lexical processing and articulatory planning) highlighted left Brodmann area (BA) 44/45 (Broca's area), BA 47, anterior insula, and medial supplementary motor area. Subtracting activity during zero inflection (the hawk; they walk) from that during overt inflection (the hawks; they walked), which highlights manipulation of phonological content, implicated subsets of the regions engaged by inflection as a whole. Subtracting activity during verbatim reading from activity during zero inflection (which highlights the manipulation of inflectional features) implicated distinct regions of BA 44, 47, and a premotor region (thereby tying these regions to grammatical features), but failed to implicate the insula or BA 45 (thereby tying these to articulation). These patterns were largely similar in nouns and verbs and in regular and irregular forms, suggesting these regions implement inflectional features cutting across word classes. Greater activity was observed for irregular than regular verbs in the anterior cingulate and supplementary motor area (SMA), possibly reflecting the blocking of regular or competing irregular candidates. The results confirm a role for Broca's area in abstract grammatical processing, and are interpreted in terms of a network of regions in left prefrontal cortex (PFC) that are recruited for processing abstract morphosyntactic features and overt morphophonological content.

Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

Estimating linear cortical magnification in human primary visual cortex via dynamic programming

Human primary visual cortex is organized retinotopically, with adjacent locations in cortex representing adjacent locations on the retina. The spatial sampling in cortex is highly nonuniform: the amount of cortex devoted to a unit area of retina decreases with increasing retinal eccentricity. This sampling property can be quantified by the linear cortical magnification factor, which is expressed in terms of millimeters of cortex per degree of visual angle. In this paper, we present a new method using dynamic programming and fMRI retinotopic eccentricity mapping to estimate the linear cortical magnification factor in human primary visual cortex (V1). We localized cortical activity while subjects viewed each of seven stationary contrast- reversing radial checkerboard rings of equal thickness that tiled the visual field from 1.62 to 12.96 degrees of eccentricity. Imaging data from all epochs of each ring were contrasted with data from fixation epochs on a subject-by-subject basis. The resulting t statistic maps were then superimposed on a local coordinate system constructed from the gray/white matter boundary surface of each individual subject's occipital lobe, separately for each ring. Smoothed maps of functional activity on the cortical surface were constructed using orthonormal bases of the Laplace-Beltrami operator that incorporate the geometry of the cortical surface. This allowed us to stably track the ridge of maximum activation due to each ring via dynamic programming optimization over all possible paths on the cortical surface. We estimated the linear cortical magnification factor by calculating geodesic distances between activation ridges on the cortical surface in a population of five normal subjects. The reliability of these estimates was assessed by comparing results based on data from one quadrant to those based on data from the full hemifield along with a split-half reliability analysis.

Regional cortical thickness matters in recall after months more than minutes

The aim of this study was to determine the role of regional cortical thickness in recall of verbal material over an extended time period. MRI scans of healthy adults of varying ages were obtained. Two scans were averaged per person to achieve high spatial resolution, and a semi-automated method for continuous measurement of thickness across the entire cortical mantle was employed. Verbal memory tests assessing recall after 5 min, 30 min, and a mean interval of 83 days were administered. A general linear model (GLM) of the effects of thickness at each vertex on the different memory indices was computed, controlling for gender, age, IQ, and intracranial volume. These analyses were repeated with hippocampal volume as an additional variable to be controlled for, to assess to which extent effects of cortical thickness were independent of hippocampal size. Minute effects of cortical thickness were observed with regard to shorter time intervals (5 and 30 min). However, even when controlling for the effects of hippocampal volume, higher recall across months was associated with thicker cortex of distinct areas including parts of the gyrus rectus, the middle frontal gyrus, the parieto-occipital sulcus and the lingual gyrus of both hemispheres. In addition, hemisphere-specific associations were found in parts of the right temporal and parietal lobe as well as parts of the left precuneus. This supports a unique and critical role of the thickness of distinct cortical areas in recall after months, more than after minutes.

