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

Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis

Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincaré disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincaré Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect brain surface abnormalities. We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer' s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer' s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling' s T² test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our brain imaging analysis toolset for studying diagnosis and prognosis of AD.

Dance Experience and Associations with Cortical Gray Matter Thickness in the Aging Population

INTRODUCTION

We investigated the effect dance experience may have on cortical gray matter thickness and cognitive performance in elderly participants with and without mild cognitive impairment (MCI).

METHODS

39 cognitively normal and 48 MCI elderly participants completed a questionnaire regarding their lifetime experience with music, dance, and song. Participants identified themselves as either dancers or nondancers. All participants received structural 1.5-tesla MRI scans and detailed clinical and neuropsychological evaluations. An advanced 3D cortical mapping technique was then applied to calculate cortical thickness.

RESULTS

Despite having a trend-level significantly thinner cortex, dancers performed better in cognitive tasks involving learning and memory, such as the California Verbal Learning Test-II (CVLT-II) short delay free recall (p = 0.004), the CVLT-II long delay free recall (p = 0.003), and the CVLT-II learning over trials 1-5 (p = 0.001).

DISCUSSION

Together, these results suggest that dance may result in an enhancement of cognitive reserve in aging, which may help avert or delay MCI.

Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry

Blindness represents a unique model to study how visual experience may shape the development of brain organization. Exploring how the structure of the corpus callosum (CC) reorganizes ensuing visual deprivation is of particular interest due to its important functional implication in vision (e.g., via the splenium of the CC). Moreover, comparing early versus late visually deprived individuals has the potential to unravel the existence of a sensitive period for reshaping the CC structure. Here, we develop a novel framework to capture a complete set of shape differences in the CC between congenitally blind (CB), late blind (LB) and sighted control (SC) groups. The CCs were manually segmented from T1-weighted brain MRI and modeled by 3D tetrahedral meshes. We statistically compared the combination of local area and thickness at each point between subject groups. Differences in area are found using surface tensor-based morphometry; thickness is estimated by tracing the streamlines in the volumetric harmonic field. Group differences were assessed on this combined measure using Hotelling's T(2) test. Interestingly, we observed that the total callosal volume did not differ between the groups. However, our fine-grained analysis reveals significant differences mostly localized around the splenium areas between both blind groups and the sighted group (general effects of blindness) and, importantly, specific dissimilarities between the LB and CB groups, illustrating the existence of a sensitive period for reorganization. The new multivariate statistics also gave better effect sizes for detecting morphometric differences, relative to other statistics. They may boost statistical power for CC morphometric analyses.

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy

BACKGROUND

This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe.

METHOD

We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning.

RESULTS

A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%).

CONCLUSIONS

Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.

Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1 -weighted images were processed using three different surface-based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT-cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT-cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples.

Abnormal hippocampal morphology in dissociative identity disorder and post-traumatic stress disorder correlates with childhood trauma and dissociative symptoms

Smaller hippocampal volume has been reported in individuals with post-traumatic stress disorder (PTSD) and dissociative identity disorder (DID), but the regional specificity of hippocampal volume reductions and the association with severity of dissociative symptoms and/or childhood traumatization are still unclear. Brain structural magnetic resonance imaging scans were analyzed for 33 outpatients (17 with DID and 16 with PTSD only) and 28 healthy controls (HC), all matched for age, sex, and education. DID patients met criteria for PTSD (PTSD-DID). Hippocampal global and subfield volumes and shape measurements were extracted. We found that global hippocampal volume was significantly smaller in all 33 patients (left: 6.75%; right: 8.33%) compared with HC. PTSD-DID (left: 10.19%; right: 11.37%) and PTSD-only with a history of childhood traumatization (left: 7.11%; right: 7.31%) had significantly smaller global hippocampal volume relative to HC. PTSD-DID had abnormal shape and significantly smaller volume in the CA2-3, CA4-DG and (pre)subiculum compared with HC. In the patient groups, smaller global and subfield hippocampal volumes significantly correlated with higher severity of childhood traumatization and dissociative symptoms. These findings support a childhood trauma-related etiology for abnormal hippocampal morphology in both PTSD and DID and can further the understanding of neurobiological mechanisms involved in these disorders.

Structural brain correlates associated with professional handball playing

Background

There is no doubt that good bimanual performance is very important for skilled handball playing. The control of the non-dominant hand is especially demanding since efficient catching and throwing needs both hands.

