Magnetic Resonance Imaging Gray Matter Research Articles

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild cognitive symptoms

BackgroundPredicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.MethodsA cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.ResultsIn the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated.ConclusionsThe DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

Rethinking “aggression” and impulsivity in bipolar disorder: Risk, clinical and brain circuitry features

BackgroundElevated aggression and impulsivity are implicated in Bipolar Disorder (BD); however, relationships between these behavioral constructs have not been clarified, which can lead to misconceptions with negative consequences including stigma and adverse outcomes including suicide. The study aimed to clarify brain-based distinctions between the two constructs and their associations to risk factors, symptoms and suicide thoughts and behaviors. MethodsSelf-rated Brown-Goodwin Aggression (BGA) and Barratt Impulsiveness Scale (BIS) scores were compared between adults with BD (n = 38, 74% female) and healthy controls (HC, n = 29, 64% female). Relationships were examined between BGA and BIS with childhood trauma questionnaire (CTQ), mood, comorbidities, and magnetic resonance imaging gray matter volume (GMV) assessments. ResultsIn BD, BGA and BIS total scores were both elevated and associated with childhood maltreatment (CM), particularly emotional CM, depression, substance use disorders (SUDs) and suicide attempts (SAs). BGA scores were increased by items corresponding to dysregulation of emotional and social behavior and associated with elevated mood states and suicide ideation and GMV decreases in bilateral orbitofrontal cortex and left posterior insula brain regions, previously associated with these behaviors and clinical features. BIS motor impulsiveness scores were associated with GMV decreases in anterior cingulate cortex implicated in mood and behavioral dyscontrol. Limitationsmodest sample size, self-reports ConclusionsThe findings suggest separable brain-based domains of dysfunction in BD of motor impulsiveness versus emotionally dysregulated feelings that are primarily self-directed. Both domains are associated with suicide behavior and modifiable risk factors of CM, depression and SUDs that could be targeted for prevention.

Multiplex Connectome Changes across the Alzheimer's Disease Spectrum Using Gray Matter and Amyloid Data.

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Voxel-based correlation of 18F-THK5351 accumulation with gray matter structural networks in cognitively normal older adults.

ObjectiveThe aim of this study was to evaluate tau-related structural network metrics derived from gray matter magnetic resonance imaging (MRI) scans in cognitively normal (CN) older adults.MethodsWe recruited 47 amyloid-negative CN older adults (mean age ± standard deviation, 65.0 ± 7.9 years; 26 women). All participants underwent 3D T1-weighted MRI and 11C-Pittsburgh compound-B and 18F-THK5351 positron emission tomography scans. Four local network metrics (betweenness centrality, clustering coefficient, characteristic path length, and degree) were computed and rendered on individual brain images. We then evaluated the correlations between 18F-THK5351 positron emission tomography images and local network metric images at the voxel level.ResultsSignificant positive correlations of the four local network metrics with 18F-THK5351 were detected in the bilateral caudate.ConclusionOur findings suggest that tau and neuroinflammation in CN older adults may influence the gray matter structural network in the caudate.

Structural brain network differences in bipolar disorder using with similarity-based approach.

Objective: Previous studies have shown differences in the regional brain structure and function between patients with bipolar disorder (BD) and healthy subjects, but little is known about the structural connectivity between BD patients and healthy subjects. In this study, we evaluated the disease-related changes in regional structural connectivity derived from gray matter magnetic resonance imaging (MRI) scans. Methods: The subjects were 73 patients with BD and 80 healthy volunteers who underwent 3-Tesla MRI. Network metrics, such as the small world properties, were computed. We also performed rendering of the network metric images such as the degree, betweenness centrality, and clustering coefficient, on individual brain image. Then, we estimated the differences between them, and evaluate the relationships between the clinical symptoms and the network metrics in the patients with BD. Results: BD patients showed a lower clustering coefficient in the right parietal region and left occipital region, compared with healthy subjects. A weak negative correlation between Young mania rating scale and clustering coefficient was found in left anterior cingulate cortex. Conclusions: We found differences in gray matter structural connectivity between BD patients and healthy subjects by a similarity-based approach. These points may provide objective biological information as an adjunct to the clinical diagnosis of BD.