Processing efficiency of divided spatial attention mechanisms in human visual cortex

Many visual tasks require deployment of attention to multiple objects or locations. We used functional magnetic resonance imaging and behavioral experiments to investigate the relative processing efficiency of two putative attentional mechanisms for performing such tasks: the "zoom lens" and "multiple spotlights." Two key questions were investigated: (1) does splitting the spotlight into multiple foci incur an overhead cost that diminishes the efficacy of attention compared with the zoom lens, and (2) does splitting the spotlight provide a benefit relative to the zoom lens by conserving attention resources that otherwise would be directed to task irrelevant stimuli? For both mechanisms, attending to multiple object locations decreased processing efficiency at a single location, resulting in both decreased behavioral performance and decreased blood oxygenation level-dependent (BOLD) signal attentional modulation. When the two mechanisms attended to multiple objects across the same spatial extent, the multiple spotlight mechanism, which ignores intervening stimuli, yielded better performance and higher BOLD signal. When the two mechanisms processed the same number of stimuli, splitting the spotlight neither impaired performance nor diminished BOLD signal in occipital cortex. The surprising efficiency of the multiple spotlight mechanism supports the emerging view that spatial attention is easily deployed in a diverse range of spatial configurations.

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates

Cerebral currents responsible for the extra-cranially recorded magnetoencephalography (MEG) data can be estimated by applying a suitable source model. A popular choice is the distributed minimum-norm estimate (MNE) which minimizes the l2-norm of the estimated current. Under the l2-norm constraint, the current estimate is related to the measurements by a linear inverse operator. However, the MNE has a bias towards superficial sources, which can be reduced by applying depth weighting. We studied the effect of depth weighting in MNE using a shift metric. We assessed the localization performance of the depth-weighted MNE as well as depth-weighted noise-normalized MNE solutions under different cortical orientation constraints, source space densities, and signal-to-noise ratios (SNRs) in multiple subjects. We found that MNE with depth weighting parameter between 0.6 and 0.8 showed improved localization accuracy, reducing the mean displacement error from 12 mm to 7 mm. The noise-normalized MNE was insensitive to depth weighting. A similar investigation of EEG data indicated that depth weighting parameter between 2.0 and 5.0 resulted in an improved localization accuracy. The application of depth weighting to auditory and somatosensory experimental data illustrated the beneficial effect of depth weighting on the accuracy of spatiotemporal mapping of neuronal sources.

fMRI Measures of Perceptual Filling-in in the Human Visual Cortex

Filling-in refers to the tendency of stabilized retinal stimuli to fade and become replaced by their background. This phenomenon is a good example of central brain mechanisms that can selectively add or delete information to/from the retinal input. Importantly, such cortical mechanisms may overlap with those that are used more generally in visual perception. In order to identify cortical areas that contribute to perceptual filling-in, we used functional magnetic resonance imaging to image activity in the visual cortex while subjects experienced filling-in. Nine subjects viewed an achromatic disc with slightly higher luminance than the background and indicated the presence or absence of filling-in by a keypress. The disc was placed in either the upper or lower left quadrant. Similar high-contrast stimuli were used to map out the retinotopic representation of the disc. Unexpectedly, the lower-field high-contrast stimulus produced more parietal cortex activation than the upper-field condition, indicating preferential representation of the lower field by attentional control mechanisms. During perceptual filling-in, we observed significant contralateral reductions in activation in lower-tier retinotopic areas V1 and V2. In contrast, increased activation was consistently observed in visual areas V3A and V4v, higher-level cortex in the intraparietal sulcus, posterior superior temporal sulcus, and the ventral occipital–temporal region, as well as the pulvinar. The filling-in activation pattern was remarkably similar for both the upper- and lower-field conditions. Behaviorally, filling-in was reported to be easier for the lower visual field, and filling-in periods were longer for the lower than the upper quadrant. We suggest this behavioral asymmetry may be partially due to the preferential parietal representation of the lower field. The results lead us to propose that perceptual filling-in recruits high-level control mechanisms to reconcile competing percepts, and alters the normal image-related signals at the first stages of cortical processing. Moreover, the overall pattern of activation during filling-in resembles that seen in other studies of perceptually bistable stimuli, including binocular rivalry, indicating common control mechanisms.