Methodology/Hypotheses

We investigated training-induced structural neuroplasticity in professional handball players using several structural neuroimaging techniques and analytic approaches and also provide a review of the literature about sport-induced structural neuroplastic alterations. Structural brain adaptations were expected in regions relevant for motor and somatosensory processing such as the grey matter (GM) of the primary/secondary motor (MI/supplementary motor area, SMA) and somatosensory cortex (SI/SII), basal ganglia, thalamus, and cerebellum and in the white matter (WM) of the corticospinal tract (CST) and corpus callosum, stronger in brain regions controlling the non-dominant left hand.

Results

Increased GM volume in handball players compared with control subjects were found in the right MI/SI, bilateral SMA/cingulate motor area, and left intraparietal sulcus. Fractional anisotropy (FA) and axial diffusivity were increased within the right CST in handball players compared with control women. Age of handball training commencement correlated inversely with GM volume in the right and left MI/SI and years of handball training experience correlated inversely with radial diffusivity in the right CST. Subcortical structures tended to be larger in handball players. The anatomical measures of the brain regions associated with handball playing were positively correlated in handball players, but not interrelated in control women.

Discussion/Conclusion

Training-induced structural alterations were found in the somatosensory-motor network of handball players, more pronounced in the right hemisphere controlling the non-dominant left hand. Correlations between handball training-related measures and anatomical differences suggest neuroplastic adaptations rather than a genetic predisposition for a ball playing affinity. Investigations of neuroplasticity specifically in sportsmen might help to understand the neural mechanisms of expertise in general.

Ictal depth EEG and MRI structural evidence for two different epileptogenic networks in mesial temporal lobe epilepsy

Hypersynchronous (HYP) and low voltage fast (LVF) activity are two separate ictal depth EEG onsets patterns often recorded in presurgical patients with MTLE. Evidence suggests the mechanisms generating HYP and LVF onset seizures are distinct, including differential involvement of hippocampal and extra-hippocampal sites. Yet the extent of extra-hippocampal structural alterations, which could support these two common seizures, is not known. In the current study, preoperative MRI from 24 patients with HYP or LVF onset seizures were analyzed to determine changes in cortical thickness and relate structural changes to spatiotemporal properties of the ictal EEG. Overall, onset and initial ipsilateral spread of HYP onset seizures involved mesial temporal structures, whereas LVF onset seizures involved mesial and lateral temporal as well as orbitofrontal cortex. MRI analysis found reduced cortical thickness correlated with longer duration of epilepsy. However, in patients with HYP onsets, the most affected areas were on the medial surface of each hemisphere, including parahippocampal regions and cingulate gyrus, whereas in patients with LVF onsets, the lateral surface of the anterior temporal lobe and orbitofrontal cortex showed the greatest effect. Most patients with HYP onset seizures were seizure-free after resective surgery, while a higher proportion of patients with LVF onset seizures had only worthwhile improvement. Our findings confirm the view that recurrent seizures cause progressive changes in cortical thickness, and provide information concerning the structural basis of two different epileptogenic networks responsible for MTLE. One, identified by HYP ictal onsets, chiefly involves hippocampus and is associated with excellent outcome after standardized anteromedial temporal resection, while the other also involves lateral temporal and orbitofrontal cortex and a seizure-free surgical outcome occurs less after this procedure. These results suggest that a more extensive tailored resection may be required for patients with the second type of MTLE.

Higher homocysteine associated with thinner cortical gray matter in 803 participants from the Alzheimer's Disease Neuroimaging Initiative

A significant portion of our risk for dementia in old age is associated with lifestyle factors (diet, exercise, and cardiovascular health) that are modifiable, at least in principle. One such risk factor, high-homocysteine levels in the blood, is known to increase risk for Alzheimer's disease and vascular disorders. Here, we set out to understand how homocysteine levels relate to 3D surface-based maps of cortical gray matter distribution (thickness, volume, and surface area) computed from brain magnetic resonance imaging in 803 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative data set. Individuals with higher plasma levels of homocysteine had lower gray matter thickness in bilateral frontal, parietal, occipital, and right temporal regions and lower gray matter volumes in left frontal, parietal, temporal, and occipital regions, after controlling for diagnosis, age, and sex and after correcting for multiple comparisons. No significant within-group associations were found in cognitively healthy people, patients with mild cognitive impairment, or patients with Alzheimer's disease. These regional differences in gray matter structure may be useful biomarkers to assess the effectiveness of interventions, such as vitamin B supplements, that aim to prevent homocysteine-related brain atrophy by normalizing homocysteine levels.

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

Morphometry-based measurements of the structural response to whole-brain radiation

PURPOSE

Morphometry techniques were applied to quantify the normal tissue therapy response in patients receiving whole-brain radiation for intracranial malignancies.