Age-Related Regional Network Covariance of Magnetic Resonance Imaging Gray Matter in the Rat.

Healthy human aging has been associated with brain atrophy in prefrontal and selective temporal regions, but reductions in other brain areas have been observed. We previously found regional covariance patterns of gray matter with magnetic resonance imaging (MRI) in healthy humans and rhesus macaques, using multivariate network Scaled Subprofile Model (SSM) analysis and voxel-based morphometry (VBM), supporting aging effects including in prefrontal and temporal cortices. This approach has yet to be applied to neuroimaging in rodent models of aging. We investigated 7.0T MRI gray matter covariance in 10 young and 10 aged adult male Fischer 344 rats to identify, using SSM VBM, the age-related regional network gray matter covariance pattern in the rodent. SSM VBM identified a regional pattern that distinguished young from aged rats, characterized by reductions in prefrontal, temporal association/perirhinal, and cerebellar areas with relative increases in somatosensory, thalamic, midbrain, and hippocampal regions. Greater expression of the age-related MRI gray matter pattern was associated with poorer spatial learning in the age groups combined. Aging in the rat is characterized by a regional network pattern of gray matter reductions corresponding to aging effects previously observed in humans and non-human primates. SSM MRI network analyses can advance translational aging neuroscience research, extending from human to small animal models, with potential for evaluating mechanisms and interventions for cognitive aging.

Medial temporal lobe white matter pathway variability is associated with individual differences in episodic memory in cognitively normal older adults

Significant evidence demonstrates that aging is associated with variability in cognitive performance, even among individuals who are cognitively normal. In this study, we examined measures from magnetic resonance imaging and cerebrospinal fluid (CSF) to investigate which measures, alone or in combination, were associated with individual differences in episodic memory performance. Using hierarchical linear regressions, we compared the ability of diffusion tensor imaging (DTI) metrics, CSF measures of amyloid and tau, and gray matter volumes to explain variability in memory performance in a cohort of cognitively normal older adults. Measures of DTI microstructure were significantly associated with variance in memory performance, even after accounting for the contribution of the CSF and magnetic resonance imaging gray matter volume measures. Significant associations were found between DTI measures of the hippocampal cingulum and fornix with individual differences in memory. No such relationships were found between memory performance and CSF markers or gray matter volumes. These findings suggest that DTI metrics may be useful in identifying changes associated with aging or age-related diseases.

Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer's Disease in AIBL Data: Group and Individual Analyses.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data related to 58 AD, 108 mild cognitive impairment (MCI) and 120 healthy elderly (HE) subjects from the Australian imaging, biomarkers and lifestyle (AIBL) dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL) technique, yielding an average of >95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with t-contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (para)hippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by r2 = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with r2 = 0.62 (p < 0.01). Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD.

Histogram-Based Feature Extraction from Individual Gray Matter Similarity-Matrix for Alzheimer's Disease Classification.

Automatic computer-aided diagnosis (CAD) systems have been widely used in classification of patients who suffer from Alzheimer's disease (AD). This paper presents an automatic CAD system based on histogram feature extraction from single-subject gray matter similarity-matrix for classifying the AD patients from healthy controls (HC) using structural magnetic resonance imaging (MRI) data. The proposed CAD system is composed of five stages. In the first stage, segmentation is employed to perform pre-processing on the MRI images, and segment into gray matter, white matter, and cerebrospinal fluid using the voxel-based morphometric toolbox procedure. In the second stage, gray matter MRI scans are used to construct similarity-matrices. In the third stage, a novel statistical feature-generation process is proposed, utilizing the histogram of the individual similarity-matrix to represent statistical patterns of the respective similarity-matrices of different size and order into fixed-size feature-vectors. In the fourth stage, we propose to combine MRI measures with a neuropsychological test, the Functional Assessment Questionnaire (FAQ), to improve the classification accuracy. Finally, the classification is performed using a support vector machine and evaluated with the 10-fold cross-validation strategy. We evaluated the proposed method on 99 AD and 102 HC subjects from the J-ADNI. The proposed CAD system yields an 84.07% classification accuracy using MRI measures and 97.01% for combining MRI measures with FAQ scores, respectively. The experimental results indicate that the performance of the proposed system is competitive with respect to state-of-the-art techniques reported in the literature.