Location and spatial profile of category-specific regions in human extrastriate cortex

Subjects were scanned in a single functional MRI (fMRI) experiment that enabled us to localize cortical regions in each subject in the occipital and temporal lobes that responded significantly in a variety of contrasts: faces>objects, body parts>objects, scenes>objects, objects>scrambled objects, and moving>stationary stimuli. The resulting activation maps were co-registered across subjects using spherical surface coordinates [Fischl et al., Hum Brain Mapp 1999;8:272-284] to produce a "percentage overlap map" indicating the percentage of subjects who showed a significant response for each contrast at each point on the surface. Prominent among the overlapping activations in these contrasts were the fusiform face area (FFA), extrastriate body area (EBA), parahippocampal place area (PPA), lateral occipital complex (LOC), and MT+/V5; only a few other areas responded consistently across subjects in these contrasts. Another analysis showed that the spatial profile of the selective response drops off quite sharply outside the standard borders of the FFA and PPA (less so for the EBA and MT+/V5), indicating that these regions are not simply peaks of very broad selectivities spanning centimeters of cortex, but fairly discrete regions of cortex with distinctive functional profiles. The data also yielded a surprise that challenges our understanding of the function of area MT+: a higher response to body parts than to objects. The anatomical consistency of each of our functionally defined regions across subjects and the spatial sharpness of their activation profiles within subjects highlight the fact that these regions constitute replicable and distinctive landmarks in the functional organization of the human brain.

Distributed current estimates using cortical orientation constraints

Distributed source models of magnetoencephalographic (MEG) and electroencephalographic (EEG) data employ dense distributions of current sources in a volume or on a surface. Previously, anatomical magnetic resonance imaging (MRI) data have been used to constrain locations and orientations based on cortical geometry extracted from anatomical MRI data. We extended this approach by first calculating cortical patch statistics (CPS), which for each patch corresponding to a current source location on the cortex comprise the area of the patch, the average normal direction, and the average deviation of the surface normal from its average. The patch areas were then incorporated in the forward model to yield estimates of the surface current density instead of dipole amplitudes at the current locations. The surface normal data were employed in a loose orientation constraint (LOC), which allows some variation of the current direction from the average normal. We employed this approach both in the l(2) minimum-norm estimates (MNE) and in the more focal l(1) minimum-norm solutions, the minimum-current estimate (MCE). Simulations in auditory and somatosensory areas with current dipoles and 10- or 20-mm diameter cortical patches as test sources showed that applying the LOC can increase localization accuracy. We also applied the method to in vivo auditory and somatosensory data.

Simplified intersubject averaging on the cortical surface using SUMA

Task and group comparisons in functional magnetic resonance imaging (fMRI) studies are often accomplished through the creation of intersubject average activation maps. Compared with traditional volume-based intersubject averages, averages made using computational models of the cortical surface have the potential to increase statistical power because they reduce intersubject variability in cortical folding patterns. We describe a two-step method for creating intersubject surface averages. In the first step cortical surface models are created for each subject and the locations of the anterior and posterior commissures (AC and PC) are aligned. In the second step each surface is standardized to contain the same number of nodes with identical indexing. An anatomical average from 28 subjects created using the AC-PC technique showed greater sulcal and gyral definition than the corresponding volume-based average. When applied to an fMRI dataset, the AC-PC method produced greater maximum, median, and mean t-statistics in the average activation map than did the volume average and gave a better approximation to the theoretical-ideal average calculated from individual subjects. The AC-PC method produced average activation maps equivalent to those produced with surface-averaging methods that use high-dimensional morphing. In comparison with morphing methods, the AC-PC technique does not require selection of a template brain and does not introduce deformations of sulcal and gyral patterns, allowing for group analysis within the original folded topology of each individual subject. The tools for performing AC-PC surface averaging are implemented and freely available in the SUMA software package.

Regional cerebral cortical thinning in bipolar disorder

OBJECTIVE

This study was conducted to explore differences in cortical thickness between subjects with bipolar disorder and healthy comparison subjects using cortical surface-based analysis.

METHODS

Brain magnetic resonance images were acquired from 25 subjects with bipolar disorder and 21 healthy comparison subjects. Cortical surface-based analysis was conducted using the Freesurfer application. Group differences in cortical thickness, defined by the distance from gray/white boundary to the pial surface, were assessed using statistical difference maps.

RESULTS

Subjects with bipolar disorder exhibited significantly decreased cortical thickness in left cingulate cortex, left middle frontal cortex, left middle occipital cortex, right medial frontal cortex, right angular cortex, right fusiform cortex and bilateral postcentral cortices, relative to healthy comparison subjects (all p < 0.001). Duration of illness in bipolar subjects was inversely correlated with the cortical thickness of the left middle frontal cortex.

CONCLUSIONS

Cortical thinning was present in multiple prefrontal cortices in bipolar disorder. There was also cortical thinning in sensory and sensory association cortices, which has not been reported in previous studies using region-of-interest or voxel-based morphometry methods. Cortical thinning observed in the current study may be related to impairment of emotional, cognitive, and sensory processing in bipolar disorder but longitudinal studies will be necessary to test this hypothesis.

