Gray Matter Volume Covariance Research Articles

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia.

Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.

Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Structural covariances of prefrontal subregions selectively associate with dopamine-related gene coexpression and schizophrenia.

Evidence highlights that dopamine (DA) system dysregulation and prefrontal cortex (PFC) dysfunction may underlie the pathophysiology of schizophrenia. However, the associations among DA genes, PFC morphometry, and schizophrenia have not yet been fully clarified. Based on the brain gene expression dataset from Allen Human Brain Atlas and structural magnetic resonance imaging data (NDIS = 1727, NREP = 408), we first identified 10 out of 22 PFC subregions whose gray matter volume (GMV) covariance profiles were reliably associated with their DA genes coexpression profiles, then four out of the identified 10 PFC subregions demonstrated abnormally increased GMV covariance with the hippocampus, insula, and medial frontal areas in schizophrenia patients (NCASE = 100; NCONTROL=102). Moreover, based on a schizophrenia postmortem expression dataset, we found that the DA genes coexpression of schizophrenia was significantly reduced between the middle frontal gyrus and hippocampus, in which 21 DA genes showed significantly unsynchronized expression changes, and the 21 genes' brain expression were enriched in brain activity invoked by working memory, reward, speech production, and episodic memory. Our findings indicate the DA genes selectively regulate the structural covariance of PFC subregions by their coexpression profiles, which may underlie the disrupted GMV covariance and impaired cognitive functions in schizophrenia.

Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy.

White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations in midlife adults. Two hundred and fifty‐four participants from the Coronary Artery Risk Development in Young Adults study were selected and stratified by WMH burden into Lo‐WMH (mean age = 50 ± 3.5 years) and Hi‐WMH (mean age = 51 ± 3.7 years) groups of equal size. We constructed group‐level covariance networks based on cerebral blood flow (CBF) and gray matter volume (GMV) maps across 74 gray matter regions. Through consensus clustering, we found that both CBF and GMV covariance networks partitioned into modules that were largely consistent between groups. Next, CBF and GMV covariance network topologies were compared between Lo‐ and Hi‐WMH groups at global (clustering coefficient, characteristic path length, global efficiency) and regional (degree, betweenness centrality, local efficiency) levels. At the global level, there were no between‐group differences in either CBF or GMV covariance networks. In contrast, we found between‐group differences in the regional degree, betweenness centrality, and local efficiency of several brain regions in both CBF and GMV covariance networks. Overall, CBF and GMV covariance analyses provide evidence that WMH‐related network alterations are present at midlife.

Gray matter volume covariance networks are associated with altered emotional processing in bipolar disorder: a source-based morphometry study

Widespread regional gray matter volume (GMV) alterations have been reported in bipolar disorder (BD). Structural networks, which are thought to better reflect the complex multivariate organization of the brain, and their clinical and psychological function have not been investigated yet in BD. 24 patients with BD type-I (BD-I), and 30 with BD type-II (BD-II), and 45 controls underwent MRI scan. Voxel-based morphometry and source-based morphometry (SBM) were performed to extract structural covariation patterns of GMV. SBM components associated with morphometric differences were compared among diagnoses. Executive function and emotional processing correlated with morphometric characteristics. Compared to controls, BD-I showed reduced GMV in the temporo-insular-parieto-occipital cortex and in the culmen. An SBM component spanning the prefrontal-temporal-occipital network exhibited significantly lower GMV in BD-I compared to controls, but not between the other groups. The structural network covariance in BD-I was associated with the number of previous manic episodes and with worse executive performance. Compared to BD-II, BD-I showed a loss of GMV in the temporal-occipital regions, and this was correlated with impaired emotional processing. Altered prefrontal-temporal-occipital network structure could reflect a neural signature associated with visuospatial processing and problem-solving impairments as well as emotional processing and illness severity in BD-I.

Cingulate networks associated with gray matter loss in Parkinson's disease show high expression of cholinergic genes in the healthy brain.

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance across a population. We examined the transcriptomic signature of such anatomical networks in the healthy brain using postmortem microarray data from the Allen Human Brain Atlas. A previous study revealed that a posterior cingulate network and anterior cingulate network showed decreased gray matter in brains of Parkinson's disease patients. Therefore, we examined these two anatomical networks to understand the underlying molecular processes that may be involved in Parkinson's disease. Whole brain transcriptomics from the healthy brain revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS‐signaling pathways. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a functional role. Finally, both networks were enriched for genes associated with neuropsychiatric disorders that overlap with Parkinson's disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinson's disease.