Prefrontal Lobe Gray Matter, Cognitive Control and Episodic Memory in Healthy Cognition

Objective: We combined neuropsychological and structural magnetic resonance imaging (MRI) measures to examine the neural and informational processes underlying episodic memory in healthy participants. Method: The Doors-and-People Test (DPT) provided a detailed assessment of episodic memory, including recall and recognition tasks of matched difficulty for social (e.g., people) and non-social (e.g., shape) content. The Wisconsin Card Sorting (WCS) test provided a measure of category learning that relies heavily on executive control and inhibition. A subset of participants also had available high-resolution, 3-T MRI gray matter volume studies of prefrontal cortex (PFC) parcellated into four regions: 1) frontal pole; 2) superior frontal gyrus; 3) middle frontal gyrus; and 4) inferior frontal gyrus. Results: Bivariate neuropsychological correlations revealed a highly statistically significant relationship of reduced WCS perseverative errors and stronger recall for people but not for shapes. By contrast, WCS perseveration did not correlate with any recognition measures. Hierarchical regression revealed that perseverative errors and people recall test scores combined to account for approximately 29.98% to 57.78% of the variance in left PFC gray matter volume. Conclusions: These results may point to an important role of the PFC in mnemonic process of retrieval inhibition in episodic memory for recall of social content in healthy participants.

Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction

Obtaining individual biomarkers for the prediction of altered neurological outcome is a challenge of modern medicine and neuroscience. Connectomics based on magnetic resonance imaging (MRI) stands as a good candidate to exhaustively extract information from MRI by integrating the information obtained in a few network features that can be used as individual biomarkers of neurological outcome. However, this approach typically requires the use of diffusion and/or functional MRI to extract individual brain networks, which require high acquisition times and present an extreme sensitivity to motion artifacts, critical problems when scanning fetuses and infants. Extraction of individual networks based on morphological similarity from gray matter is a new approach that benefits from the power of graph theory analysis to describe gray matter morphology as a large-scale morphological network from a typical clinical anatomic acquisition such as T1-weighted MRI. In the present paper we propose a methodology to normalize these large-scale morphological networks to a brain network with standardized size based on a parcellation scheme. The proposed methodology was applied to reconstruct individual brain networks of 63 one-year-old infants, 41 infants with intrauterine growth restriction (IUGR) and 22 controls, showing altered network features in the IUGR group, and their association with neurodevelopmental outcome at two years of age by means of ordinal regression analysis of the network features obtained with Bayley Scale for Infant and Toddler Development, third edition. Although it must be more widely assessed, this methodology stands as a good candidate for the development of biomarkers for altered neurodevelopment in the pediatric population.

Gray matter network associated with risk for Alzheimer's disease in young to middle-aged adults

The apolipoprotein E (APOE) ε4 allele increases the risk for late-onset Alzheimer's disease (AD) and age-related cognitive decline. We investigated whether ε4 carriers show reductions in gray matter volume compared with ε4 non-carriers decades before the potential onset of AD dementia or healthy cognitive aging. Fourteen cognitively normal ε4 carriers, aged 26 to 45 years, were compared with 10 age-matched, ε4 non-carriers using T1-weighted volumetric magnetic resonance imaging (MRI) scans. All had reported first- or second-degree family histories of dementia. Group differences in gray matter were tested using voxel-based morphometry (VBM) and a multivariate model of regional covariance, the Scaled Subprofile Model (SSM). A combination of the first two SSM MRI gray matter patterns distinguished the APOE ε4 carriers from non-carriers. This combined pattern showed gray matter reductions in bilateral dorsolateral and medial frontal, anterior cingulate, parietal, and lateral temporal cortices with covarying relative increases in cerebellum, occipital, fusiform, and hippocampal regions. With these gray matter differences occurring decades before the potential onset of dementia or cognitive aging, the results suggest longstanding, gene-associated differences in brain morphology that may lead to preferential vulnerability for the later effects of late-onset AD or healthy brain aging.