Determinants of Bold Signal Correlates of Processing Object-Extracted Relative Clauses

Event-related functional magnetic resonance imaging (fMRI) was used to investigate the determinants of blood oxygenation level-dependent (BOLD) signal correlates of processing relative clauses. Matched pairs of sentences that differed in their processing demands were compared. One member of the pair consisted of a syntactically simpler object-subject (OS) sentence, containing a subject-relativized clause attached to the object noun phrase. The second member of the pair consisted of a syntactically more complex subject-object (SO) sentence, containing an object-relativized clause attached to the subject noun phrase. Participants made plausibility judgments about the sentences in whole sentence visual presentation. Voxel-wise statistical activation maps showed increased BOLD signal in multiple cortical regions for complex compared to simple syntactic structures. This pattern was found for plausible sentences only and, within the set of plausible sentences, for SO sentences in which the head noun of the relative clause was animate and the subject noun of the relative clause was inanimate. These results require a re-interpretation of previous results with the same materials using positron emission tomography.

Automated Topology Correction for Human Brain Segmentation

We describe a new method to reconstruct human brain structures from 3D magnetic resonance brain images. Our method provides a fully automatic topology correction mechanism, thus avoiding tedious manual correction. Topological correctness is important because it is an essential prerequisite for brain atlas deformation and surface flattening. Our method uses an axis-aligned sweep through the volume to locate handles. Handles are detected by successively constructing and analyzing a directed graph. A multiple local region-growing process is used which simultaneously acts on the foreground and the background to isolate handles and tunnels. The sizes of handles and tunnels are measured, then handles are removed or tunnels filled based on their sizes. This process was used for 256 T1-weighted MR volumes.

Segmenting magnetic resonance images via hierarchical mixture modelling

We present a statistically innovative as well as scientifically and practically relevant method for automatically segmenting magnetic resonance images using hierarchical mixture models. Our method is a general tool for automated cortical analysis which promises to contribute substantially to the science of neuropsychiatry. We demonstrate that our method has advantages over competing approaches on a magnetic resonance brain imagery segmentation task.

Ecphory of autobiographical memories: an fMRI study of recent and remote memory retrieval

Ecphory occurs when one recollects a past event cued by a trigger, such as a picture, odor, or name. It is a central component of autobiographical memory, which allows us to "travel mentally back in time" and re-experience specific events from our personal past. Using fMRI and focusing on the role of medial temporal lobe (MTL) structures, we investigated the brain bases of autobiographical memory and whether they change with the age of memories. Importantly, we used an ecphory task in which the remote character of the memories was ensured. The results showed that a large bilateral network supports autobiographical memory: temporal lobe, temporo-parieto-occipital junction, dorsal prefrontal cortex, medial frontal cortex, retrosplenial cortex and surrounding areas, and MTL structures. This network, including MTL structures, changed little with the age of the memories.

Rostral and dorsal anterior cingulate cortex make dissociable contributions during antisaccade error commission

The anterior cingulate cortex (ACC) participates in both performance optimization and evaluation, with dissociable contributions from dorsal (dACC) and rostral (rACC) regions. Deactivation in rACC and other default-mode regions is important for performance optimization, whereas increased rACC and dACC activation contributes to performance evaluation. Errors activate both rACC and dACC. We propose that this activation reflects differential error-related involvement of rACC and dACC during both performance optimization and evaluation, and that these two processes can be distinguished by the timing of their occurrence within a trial. We compared correct and error antisaccade trials. We expected errors to correlate with an early failure of rACC deactivation and increased activation of both rACC and dACC later in the trial. Eighteen healthy subjects performed a series of prosaccade and antisaccade trials during event-related functional MRI. We estimated the hemodynamic responses for error and correct antisaccades using a finite impulse-response model. We examined ACC activity by comparing error and correct antisaccades with a fixation baseline and error to correct antisaccades directly. Compared with correct antisaccades, errors were characterized by an early bilateral failure of deactivation of rACC and other default-mode regions. This difference was significant in rACC. Errors also were associated with increased activity in both rACC and dACC later in the trial. These results show that accurate performance involves deactivation of the rACC and other default mode regions and suggest that both rACC and dACC contribute to the evaluation of error responses.