METHODS

Pre- and Post-irradiation magnetic resonance imaging (MRI) data sets were retrospectively analyzed in N = 15 patients. Volume changes with respect to pre-irradiation were quantitatively measured in the cerebrum and ventricles. Measurements were correlated with the time interval from irradiation. Criteria for inclusion included craniospinal irradiation, pre-irradiation MRI, at least one follow-up MRI, and no disease progression. The brain on each image was segmented to remove the skull and registered to the initial pre-treatment scan. Average volume changes were measured using morphometry analysis of the deformation Jacobian and direct template registration-based segmentation of brain structures.

RESULTS

An average cerebral volume atrophy of -0.2 and -3% 3% was measured for the deformation morphometry and direct segmentation methods, respectively. An average ventricle volume dilation of 21 and 20% was measured for the deformation morphometry and direct segmentation methods, respectively.

CONCLUSION

The presented study has developed an image processing pipeline for morphometric monitoring of brain tissue volume changes as a response to radiation therapy. Results indicate that quantitative morphometric monitoring is feasible and may provide additional information in assessing response.

Acupuncture modulates cortical thickness and functional connectivity in knee osteoarthritis patients

In this study, we investigated cortical thickness and functional connectivity across longitudinal acupuncture treatments in patients with knee osteoarthritis (OA). Over a period of four weeks (six treatments), we collected resting state functional magnetic resonance imaging (fMRI) scans from 30 patients before their first, third and sixth treatments. Clinical outcome showed a significantly greater Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score (improvement) with verum acupuncture compared to the sham acupuncture. Longitudinal cortical thickness analysis showed that the cortical thickness at left posterior medial prefrontal cortex (pMPFC) decreased significantly in the sham group across treatment sessions as compared with verum group. Resting state functional connectivity (rsFC) analysis using the left pMPFC as a seed showed that after longitudinal treatments, the rsFC between the left pMPFC and the rostral anterior cingulate cortex (rACC), medial frontal pole (mFP) and periaquiduct grey (PAG) are significantly greater in the verum acupuncture group as compared with the sham group. Our results suggest that acupuncture may achieve its therapeutic effect on knee OA pain by preventing cortical thinning and decreases in functional connectivity in major pain related areas, therefore modulating pain in the descending pain modulatory pathway.

Skull-stripping with machine learning deformable organisms

BACKGROUND

Segmentation methods for medical images may not generalize well to new data sets or new tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. We validate our framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping).

NEW METHOD

Our method borrows ideas from artificial life to govern a set of deformable models. We use control processes such as sensing, proactive planning, reactive behavior, and knowledge representation to segment an image. The image may have landmarks and features specific to that dataset; these may be easily incorporated into the plan. In addition, we use a machine learning method to make our segmentation more accurate.

RESULTS

Our method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error and performed on par to skull-stripping specific method on other metrics.

COMPARISON WITH EXISTING METHOD(S)

We tested our method on 838 T1-weighted images, evaluating results using distance and overlap error metrics based on expert gold standard segmentations. We evaluated the results before and after the learning step to quantify its benefit; we also compare our results to three other widely used methods: BSE, BET, and the Hybrid Watershed algorithm.

CONCLUSIONS

Our framework captures diverse categories of information needed for brain segmentation and will provide a foundation for tackling a wealth of segmentation problems.

Detection of volume loss in hippocampal layers in Alzheimer's disease using 7 T MRI: a feasibility study

In Alzheimer's disease (AD), the hippocampus is an early site of tau pathology and neurodegeneration. Histological studies have shown that lesions are not uniformly distributed within the hippocampus. Moreover, alterations of different hippocampal layers may reflect distinct pathological processes. 7 T MRI dramatically improves the visualization of hippocampal subregions and layers. In this study, we aimed to assess whether 7 T MRI can detect volumetric changes in hippocampal layers in vivo in patients with AD. We studied four AD patients and seven control subjects. MR images were acquired using a whole-body 7 T scanner with an eight channel transmit–receive coil. Hippocampal subregions were manually segmented from coronal T2*-weighted gradient echo images with 0.3 × 0.3 × 1.2 mm³ resolution using a protocol that distinguishes between layers richer or poorer in neuronal bodies. Five subregions were segmented in the region of the hippocampal body: alveus, strata radiatum, lacunosum and moleculare (SRLM) of the cornu Ammonis (CA), hilum, stratum pyramidale of CA and stratum pyramidale of the subiculum. We found strong bilateral reductions in the SRLM of the cornu Ammonis and in the stratum pyramidale of the subiculum (p < 0.05), with average cross-sectional area reductions ranging from −29% to −49%. These results show that it is possible to detect volume loss in distinct hippocampal layers using segmentation of 7 T MRI. 7 T MRI-based segmentation is a promising tool for AD research.