ACE I/D polymorphism affects cognitive function and gray-matter volume in amnestic mild cognitive impairment

To characterize the correlates of cognitive function, serum concentrations of angiotensin converting enzyme (ACE) and brain structure with the ACE insertion or deletion (I/D) polymorphism were analyzed in subjects with amnestic-mild cognitive impairment (aMCI). A group of 48 subjects meeting criteria for aMCI and 36 age-matched control subjects were assessed using a comprehensive battery of standardized neuropsychological tests and magnetic resonance imaging (MRI). The ACE gene's I/D polymorphism was analyzed by means of PCR, and serum ACE concentrations were measured using ultraviolet spectrophotometry. Genotype effects on neuropsychological domains and MRI gray matter volume (GMV) measurements (optimized voxel-based morphometry) were examined using general linear models. The D carriers among the aMCI subjects performed significantly worse on AVLT-delayed recall compared to the I homozygous group. The D carriers had higher serum ACE concentrations than did the I homozygous carriers, though this difference only reached statistical significance in the aMCI group. Compared with the I homozygous carriers, in the aMCI group, D carriers showed smaller GMV of the bilateral middle frontal gyrus, right cuneus, right precentral gyrus, right medial frontal gyrus, right superior frontal gyrus, and left postcentral gyrus. However, there was no significant difference in GMV between I homozygous and D carriers in the normal control group. The study suggests that ACE genotype has considerable effect on the cognitive performance of aMCI subjects, particularly episodic memory, serum activity of ACE, and the structure of specified brain regions. The ACE D allele may be a genetic risk factor for greater atrophy of gray matter in aMCI.

Blood–brain barrier permeability in Alzheimer's disease: a case–control MRI study

Blood-brain barrier (BBB) dysfunction may contribute to the risk of Alzheimer's disease (AD). Dynamic contrast-enhanced magnetic resonance imaging (MRI) was performed repeatedly nine times before and up to 30 min following a 20 ml Gd-DTPA bolus injection in 15 AD participants and 15 healthy older people. For each participant, small circular regions of interest (size: 9 voxels) were placed to sample widely the deep gray matter (12 regions), cortical gray matter (72 regions), white matter (72 regions) and CSF (8 regions) as well as the basilar and internal carotid arteries (3 regions). Data were analysed using mixed effects models. There was no overall significant difference for AD subjects versus controls, but there was a significant effect for the time-by-AD interaction. Estimated marginal means remained essentially unchanged in AD subjects, but increased slowly after 15 min in healthy controls. An initial rise in gray matter MRI signal intensity followed by a later increase was also seen in AD participants after adjusting for CSF MRI signal intensities. The data suggest that BBB permeability is present even at an early stage of AD. Though the extent of leakage was no greater than that of non-demented people of a similar age in this small sample, the temporal pattern differed, indicating different blood–brain–CSF compartmental kinetics.

Regional network of magnetic resonance imaging gray matter volume in healthy aging

Healthy aging has been associated with brain volume reductions preferentially affecting the frontal cortex, but also involving other regions. We used a network model of regional covariance, the Scaled Subprofile Model, with magnetic resonance imaging voxel-based morphometry to identify the regional distribution of gray matter associated with aging in 26 healthy adults, 22-77 years old. Scaled Subprofile Model analysis identified a pattern that was highly correlated with age (R2=0.66, P<or=0.0001). Older age was associated with less gray matter in the bilateral frontal, temporal,thalamic, and right cerebellar regions. Gender differences suggested more advanced brain aging in the men. In this healthy adult sample, aging was associated with a regional pattern of gray matter atrophy most prominently involving the frontal and temporal cortices. Scaled Subprofile Model network analysis may aid in the detection and tracking of brain aging and in the evaluation of putative antiaging therapies.