Genetics of brain structure and intelligence

Genetic influences on brain morphology and IQ are well studied. A variety of sophisticated brain-mapping approaches relating genetic influences on brain structure and intelligence establishes a regional distribution for this relationship that is consistent with behavioral studies. We highlight those studies that illustrate the complex cortical patterns associated with measures of cognitive ability. A measure of cognitive ability, known as g, has been shown highly heritable across many studies. We argue that these genetic links are partly mediated by brain structure that is likewise under strong genetic control. Other factors, such as the environment, obviously play a role, but the predominant determinant appears to be genetic.

Computational biology for visualization of brain structure

The complexity and variability of human brain (as well as other species) across subjects is so great that reliance on maps and atlases is essential to effectively manipulate, analyze and interpret brain data. Central to these tasks is the construction of averages, templates and models to describe how the brain and its component parts are organized. Design of appropriate reference systems and visualization strategies for human brain data presents considerable challenges, since these systems must capture how brain structure and function vary in large populations, across age and gender, in different disease states, across imaging modalities and even across species. This paper will describe the application of brain maps to a variety of questions and problems in health and disease. It includes a brief survey of different types of maps, including those that capture dynamic patterns of brain change over time.

Application of Magnetoencephalography in Epilepsy Patients with Widespread Spike or Slow-wave Activity

PURPOSE

To examine whether magnetoencephalography (MEG) can be used to determine patterns of brain activity underlying widespread paroxysms of epilepsy patients, thereby extending the applicability of MEG to a larger population of epilepsy patients.

METHODS

We studied two children with symptomatic localization-related epilepsy. Case 1 had widespread spikes in EEG with an operation scar from a resection of a brain tumor; Case 2 had hemispheric slow-wave activity in EEG with sensory auras. MEG was collected with a 204-channel helmet-shaped sensor array. Dynamic statistical parametric maps (dSPMs) were constructed to estimate the cortical distribution of interictal discharges for these patients. Equivalent current dipoles (ECDs) also were calculated for comparison with the results of dSPM.

RESULTS

In case 1 with widespread spikes, dSPM presented the major activity at the vicinity of the operation scar in the left frontal lobe at the peak of the spikes, and some activities were detected in the left temporal lobe just before the peak in some spikes. In case 2 with hemispheric slow waves, the most active area was located in the left parietal lobe, and additional activity was seen at the ipsilateral temporal and frontal lobes in dSPM. The source estimates correlated well with the ictal manifestation and interictal single-photon emission computed tomography (SPECT) findings for this patient. In comparison with the results of ECDs, ECDs could not express a prior activity at the left temporal lobe in case 1 and did not model well the MEG data in case 2.

CONCLUSIONS

We suggest that by means of dSPM, MEG is useful for presurgical evaluation of patients, not only with localized epileptiform activity, but also with widespread spikes or slow waves, because it requires no selections of channels and no time-point selection.

Thickness of ventromedial prefrontal cortex in humans is correlated with extinction memory

The ventromedial prefrontal cortex (vmPFC) has been implicated in fear extinction [Phelps, E. A., Delgado, M. R., Nearing, K. I. & Ledoux, J. E. (2004) Neuron 43, 897-905; Herry, C. & Garcia, R. (2003) Behav. Brain Res. 146, 89-96]. Here, we test the hypothesis that the cortical thickness of vmPFC regions is associated with how well healthy humans retain their extinction memory a day after having been conditioned and then extinguished. Fourteen participants underwent a 2-day fear conditioning and extinction protocol. The conditioned stimuli (CSs) were pictures of virtual lights, and the unconditioned stimulus (US) was an electric shock. On day 1, participants received 5 CS+US pairings (conditioning), followed by 10 CS trials with no US (extinction). On day 2, the CS was presented alone to test for extinction memory. Skin conductance response (SCR) was the behavioral index of conditioning and extinction. Participants underwent MRI scans to obtain structural images, from which cortical thickness was measured. We performed a vertex-based analysis across the entire cortical surface and a region-of-interest analysis of a priori hypothesized territories to measure cortical thickness and map correlations between this measure and SCR. We found significant, direct correlation between thickness of the vmPFC, specifically medial orbitofrontal cortex, and extinction retention. That is, thicker medial orbitofrontal cortex was associated with lower SCR to the conditioned stimulus during extinction recall (i.e., greater extinction memory). These results suggest that the size of the vmPFC might explain individual differences in the ability to modulate fear among humans.