Gray matter reorganization underpinnings of antidepressant treatment of persistent depressive disorder

Brain gray matter is organized in a manner with interconnected brain regions, resulting in a notable covariance pattern that recapitulates either the functional coactivation or structural connectivity of brain regions, which is believed to underpin psychiatric disorders such as depression. This study aimed to investigate whether and how antidepressants took effect in treating depression and reducing symptoms by altering the gray matter covariance pattern. We combined structural magnetic resonance imaging (MRI) scans acquired in two randomized, double-blind, placebo-controlled trial (RCT) studies of the treatment using serotonin-norepinephrine reuptake inhibitor (SNRI) antidepressant medications in patients with persistent depressive disorder (PDD). One was an RCT of 10-week duloxetine medication that consisted of patients who received duloxetine (N = 21) or placebo (N = 21), and the other was an RCT of 12-week desvenlafaxine medication that consisted of 19 and 17 patients respectively who received desvenlafaxine or placebo. We examined treatment effect on gray matter volume (GMV) and topological organization of GMV covariance pattern (i.e., GMV-based network). We found a treatment-by-time effect on GMV in the angular gyrus and cuneus areas, whereas the GMV change rate of the cuneus was inversely correlated with the response rate. We observed a significant increase in the local efficiency of the GMV-based network following medication treatment compared with placebo. Our findings provide preliminary evidence for a GMV-based network-specific reconfiguration caused by antidepressants compared to placebo and the cuneus may be a possible candidate region to predict antidepressant response.

The atypical social brain network in autism: advances in structural and functional MRI studies.

To review advances in structural and functional MRI studies regarding the neural underpinnings of social atypicalities in autism spectrum disorder (ASD). According to the hypothesis that the social brain network, which includes brain regions, such as the amygdala and superior temporal sulcus, may be atypical in ASD, recent structural MRI studies have identified regional gray matter volume abnormalities in the social brain regions in ASD groups compared with the typically developing groups. Studies evaluating gray matter volume covariance and white matter volume/integrity suggested network-level abnormalities associated with the social brain regions. Recent functional MRI studies assessing resting-state neural activity showed reduced functional connectivity among the social brain regions in individuals with ASD compared with typically developing groups. Similarly, task-based functional MRI studies recently revealed a reduction in regional activity and intraregional functional coupling in the social brain regions during the processing of social stimuli in individuals with ASD. These structural and functional MRI studies provide supportive evidence for the hypothesis that an atypical social brain network underlies behavioral social problems in ASD.

Parcellation of the human hippocampus based on gray matter volume covariance: Replicable results on healthy young adults.

The hippocampus is a key brain region that participates in a range of cognitive and affective functions, and is involved in the etiopathogenesis of numerous neuropsychiatric disorders. The structural complexity and functional diversity of the hippocampus suggest the existence of structural and functional subdivisions within this structure. For the first time, we parcellated the human hippocampus with two independent data sets, each of which consisted of 198 T1-weighted structural magnetic resonance imaging (sMRI) images of healthy young subjects. The method was based on gray matter volume (GMV) covariance, which was quantified by a bivariate voxel-to-voxel linear correlation approach, as well as a multivariate masked independent component analysis approach. We subsequently interrogated the relationship between the GMV covariance patterns and the functional connectivity patterns of the hippocampal subregions using sMRI and resting-state functional MRI (fMRI) data from the same participants. Seven distinct GMV covariance-based subregions were identified for bilateral hippocampi, with robust reproducibility across the two data sets. We further demonstrated that the structural covariance patterns of the hippocampal subregions had a correspondence with the intrinsic functional connectivity patterns of these subregions. Together, our results provide a topographical configuration of the hippocampus with converging structural and functional support. The resulting subregions may improve our understanding of the hippocampal connectivity and functions at a subregional level, which provides useful parcellations and masks for future neuroscience and clinical research on the structural and/or functional connectivity of the hippocampus.

Gray Matter Volume Covariance Networks, Social Support, and Cognition in Older Adults.