Cognitive and Magnetic Resonance Imaging Brain Morphometric Correlates of Brain-Derived Neurotrophic Factor Val66Met Gene Polymorphism in Patients With Schizophrenia and Healthy Volunteers

Relatively little is known about genetic determinants of cognitive dysfunction in schizophrenia. Recent studies suggest that a brain-derived neurotrophic factor (BDNF) prodomain single nucleotide polymorphism resulting in a valine (Val)-to-methionine (Met) substitution is associated with impaired declarative memory in healthy volunteers and patients with schizophrenia. These studies indicate that the BDNF(Met) variant may mediate hippocampal cognitive functions by modulating intracellular trafficking and activity-dependent BDNF release. To our knowledge, the way in which this functional single nucleotide polymorphism affects other neurocognitive measures has not been examined. Its role in determining cognitive deficits in schizophrenia has also not been systematically studied. To characterize the neurocognitive and brain morphometric phenotypic correlates of the BDNF Val66Met polymorphism and to test the specificity of the BDNF(Met) variant on cognitive dysfunction in schizophrenia. A comprehensive battery of standardized neuropsychological tests was administered to 144 healthy volunteers and 293 patients with schizophrenia spectrum disorder at a tertiary care university hospital. Approximately two thirds of the sample also underwent high-resolution magnetic resonance imaging brain scans. Genotype effects (in Met allele carriers vs Val homozygotes) on 5 cognitive domain z scores and magnetic resonance imaging gray matter brain volume measures (Talairach atlas-based cerebral lobes and optimized voxel-based morphometry) were examined using general linear models. On verbal memory, there was a significant genotype effect but no genotype x diagnosis effects. In both patients with schizophrenia and healthy volunteers, Met allele carriers had poorer verbal memory performance than their Val-homozygous counterparts. On visuospatial abilities, there were significant genotype and genotype x diagnosis effects. Met allele-associated visuospatial impairment was specific to patients with schizophrenia but not healthy volunteers. There were significant genotype effects on gray matter volumes within brain regions known to subserve these 2 cognitive domains, with Met allele carriers having smaller temporal and occipital lobar gray matter volumes. Optimized voxel-based morphometry further suggests that parietal heteromodal cortical gray matter deficits may underlie visuospatial impairment in patients with schizophrenia carrying the Met allele. We replicated the association between the BDNF(Met) variant and poor medial temporal lobe-related memory performance. The consonance of our cognitive and brain morphology findings further suggests that the BDNF(Met) variant may have a specific role in conferring visuospatial dysfunction in schizophrenia.

An account of the discrepancy between MRI and PET cerebral blood flow measures. A high-field MRI investigation

There is controversy concerning the discrepancy between absolute cerebral blood flow (CBF) values measured using positron emission tomography (PET) and magnetic resonance imaging (MRI). To gain insight into this problem, the increased signal-to-noise ratio (SNR) and extended T(1) relaxation times of blood and tissue at 3.0 T were exploited to perform pulsed arterial spin labeling (PASL) MRI measurements as a function of spatial resolution and post-labeling delay. The results indicate that, when using post-labeling delays shorter than 1500 ms, MRI gray matter flow values may become as high as several times the correct CBF values owing to tissue signal contamination by remaining arterial blood water label. For delays above 1500 ms, regional PASL-based CBF values (n = 5; frontal gray matter: 48.8 +/- 3.3(SD) ml/100 g/min; occipital gray matter: 49.3 +/- 4.5 ml/100 g/min) comparable with PET-based measurements can be obtained by using spatial resolutions comparable with PET (5-7.5 mm in-plane). At very high resolution (2.5 x 2.5 x 3 mm(3)), gray matter CBF values were found to increase by 10-20%, a consequence attributed to reduction in partial volume effects with cerebrospinal fluid and white matter. The recent availability of MRI field strengths of 3.0 T and higher will facilitate the use of MRI-based CBF measurements in the clinic.

Human cortical dysplasia and epilepsy: an ontogenetic hypothesis based on volumetric MRI and NeuN neuronal density and size measurements.