Visualizing human brain surface from T1-weighted MR images using texture-mapped triangle meshes

We describe a novel method for visualizing brain surface from anatomical magnetic resonance images (MRIs). The method utilizes standard 2D texture mapping capabilities of OpenGL graphics language. It combines the benefits of volume rendering and triangle-mesh rendering, allowing fast and realistic-looking brain surface visualizations. Consequently, relatively low-resolution triangle meshes can be used while the texture images provide the necessary details. The mapping is optimized to provide good texture-image resolution for the triangles with respect to their original sizes in the 3D MRI volume. The actual 2D texture images are generated by depth integration from the original MRI data. Our method adapts to anisotropic voxel sizes without any need to interpolate the volume data into cubic voxels, and it is very well suited for visualizing brain anatomy from standard T(1)-weighted MR images. Furthermore, other OpenGL objects and techniques can be easily combined, for example, to use cut planes, to show other surfaces and objects, and to visualize functional data in addition to the anatomical information.

A stochastic model for studying the laminar structure of cortex from MRI

The human cerebral cortex is a laminar structure about 3 mm thick, and is easily visualized with current magnetic resonance (MR) technology. The thickness of the cortex varies locally by region, and is likely to be influenced by such factors as development, disease and aging. Thus, accurate measurements of local cortical thickness are likely to be of interest to other researchers. We develop a parametric stochastic model relating the laminar structure of local regions of the cerebral cortex to MR image data. Parameters of the model include local thickness, and statistics describing white, gray and cerebrospinal fluid (CSF) image intensity values as a function of the normal distance from the center of a voxel to a local coordinate system anchored at the gray/white matter interface. Our fundamental data object, the intensity-distance histogram (IDH), is a two-dimensional (2-D) generalization of the conventional 1-D image intensity histogram, which indexes voxels not only by their intensity value, but also by their normal distance to the gray/white interface. We model the IDH empirically as a marked Poisson process with marking process a Gaussian random field model of image intensity indexed against normal distance. In this paper, we relate the parameters of the IDH model to the local geometry of the cortex. A maximum-likelihood framework estimates the parameters of the model from the data. Here, we show estimates of these parameters for 10 volumes in the posterior cingulate, and 6 volumes in the anterior and posterior banks of the central sulcus. The accuracy of the estimates is quantified via Cramer-Rao bounds. We believe that this relatively crude model can be extended in a straightforward fashion to other biologically and theoretically interesting problems such as segmentation, surface area estimation, and estimating the thickness distribution in a variety of biologically relevant contexts.

Dynamic statistical parametric mapping for analyzing the magnetoencephalographic epileptiform activity in patients with epilepsy

Our current purpose is to evaluate the applicability of dynamic statistical parametric mapping, a novel method for localizing epileptiform activity recorded with magnetoencephalography in patients with epilepsy. We report four pediatric patients with focal epilepsies. Magnetoencephalographic data were collected with a 306-channel whole-head helmet-shaped sensor array. We calculated equivalent current dipoles and dynamic statistical parametric mapping movies of the interictal epileptiform discharges that were based in the minimum-L2 norm estimate, minimizing the square sum of the dipole element amplitudes. The dynamic statistical parametric mapping analysis of interictal epileptiform discharges can demonstrate the rapid change and propagation of interical epileptiform discharges. According to these findings, specific epileptogenic lesion-focal cortical dysplasia could be found and patients could be operated on successfully. The presurgical analysis of interictal epileptiform discharges using dynamic statistical parametric mapping seems to be promising in patients with a possible underlying focal cortical dysplasia and might help to guide the placement of invasive electrodes.

Brain structural mapping using a novel hybrid implicit/explicit framework based on the level-set method

This paper presents a novel approach to feature-based brain image warping, by using a hybrid implicit/explicit framework, which unifies many prior approaches in a common framework. In the first step, we develop links between image warping and the level-set method, and we formulate the fundamental mathematics required for this hybrid implicit/explicit approach. In the second step, we incorporate the large-deformation models into these formulations, leading to a complete and elegant treatment of anatomical structure matching. In this latest approach, exact matching of anatomy is achieved by comparing the target to the warped source structure under the forward mapping and the source to the warped target structure under the backward mapping. Because anatomy is represented nonparametrically, a path is constructed linking the source to the target structure without prior knowledge of their point correspondence. The final point correspondence is constructed based on the linking path with the minimal energy. Intensity-similarity measures can be naturally incorporated in the same framework as landmark constraints by combining them in the gradient descent body forces. We illustrate the approach with two applications: (1) tensor-based morphometry of the corpus callosum in autistic children; and (2) matching cortical surfaces to measure the profile of cortical anatomic variation. In summary, the new mathematical techniques introduced here contribute fundamentally to the mapping of brain structure and its variation and provide a framework that unites feature and intensity-based image registration techniques.