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Manual segmentation of hippocampal layers is feasible in-vivo at 7T in patients with Alzheimer’s disease (AD)

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It allows distinguishing layers richer and poorer in neuronal bodies

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In AD patients, strong atrophy is found in the stratum pyramidale of the subiculum and the strata radiatum, lacunosum and moleculare of Ammon’s horn

Typical and atypical brain development: a review of neuroimaging studies

In the course of development, the brain undergoes a remarkable process of restructuring as it adapts to the environment and becomes more efficient in processing information. A variety of brain imaging methods can be used to probe how anatomy, connectivity, and function change in the developing brain. Here we review recent discoveries regarding these brain changes in both typically developing individuals and individuals with neurodevelopmental disorders. We begin with typical development, summarizing research on changes in regional brain volume and tissue density, cortical thickness, white matter integrity, and functional connectivity. Space limits preclude the coverage of all neurodevelopmental disorders; instead, we cover a representative selection of studies examining neural correlates of autism, attention deficit/hyperactivity disorder, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Down syndrome, and Turner syndrome. Where possible, we focus on studies that identify an age by diagnosis interaction, suggesting an altered developmental trajectory. The studies we review generally cover the developmental period from infancy to early adulthood. Great progress has been made over the last 20 years in mapping how the brain matures with MR technology. With ever-improving technology, we expect this progress to accelerate, offering a deeper understanding of brain development, and more effective interventions for neurodevelopmental disorders.

Charting Cognitive and Volumetric Trajectories after Stroke: Protocol for the Cognition and Neocortical Volume after Stroke (CANVAS) Study

Rationale

Globally, stroke and dementia are leading causes of disability and mortality. More than one third of stroke patients will develop dementia, but mechanisms are unclear.

Aims

The study aims to establish whether brain volume change is associated with poststroke dementia, and to elucidate potential causal mechanisms, including genetic markers, amyloid deposition and vascular risk factors. An understanding of whether – and in whom – stroke is neurodegenerative is critical for the strategic use of potential disease-modifying therapies.

Hypotheses

That stroke patients will exhibit greater brain volume loss than comparable cohorts of stroke-free controls; and that those who develop dementia will exhibit greater brain volume loss than those who do not.

Design

Advanced brain imaging techniques are used to longitudinally measure brain volume and cortical thickness in 135 stroke patients. Concurrent neuropsychological testing will correlate clinical profile with these measures.

Primary outcomes

Primary imaging end-point is brain volume change between three-months and three-years poststroke; primary clinical outcome is the presence of dementia at three-years.

Secondary outcomes

We will examine the correlations with the following variables: dementia subtype; physical activity levels; behavioral dysfunction as measured by patient and caregiver-reported scales; structural and functional brain connectivity disruption; apolipoprotein E; and specific neuropsychological test scores.

Discussion

Magnetic resonance imaging markers of structural brain aging and performance on neuropsychological tests are powerful predictors of dementia. We need to understand the trajectory of regional brain volume change and cognitive decline in patients after stroke. This will allow future risk stratification for prognostic counseling, service planning, and early therapeutic intervention.

Multi-resolutional shape features via non-Euclidean wavelets: applications to statistical analysis of cortical thickness

Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer's disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer's Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer's Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation.

Reversed hierarchy in the brain for general and specific cognitive abilities: a morphometric analysis

Intelligence is composed of a set of cognitive abilities hierarchically organized. General and specific abilities capture distinguishable, but related, facets of the intelligence construct. Here, we analyze gray matter with three morphometric indices (volume, cortical surface area, and cortical thickness) at three levels of the intelligence hierarchy (tests, first-order factors, and a higher-order general factor, g). A group of one hundred and four healthy young adults completed a cognitive battery and underwent high-resolution structural MRI. Latent scores were computed for the intelligence factors and tests were also analyzed. The key finding reveals substantial variability in gray matter correlates at the test level, which is substantially reduced for the first-order and the higher-order factors. This supports a reversed hierarchy in the brain with respect to cognitive abilities at different psychometric levels: the greater the generality, the smaller the number of relevant gray matter clusters accounting for individual differences in intelligent performance.

Landmarking the brain for geometric morphometric analysis: an error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.

Unraveling the characteristics of microRNA regulation in the developmental and aging process of the human brain

Background

Structure and function of the human brain are subjected to dramatic changes during its development and aging. Studies have demonstrated that microRNAs (miRNAs) play an important role in the regulation of brain development and have a significant impact on brain aging and neurodegeneration. However, the underling molecular mechanisms are not well understood. In general, development and aging are conventionally studied separately, which may not completely address the physiological mechanism over the entire lifespan. Thus, we study the regulatory effect between miRNAs and mRNAs in the developmental and aging process of the human brain by integrating miRNA and mRNA expression profiles throughout the lifetime.