We examined the neural substrates of social support in older adults. Social support is associated with better outcomes in many facets of aging-including cognitive and functional health-but the underlying neural substrates remain largely unexplored. Voxel-based morphometry and multivariate statistics were used to identify gray matter volume covariance networks associated with social support in 112 older adults without dementia (M age = 74.6 years, 50% female), using the Medical Outcomes Study Social Support Survey. A gray matter network associated with overall social support was identified and included prefrontal, hippocampal, amygdala, cingulate, and thalamic regions. A gray matter network specifically associated with tangible social support (e.g., someone to help you if you were confined to bed) was also identified, included prefrontal, hippocampal, cingulate, insular, and thalamic regions, and correlated with memory and executive function. Gray matter networks associated with overall and tangible social support in this study were composed of regions previously associated with memory, executive function, aging, and dementia. Longitudinal research of the interrelationships between social support, brain structure, and cognition is needed, but strengthening social support may represent a new path toward improving cognition in aging that should be explored.

Gray matter volume covariance networks associated with dual-task cost during walking-while-talking.

We studied gray matter volume covariance networks associated with normal pace walking (NPW) speed and dual-task costs (DTCs) during walking-while-talking (WWT)-a mobility stress test that involves walking while reciting alternate letters of the alphabet. Using a multivariate covariance-based analytic approach, we identified gray matter networks associated with NPW speed (mean 102.1 cm/s ±22.5 cm/s) and DTC (percent difference in gait speed between NPW and WWT, mean 25.9% ± 18.8%) in 139 older adults without dementia (M = 75.3 ± 6.1 years). The gray matter network associated with NPW was primarily composed of supplementary motor area, precuneus cortex, and the middle frontal gyrus. Greater expression of this NPW network was associated with better processing speed (trail-making test A [r = -0.30, p = 0.005]) and executive function (trail-making test B - A [r = -0.43, p < 0.0001]). The gray matter network associated with DTC was primarily composed of medial prefrontal, cingulate, and thalamic regions. Greater expression of this DTC network was associated with better episodic memory performance on the free and cued selective reminding test (r = 0.30, p = 0.007). These results suggest that NPW speed and DTC are supported by different networks, and are associated with different cognitive domains.

Gray matter volume covariance networks associated with social networks in older adults

ABSTRACTExtensive social networks are associated with better physical, mental, and cognitive health in aging, but the underlying brain substrates remain largely unexplored. Voxel-based morphometry and multivariate statistics were used to identify gray matter volume covariance networks associated with social networks in 86 older adults without dementia (M Age = 75.20 years, 53% women). Gray matter networks associated with the number of high-contact social roles and the total number of network members were identified after adjusting for age, sex, education, global health, and total intracranial volume – and shared nodes included medial, lateral and orbital prefrontal, hippocampal, precuneus, insular, and cingulate regions. Greater expression of these gray matter networks was associated with better memory scores on the Free and Cued Selective Reminding Test. A more distributed network was associated with high-contact social roles than total number of networks members – also extending into amygdala and entorhinal cortex. Thus, high-contact social roles and total number of network members in older adults are associated with gray matter networks composed of regions previously linked to memory and affected by both healthy aging and Alzheimer disease – and high-contact social roles are more strongly associated with brain structures than the total number of network members.

A Gray Matter Volume Covariance Network Associated with the Motoric Cognitive Risk Syndrome: A Multicohort MRI Study.

Motoric cognitive risk (MCR) syndrome is a predementia syndrome characterized by slow gait and cognitive complaint that predicts both Alzheimer's disease and vascular dementia. Yet, we know very little about the brain structures and brain pathologies associated with MCR. The aim of this study was to identify gray matter (GM) networks associated with MCR. We used voxel-based morphometry and multivariate covariance-based statistics to identify GM networks associated with MCR in a pooled sample of 267 older adults without dementia from three different cohorts-two North American cohorts and one French cohort. The mean age of participants was 75.63 years, 50.56% identified as female, 57.68% had ≥13 years of education, and 5.99% had a prior history of stroke. A total of 14.23% participants met criteria for MCR. We identified a significant GM volume covariance pattern that was associated with MCR-even after adjusting for age, sex, education, mild cognitive impairment, stroke, total intracranial volume, and cohort status. This GM volume covariance network was primarily composed of supplementary motor, insular, and prefrontal cortex regions. These findings suggest that MCR is primarily associated with GM atrophy in brain regions previously linked to the control aspects of gait such as motor planning and modulation rather than the motor aspects of gait such as gait initiation and maintenance.

Gray matter volume covariance patterns associated with gait speed in older adults: a multi-cohort MRI study.