Question/statement judgments: an fMRI study of intonation processing

We examined changes in fMRI BOLD signal associated with question/statement judgments in an event-related paradigm to investigate the neural basis of processing one aspect of intonation. Subjects made judgments about digitized recordings of three types of utterances: questions with rising intonation (RQ; e.g., "She was talking to her father?"), statements with a falling intonation (FS; e.g., "She was talking to her father."), and questions with a falling intonation and a word order change (FQ; e.g., "Was she talking to her father?"). Functional echo planar imaging (EPI) scans were collected from 11 normal subjects. There was increased BOLD activity in bilateral inferior frontal and temporal regions for RQ over either FQ or FS stimuli. The study provides data relevant to the location of regions responsive to intonationally marked illocutionary differences between questions and statements.

CRUISE: Cortical reconstruction using implicit surface evolution

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

Deformable registration of cortical structures via hybrid volumetric and surface warping

Registration of cortical structures across individuals is a very important step for quantitative analysis of the human brain cortex. This paper presents a method for deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. In the first step, a feature-based volumetric registration algorithm is used to warp a model cortical surface to the individual's space. This step greatly reduces the variation between the model and individual, thus providing a good initialization for the next step of surface warping. In the second step, a surface registration method, based on matching geometric attributes, warps the model surface to the individual. Point correspondences are also established at this step. The attribute vector, as the morphological signature of surface, was designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on both synthesized and real brain data demonstrate the performance of the proposed method in the registration of cortical structures across individuals.

Multiple spotlights of attentional selection in human visual cortex

Spatially directed attention strongly enhances visual perceptual processing. The metaphor of the "spotlight" has long been used to describe spatial attention; however, there has been considerable debate as to whether spatial attention must be unitary or may be split between discrete regions of space. This question was addressed here through functional MR imaging of human subjects as they performed a task that required simultaneous attention to two briefly displayed and masked targets at locations separated by distractor stimuli. These data reveal retinotopically specific enhanced activation in striate and extrastriate visual cortical representations of the two attended stimuli and no enhancement at the intervening representation of distractor stimuli. This finding of two spotlights was obtained within a single cortical hemisphere and across the two hemispheres. This provides direct evidence that spatial attention can select, in parallel, multiple low-level perceptual representations.

A magnetic resonance imaging study of cortical thickness in animal phobia

BACKGROUND

Despite the high prevalence of specific phobia (SP), its neural substrates remain undetermined. Although an initial series of functional neuroimaging studies have implicated paralimbic and sensory cortical regions in the pathophysiology of SP, to date contemporary morphometric neuroimaging methods have not been applied to test specific hypotheses regarding structural abnormalities.

METHODS

Morphometric magnetic resonance imaging (MRI) methods were used to measure regional cortical thickness in 10 subjects with SP (animal type) and 20 healthy comparison (HC) subjects.

RESULTS

Consistent with a priori hypotheses, between-group differences in cortical thickness were found within paralimbic and sensory cortical regions. Specifically, in comparison with the HC group, the SP group exhibited increased cortical thickness in bilateral insular, bilateral pregenual anterior cingulate, and bilateral posterior cingulate cortex as well as left visual cortical regions.

CONCLUSIONS

Taken together, these structural findings parallel results from initial functional imaging studies that implicate paralimbic and sensory cortical regions in the mediating anatomy of SP symptoms. Further research will be necessary to replicate these findings and to determine their specificity as well as their pathophysiologic significance.