Methods

In this study, we integrated miRNA and mRNA expression profiles in the human brain across lifespan from the network perspective. First, we chose the age-related miRNAs by polynomial regression models. Second, we constructed the bipartite miRNA-mRNA regulatory network by pair-wise correlation coefficient analysis between miRNA and mRNA expression profiles. At last, we constructed the miRNA-miRNA synergistic network from the miRNA-mRNA network, considering not only the enrichment of target genes but also GO function enrichment of co-regulated target genes.

Results

We found that the average degree of age-related miRNAs was significantly higher than that of non age-related miRNAs in the miRNA-mRNA regulatory network. The topological features between age-related and non age-related miRNAs were significantly different, and 34 reliable age-related miRNA synergistic modules were identified using Cfinder in the miRNA-miRNA synergistic network. The synergistic regulations of module genes were verified by reviewing miRNA target databases and previous studies.

Conclusions

Age-related miRNAs play a more important role than non age-related mrRNAs in the developmental and aging process of the human brain. The age-related miRNAs have synergism, which tend to work together as small modules. These results may provide a new insight into the regulation of miRNAs in the developmental and aging process of the human brain.

Neuroimaging, nutrition, and iron-related genes

Several dietary factors and their genetic modifiers play a role in neurological disease and affect the human brain. The structural and functional integrity of the living brain can be assessed using neuroimaging, enabling large-scale epidemiological studies to identify factors that help or harm the brain. Iron is one nutritional factor that comes entirely from our diet, and its storage and transport in the body are under strong genetic control. In this review, we discuss how neuroimaging can help to identify associations between brain integrity, genetic variations, and dietary factors such as iron. We also review iron's essential role in cognition, and we note some challenges and confounds involved in interpreting links between diet and brain health. Finally, we outline some recent discoveries regarding the genetics of iron and its effects on the brain, suggesting the promise of neuroimaging in revealing how dietary factors affect the brain.

Age-related brain trajectories in schizophrenia: a systematic review of structural MRI studies

Using the Pubmed database, we performed a detailed literature search for structural magnetic resonance imaging studies on patients with schizophrenia, investigating the relationship between macroscopic and microscopic structural parameters and age, to delineate an age-related trajectory. Twenty-six studies were considered for the review, from January 2000 to June 2012. Research results are heterogeneous because of the multifactorial features of schizophrenia and the multiplicity of the methodological approaches adopted. Some areas, within the amygdala-hippocampus complex, which are affected early in life by schizophrenia, age in a physiological way. Other regions, such as the superior temporal gyrus, appear already impaired at the onset of symptoms, undergo a worsening in the acute phase but later stabilize, progressing physiologically over years. Finally, there are regions, such as the uncinate fasciculus, which are not altered early in life, but are affected around the onset of schizophrenia, with their impairment continuously worsening over time. Further extensive longitudinal studies are needed to understand the timing and the possible degenerative characteristics of structural impairment associated with schizophrenia.

Insights on the functional interactions between miRNAs and copy number variations in the aging brain

MicroRNAs (miRNAs) are regulatory genetic elements that coordinate the expression of thousands of genes and play important roles in brain aging and neurodegeneration. DNA polymorphisms affecting miRNA biogenesis, dosage, and gene targeting may represent potentially functional variants. The consequences of single nucleotide polymorphisms affecting miRNA function were previously demonstrated by both experimental and computational methods. However, little is known about how copy number variations (CNVs) influence miRNA metabolism and regulatory networks. We discuss potential mechanisms of CNVs-mediated effects on miRNA function and regulation that might have consequences for brain aging. We argue that CNVs, which potentially can alter miRNA expression, regulation or target gene recognition, are possible functional variants and should be considered high priority candidates in genotype–phenotype mapping studies of brain-related disorders.

"Autism-plus" spectrum disorders: intersection with psychosis and the schizophrenia spectrum

Patients are often encountered clinically who have autism spectrum disorders (ASD) and also have symptoms suggestive of a comorbid psychotic disorder. A careful assessment for the presence of comorbid disorders is important. However, the core deficits seen in ASD, in social reciprocity, communication, and restricted behaviors and interests, can be mistaken for psychosis. Also, there is a subset of patients who present with a complex neurodevelopmental disorder with impairments that cross diagnostic categories. This article reviews the connections between ASD and psychosis, and highlights the key points to consider in patients who present with these "autism-plus" disorders.