In epilepsy patients with cortical dysplasia (CD), this study determined the probable ontogenetic timing of pathogenesis based on the number, location and appearance of neurons. Magnetic resonance imaging (MRI) determined gray and white matter volumes of affected and non-affected cerebral hemispheres, and gray and white matter neuronal-nuclear protein (NeuN) densities and sizes were assessed in epilepsy surgery patients (0.2-38 years) with CD (n = 25) and non-CD etiologies (n = 14), and compared with autopsy cases (n = 13; 0-33 years). Pathology group, seizure type and age at surgery were compared against MRI and NeuN data. CD patients demonstrated increased MRI cerebral (3%) and gray matter (8%) volumes of the affected compared with non-affected cerebral hemisphere, and increased layer 1 (131%), upper cortical (9-23%) and white matter (28-77%) NeuN densities compared with autopsy cases. Non-CD cases showed decreased cerebral volumes of the affected hemisphere (14-18%) without changes in NeuN densities. Compared with autopsy cases, in CD and non-CD patients, cortical neurons were hypertrophied. Patients with a history of infantile spasms had a 40% increase in the size of layer 1 neurons compared with cases without spasms. By age, regardless of pathology group, there were logarithmic increases in MRI cerebral and white matter volumes, logarithmic increases in the size of lower gray and superficial white matter neurons, and logarithmic decreases in gray and white matter neuronal densities. These results support the concept that there were more neurons than expected in layer 1, gray, and white matter of CD patients compared with non-CD and autopsy cases. In addition, the location and appearance of neurons are consistent with the hypothesis that CD is the consequence of abnormalities occurring late in corticoneurogenesis that involve excessive neurogenesis with retention of pre-plate cells in the molecular layer and subplate regions.

Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxel-based morphometry

BackgroundStructural magnetic resonance imaging (MRI) studies of regions of interest in brain have been inconsistent in demonstrating volumetric differences in subjects with bipolar disorder (BD). Voxel-based morphometry (VBM) provides an unbiased survey of the brain, can identify novel brain areas, and validates previously hypothesized regions. We conducted both optimized VBM, comparing MRI gray matter volume, and traditional VBM, comparing MRI gray matter density, in 11 BD subjects and 31 healthy volunteers. To our knowledge, these are the first VBM analyses of BD. MethodsSegmented MRI gray matter images were normalized into standardized stereotactic space, modulated to allow volumetric analysis (optimized only), smoothed, and compared at the voxel level with statistical parametric mapping. ResultsOptimized VBM showed that BD subjects had smaller volume in left ventromedial temporal cortex and bilateral cingulate cortex and larger volume in left insular/frontoparietal operculum cortex and left ventral occipitotemporal cortex. Traditional VBM showed that BD subjects had less gray matter density in left ventromedial temporal cortex and greater gray matter density in left insular/frontoparietal operculum cortex and bilateral thalamic cortex. Exploratory analyses suggest that these abnormalities might differ according to gender. ConclusionsBipolar disorder is associated with volumetric and gray matter density changes that involve brain regions hypothesized to influence mood.

Progressive decrease of left Heschl gyrus and planum temporale gray matter volume in first-episode schizophrenia: a longitudinal magnetic resonance imaging study.

The Heschl gyrus and planum temporale have crucial roles in auditory perception and language processing. Our previous investigation using magnetic resonance imaging (MRI) indicated smaller gray matter volumes bilaterally in the Heschl gyrus and in left planum temporale in patients with first-episode schizophrenia but not in patients with first-episode affective psychosis. We sought to determine whether there are progressive decreases in anatomically defined MRI gray matter volumes of the Heschl gyrus and planum temporale in patients with first-episode schizophrenia and also in patients with first-episode affective psychosis. At a private psychiatric hospital, we conducted a prospective high spatial resolution MRI study that included initial scans of 28 patients at their first hospitalization (13 with schizophrenia and 15 with affective psychosis, 13 of whom had a manic psychosis) and 22 healthy control subjects. Follow-up scans occurred, on average, 1.5 years after the initial scan. Patients with first-episode schizophrenia showed significant decreases in gray matter volume over time in the left Heschl gyrus (6.9%) and left planum temporale (7.2%) compared with patients with first-episode affective psychosis or control subjects. These findings demonstrate a left-biased progressive volume reduction in the Heschl gyrus and planum temporale gray matter in patients with first-episode schizophrenia in contrast to patients with first-episode affective psychosis and control subjects. Schizophrenia but not affective psychosis seems to be characterized by a postonset progression of neocortical gray matter volume loss in the left superior temporal gyrus and thus may not be developmentally fixed.