Sequence-independent segmentation of magnetic resonance images

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

Dynamic programming generation of boundaries of local coordinatized submanifolds in the neocortex: application to the planum temporale

Dynamic programming is used to define boundaries of cortical submanifolds with focus on the planum temporale (PT) of the superior temporal gyrus (STG), which has been implicated in a variety of neuropsychiatric disorders. To this end, automated methods are used to generate the PT manifold from 10 high-resolution MRI subvolumes ROI masks encompassing the STG. A procedure to define the subvolume ROI masks from original MRI brain scans is developed. Bayesian segmentation is then used to segment the subvolumes into cerebrospinal fluid, gray matter (GM), and white matter (WM). 3D isocontouring using the intensity value at which there is equal probability of GM and WM is used to reconstruct the triangulated graph representing the STG cortical surface, enabling principal curvature at each point on the graph to be computed. Dynamic programming is used to delineate the PT manifold by tracking principal curves from the retro-insular end of the Heschl's gyrus (HG) to the STG, along the posterior STG up to the start of the ramus and back to the retro-insular end of the HG. A coordinate system is then defined on the PT manifold. The origin is defined by the retro-insular end of the HG and the y-axis passes through the point on the posterior STG where the ramus begins. Automated labeling of GM in the STG is robust with L(1) distances between Bayesian and manual segmentation in the range 0.001-0.12 (n = 20). PT reconstruction is also robust with 90% of the vertices of the reconstructed PT within about 1 voxel (n = 20) from semiautomated contours. Finally, the reliability index (based on interrater intraclass correlation) for the surface area derived from repeated reconstructions is 0.96 for the left PT and 0.94 for the right PT, thus demonstrating the robustness of dynamic programming in defining a coordinate system on the PT. It provides a method with potential significance in the study of neuropsychiatric disorders.

Permutation tests for classification: towards statistical significance in image-based studies

Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a non-parametric technique for estimation of statistical significance in the context of discriminative analysis (i.e., training a classifier function to label new examples into one of two groups). Our approach adopts permutation tests, first developed in classical statistics for hypothesis testing, to estimate how likely we are to obtain the observed classification performance, as measured by testing on a hold-out set or cross-validation, by chance. We demonstrate the method on examples of both structural and functional neuroimaging studies.

The LONI Pipeline Processing Environment

The analysis of raw data in neuroimaging has become a computationally entrenched process with many intricate steps run on increasingly larger datasets. Many software packages exist that provide either complete analyses or specific steps in an analysis. These packages often possess diverse input and output requirements, utilize different file formats, run in particular environments, and have limited abilities with certain types of data. The combination of these packages to achieve more sensitive and accurate results has become a common tactic in brain mapping studies but requires much work to ensure valid interoperation between programs. The handling, organization, and storage of intermediate data can prove difficult as well. The LONI Pipeline Processing Environment is a simple, efficient, and distributed computing solution to these problems enabling software inclusion from different laboratories in different environments. It is used here to derive a T1-weighted MRI atlas of the human brain from 452 normal young adult subjects with fully automated processing. The LONI Pipeline Processing Environment's parallel processing efficiency using an integrated client/server dataflow model was 80.9% when running the atlas generation pipeline from a PC client (Acer TravelMate 340T) on 48 dedicated server processors (Silicon Graphics Inc. Origin 3000). The environment was 97.5% efficient when the same analysis was run on eight dedicated processors.

What neural correlates underlie successful encoding and retrieval? A functional magnetic resonance imaging study using a divided attention paradigm

If attention is divided during learning, memory suffers. Nevertheless, individuals can learn information with divided attention. This event-related functional magnetic resonance imaging (fMRI) study (n = 17) investigated what neural processes support (1) learning with divided attention and (2) retrieval of information learned with divided attention. Participants encoded words (Is the word abstract or concrete?) while performing an auditory discrimination task (press a button whenever an auditory pattern changes). The auditory task was easy or hard, depending on the similarity of the patterns. A behavioral study indicated that detailed ("recollective") information was more likely to be present for words encoded with the easy versus the hard concurrent task. Words encoded with the hard versus the easy concurrent task, in contrast, were more likely to rely on less detailed ("familiarity"-based) information. fMRI revealed encoding-related activation in the left prefrontal cortex (PFC) and left hippocampus that was linked to successful memory formation only for items encoded with the easy task. In contrast, activation in the right PFC and left parahippocampal gyrus was linked to successful memory for all items. Thus, successful encoding with the hard concurrent task was supported by a subset of the regions recruited for successful encoding with the easy task. The neural processes recruited for successful retrieval also depended on the encoding condition: The left PFC was disproportionately recruited for retrieval of items encoded with the easy task, whereas the right PFC was disproportionately recruited for retrieval of items encoded with the hard task. These findings may reflect left-sided specialization for recollective memories and right-sided specialization for familiarity-based traces.