Patterns of cortical thickness and the Child Behavior Checklist in childhood epilepsy

The purpose of this investigation was to characterize the neuroanatomical correlates (cortical thickness) of variations in parent-reported Child Behavior Checklist (CBCL) scores. Ninety children with epilepsy (aged 8-18) underwent brain MRI, and their parents completed the CBCL. FreeSurfer-derived measures of cortical thickness were examined in relation to the CBCL broad and narrow band competence and behavioral problem scales, as well as the newer DSM-oriented scales. Parent reports of higher (better) social competence skills were associated with increased cortical thickness, especially in frontal regions. Parent reports of behavioral problems were associated with patterns of decreased cortical thickness that varied as a function of the specific behavioral issue under investigation. Congruence of patterns of cortical thinning between the DSM-oriented scales and conceptually related specific problem scales (e.g., ADHD Problems and Attention Problems) was generally weak. The parent-report version of the CBCL is associated with variations in cortical thickness among children with epilepsy. Anatomic abnormalities specific to selected competence and behavioral problem scales can be identified, with more reliable and robust patterns of thinning across scales assessing externalizing behaviors, with generally less prominent findings on scales assessing internalizing behaviors.

Cognitive and behavioral correlates of caudate subregion shape variation in fragile X syndrome

Individuals with fragile X syndrome (FXS) exhibit frontal lobe-associated cognitive and behavioral deficits, including impaired general cognitive abilities, perseverative behaviors, and social difficulties. Neural signals related to these functions are communicated through frontostriatal circuits, which connect with distinct regions of the caudate nucleus (CN). Enlargement of the CN is the most robust and reproduced neuroanatomical abnormality in FXS, but very little is known on how this affects behavioral/cognitive outcomes in this condition. Here, we investigated topography within focal regions of the CN associated with prefrontal circuitry and its link with aberrant behavior and intellect in FXS. Imaging data were acquired from 48 individuals with FXS, 28 IQ-matched controls without FXS (IQ-CTL), and 36 typically developing controls (TD-CTL). Of the total participant count, cognitive and behavioral assessment data were obtained from 44 individuals with FXS and 27 participants in the IQ-CTL group. CN volume and topography were compared between groups. Correlations were performed between CN topography and cognitive as well as behavioral measures within FXS and IQ-CTL groups. As expected, the FXS group had larger CN compared with both IQ-CTL and TD-CTL groups. Correlations between focal CN topography and frontal lobe-associated cognitive and behavioral deficits in the FXS group supported the hypothesis that CN enlargement is related to abnormal orbitofrontal-caudate and dorsolateral-caudate circuitry in FXS. These findings deepen our understanding of neuroanatomical mechanisms underlying cognitive-behavioral problems in FXS and hold promise for informing future behavioral and psychopharmacological interventions targeting specific neural pathways.

Surface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampus

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.

The effect of aerobic exercise on cortical architecture in patients with chronic schizophrenia: a randomized controlled MRI study

Via influencing brain plasticity, aerobic exercise could contribute to the treatment of schizophrenia patients. As previously shown, physical exercise increases hippocampus volume and improves short-term memory. We now investigated gray matter density and brain surface expansion in this sample using MRI-based cortical pattern matching methods. Comparing schizophrenia patients to healthy controls before and after 3 months of aerobic exercise training (cycling) plus patients playing table football yielded gray matter density increases in the right frontal and occipital cortex merely in healthy controls. However, respective exercise effects might be attenuated in chronic schizophrenia, which should be verified in a larger sample.

3D Vascular Decomposition and Classification for Computer-Aided Detection

In this study, we propose a weighted approximate convex decomposition (WACD) and classification methodology for computer-aided detection (CADe) and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and introduce a methodology for compact geometric decomposition. The classification of decomposed vessel sections is performed using the most relevant eigenvalues obtained through feature selection. The method was validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for classification. The proposed method produced promising results with an estimated 81.5% of the vessel sections correctly decomposed. Recursive feature elimination was performed to find the most compact and informative set of features. We showed that the selected subset of eigenvalues produces minimum error and improved classifier precision. The method was also validated on a longitudinal study of four cases having internal cerebral aneurysms. Volumetric and surface area comparisons were made between expert-segmented sections and WACD classified sections containing aneurysms. Results suggest that the approach is able to classify and detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

Quantitative analysis of structural neuroimaging of mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is the most common of the surgically remediable drug-resistant epilepsies. MRI is the primary diagnostic tool to detect anatomical abnormalities and, when combined with EEG, can more accurately identify an epileptogenic lesion, which is often hippocampal sclerosis in cases of MTLE. As structural imaging technology has advanced the surgical treatment of MTLE and other lesional epilepsies, so too have the analysis techniques that are used to measure different structural attributes of the brain. These techniques, which are reviewed here and have been used chiefly in basic research of epilepsy and in studies of MTLE, have identified different types and the extent of anatomical abnormalities that can extend beyond the affected hippocampus. These results suggest that structural imaging and sophisticated imaging analysis could provide important information to identify networks capable of generating spontaneous seizures and ultimately help guide surgical therapy that improves postsurgical seizure-freedom outcomes.

Transcriptomics of cortical gray matter thickness decline during normal aging

INTRODUCTION

We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging.

METHODS

Transcriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 μm) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability.

RESULTS

Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT.

CONCLUSION

Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation.

Spreading of Alzheimer's disease inflammatory signaling through soluble micro-RNA

Alzheimer's disease is a progressive, neurodegenerative disorder that develops within the limbic system, spreading radially into anatomically linked brain association areas as the disease progresses. Analysis of temporal-lobe association of neocortex-derived extracellular fluid and cerebrospinal fluid from Alzheimer's disease patients shows an abundant presence of micro-RNA (miRNA), including the proinflammatory miRNA-146a and miRNA-155. Using a novel and highly sensitive LED-Northern dot-blot focusing technique, we detected the secretion of potentially pathogenic amounts of miRNA-146a and miRNA-155 from stressed human primary neural cells. A conditioned medium containing miRNA-146a and miRNA-155 was found to induce Alzheimer-type gene expression changes in control brain cells. These included downregulation in the expression of an important repressor of the innate immune response, complement factor H (CFH). These effects were neutralized using anti-miRNA strategies. Anti-miRNA-based therapeutics may provide a novel and efficacious treatment to stem the miRNA-mediated spreading of inflammatory signaling involved in Alzheimer's disease.

Three-dimensional mapping of hippocampal and amygdalar structure in euthymic adults with bipolar disorder not treated with lithium

Structural neuroimaging studies of the amygdala and hippocampus in bipolar disorder have been largely inconsistent. This may be due in part to differences in the proportion of subjects taking lithium or experiencing an acute mood state, as both factors have recently been shown to influence gray matter structure. To avoid these problems, we evaluated euthymic subjects not currently taking lithium. Thirty-two subjects with bipolar type I disorder and 32 healthy subjects were scanned using magnetic resonance imaging. Subcortical regions were manually traced, and converted to three-dimensional meshes to evaluate the main effect of bipolar illness on radial distance. Statistical analyses found no evidence for a main effect of bipolar illness in either region, although exploratory analyses found a significant age by diagnosis interaction in the right amygdala, as well as positive associations between radial distance of the left amygdala and both prior hospitalizations for mania and current medication status. These findings suggest that, when not treated with lithium or in an acute mood state, patients with bipolar disorder exhibit no structural abnormalities of the amygdala or hippocampus. Future studies, nevertheless, that further elucidate the impact of age, course of illness, and medication on amygdala structure in bipolar disorder are warranted.

Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data

This paper presents a framework to introduce spatial and anatomical priors in SVM for brain image analysis based on regularization operators. A notion of proximity based on prior anatomical knowledge between the image points is defined by a graph (e.g., brain connectivity graph) or a metric (e.g., Fisher metric on statistical manifolds). A regularization operator is then defined from the graph Laplacian, in the discrete case, or from the Laplace-Beltrami operator, in the continuous case. The regularization operator is then introduced into the SVM, which exponentially penalizes high-frequency components with respect to the graph or to the metric and thus constrains the classification function to be smooth with respect to the prior. It yields a new SVM optimization problem whose kernel is a heat kernel on graphs or on manifolds. We then present different types of priors and provide efficient computations of the Gram matrix. The proposed framework is finally applied to the classification of brain Magnetic Resonance (MR) images (based on Gray Matter (GM) concentration maps and cortical thickness measures) from 137 patients with Alzheimer's Disease (AD) and 162 elderly controls. The results demonstrate that the proposed classifier generates less-noisy and consequently more interpretable feature maps with high classification performances.

Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis

Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.

Geodesic distances to landmarks for dense correspondence on ensembles of complex shapes

Establishing correspondence points across a set of biomedical shapes is an important technology for a variety of applications that rely on statistical analysis of individual subjects and populations. The inherent complexity (e.g. cortical surface shapes) and variability (e.g. cardiac chambers) evident in many biomedical shapes introduce significant challenges in finding a useful set of dense correspondences. Application specific strategies, such as registration of simplified (e.g. inflated or smoothed) surfaces or relying on manually placed landmarks, provide some improvement but suffer from limitations including increased computational complexity and ambiguity in landmark placement. This paper proposes a method for dense point correspondence on shape ensembles using geodesic distances to a priori landmarks as features. A novel set of numerical techniques for fast computation of geodesic distances to point sets is used to extract these features. The proposed method minimizes the ensemble entropy based on these features, resulting in isometry invariant correspondences in a very general, flexible framework.

Cortical thickness and semantic fluency in Alzheimer's disease and mild cognitive impairment

The hallmark of Alzheimer's disease (AD) is declarative memory loss, but deficits in semantic fluency are also observed. We assessed how semantic fluency relates to cortical atrophy to identify specific regions that play a role in the loss of access to semantic information. Whole-brain structural magnetic resonance imaging (MRI) data were analyzed from 9 Normal Control (NC)(M=76.7, SD=5.6), 40 Mild Cognitive Impairment (MCI) (M=74.4, SD=8.6), and 10 probable AD (M=72.4, SD=8.0) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). They all were administered the Category Fluency (CF) animals and vegetables tests. Poorer semantic fluency was associated with bilateral cortical atrophy of the inferior parietal lobule (Brodman areas (BA) 39 and 40) and BA 6, 8, and 9 in the frontal lobe, as well as BA 22 in the temporal lobe. More diffuse frontal associations were seen in the left hemisphere involving BA 9, 10, 32, 44, 45, and 46. Additional cortical atrophy was seen in the temporoparietal (BA 37) and the right parastriate (BA 19, 18) cortices. Associations were more diffuse for performance on vegetable fluency than animal fluency. The permutation-corrected map-wise significance for CF animals was p_corrected=0.01 for the left hemisphere, and p_corrected=0.06 for the right hemisphere. The permutation-corrected map-wise significance for CF vegetables was p_corrected=0.009 for the left hemisphere, and p_corrected=0.03 for the right hemisphere. These results demonstrate the profound effect of cortical atrophy on semantic fluency. Specifically, tapping into semantic knowledge involves the frontal lobe in addition to the language cortices of the temporoparietal region.

ADHD comorbidity can matter when assessing cortical thickness abnormalities in patients with bipolar disorder

OBJECTIVES

Attention-deficit hyperactivity disorder (ADHD) is prevalent in patients with bipolar disorder (BP), but very few studies consider this when interpreting magnetic resonance imaging findings. No studies, to our knowledge, have screened for or controlled for the presence of ADHD when examining cortical thickness in patients with BP. We used a 2 × 2 design to evaluate the joint effects of BP and ADHD on cortical thickness and uncover the importance of ADHD comorbidity in BP subjects.

METHODS

The study included 85 subjects: 31 healthy controls, 17 BP-only, 19 ADHD-only, and 18 BP/ADHD. All patients with BP were subtype I, euthymic, and not taking lithium. Groups did not differ significantly in age or gender distribution. We used cortical thickness measuring tools combined with cortical pattern matching methods to align sulcal/gyral anatomy across participants. Significance maps were used to check for both main effects of BP and ADHD and their interaction. Post-hoc comparisons assessed how the effects of BP on cortical thickness varied as a function of the presence or absence of ADHD.

RESULTS

Interactions of BP and ADHD diagnoses were found in the left subgenual cingulate and right orbitofrontal cortex, demonstrating that the effect of BP on cortical thickness depends on ADHD status.

CONCLUSIONS

Some brain abnormalities attributed to BP may result from the presence of ADHD. Diagnostic interactions were found in regions previously implicated in the pathophysiology of BP, making it vital to control for an ADHD comorbid diagnosis when attempting to isolate neural or genetic abnormalities specific to BP.

Effects of alcohol dependence on cortical thickness as determined by magnetic resonance imaging

Alterations of brain structures have been seen in patients suffering from drug abuse or mental disorders like schizophrenia. Similar changes in volume of brain structures have been observed in both alcoholic men and women. We examined the thickness of gray matter in the cerebral cortex in control men and women (n=69, 47 men) and alcohol-dependent subjects (n=130, 83 men) to test the hypothesis that alcoholic inpatients would have more cortical damage than controls. We also hypothesized that alcoholic women would be more affected than alcoholic men. Alcoholic participants with a history of schizophrenia, psychotic, or bipolar disorder were excluded from the study. Volumetric structural magnetic resonance images were collected, 3D surfaces were created using Freesurfer, and statistical testing for cortical thickness differences was carried out using AFNI/SUMA. Covarying for age and years of education, we confirmed significant differences between alcoholics and healthy controls in cortical thickness in both the left and right hemispheres. Significant differences in cortical thickness between control men and women were also observed. These differences may reflect sexual dimorphisms in the human brain, a genetic predisposition to alcoholism and comorbid drug use, and the extent of gray matter damage in alcoholism and substance use.