Accelerated gait decline in aging is associated with many adverse outcomes, including an increased risk for falls, cognitive decline, and dementia. Yet, the brain structures associated with gait speed, and how they relate to specific cognitive domains, are not well-understood. We examined structural brain correlates of gait speed, and how they relate to processing speed, executive function, and episodic memory in three non-demented and community-dwelling older adult cohorts (Overall N = 352), using voxel-based morphometry and multivariate covariance-based statistics. In all three cohorts, we identified gray matter volume covariance patterns associated with gait speed that included brain stem, precuneus, fusiform, motor, supplementary motor, and prefrontal (particularly ventrolateral prefrontal) cortex regions. Greater expression of these gray matter volume covariance patterns linked to gait speed were associated with better processing speed in all three cohorts, and with better executive function in one cohort. These gray matter covariance patterns linked to gait speed were not associated with episodic memory in any of the cohorts. These findings suggest that gait speed, processing speed (and to some extent executive functions) rely on shared neural systems that are subject to age-related and dementia-related change. The implications of these findings are discussed within the context of the development of interventions to compensate for age-related gait and cognitive decline.

Parcellation of the human orbitofrontal cortex based on gray matter volume covariance.

The human orbitofrontal cortex (OFC) is an enigmatic brain region that cannot be parcellated reliably using diffusional and functional magnetic resonance imaging (fMRI) because there is signal dropout that results from an inherent defect in imaging techniques. We hypothesise that the OFC can be reliably parcellated into subregions based on gray matter volume (GMV) covariance patterns that are derived from artefact-free structural images. A total of 321 healthy young subjects were examined by high-resolution structural MRI. The OFC was parcellated into subregions-based GMV covariance patterns; and then sex and laterality differences in GMV covariance pattern of each OFC subregion were compared. The human OFC was parcellated into the anterior (OFCa), medial (OFCm), posterior (OFCp), intermediate (OFCi), and lateral (OFCl) subregions. This parcellation scheme was validated by the same analyses of the left OFC and the bilateral OFCs in male and female subjects. Both visual observation and quantitative comparisons indicated a unique GMV covariance pattern for each OFC subregion. These OFC subregions mainly covaried with the prefrontal and temporal cortices, cingulate cortex and amygdala. In addition, GMV correlations of most OFC subregions were similar across sex and laterality except for significant laterality difference in the OFCl. The right OFCl had stronger GMV correlation with the right inferior frontal cortex. Using high-resolution structural images, we established a reliable parcellation scheme for the human OFC, which may provide an in vivo guide for subregion-level studies of this region and improve our understanding of the human OFC at subregional levels.

Extensive brain structural network abnormality in first-episode treatment-naive patients with schizophrenia: morphometrical and covariation study

Alterations in gray matter (GM) are commonly observed in schizophrenia. Accumulating studies suggest that the brain changes associated with schizophrenia are distributed rather than focal, involving interconnected networks of areas as opposed to single regions. In the current study we aimed to explore GM volume (GMV) changes in a relatively large sample of treatment-naive first-episode schizophrenia (FES) patients using optimized voxel-based morphometry (VBM) and covariation analysis. High-resolution T1-weighted images were obtained using 3.0-T magnetic resonance imaging (MRI) from 86 first-episode drug-naive patients with schizophrenia and 86 age- and gender-matched healthy volunteers. Symptom severity was evaluated using the Positive and Negative Syndrome Scale (PANSS). GMV was assessed using optimized VBM and in 16 regions of interest (ROIs), selected on the basis of a previous meta-analysis. The relationships between GMVs in the ROIs were examined using an analysis of covariance (ANCOVA). The VBM analysis revealed that first-episode patients showed reduced GMV in the hippocampus bilaterally. The ROI analysis identified reductions in GMV in the left inferior frontal gyrus, bilateral hippocampus and right thalamus. The ANCOVA revealed different patterns of regional GMV correlations in patients and controls, including of inter- and intra-insula, inter-amygdala and insula-postcentral gyrus connections. Schizophrenia involves regional reductions in GMV and changes in GMV covariance in the insula, amygdala and postcentral gyrus. These findings were evident at the onset of the disorder, before treatment, and therefore cannot be attributable to the effects of chronic illness progression or medication.

Cerebral blood flow and gray matter volume covariance patterns of cognition in aging

Advancing age results in altered cognitive and neuroimaging-derived markers of neural integrity. Whether cognitive changes are the result of variations in brain measures remains unclear and relating the two across the lifespan poses a unique set of problems. It must be determined whether statistical associations between cognitive and brain measures truly exist and are not epiphenomenal due solely to their shared relationships with age. The purpose of this study was to determine whether cerebral blood flow (CBF) and gray matter volume (GMV) measures make unique and better predictions of cognition than age alone. Multivariate analyses identified brain-wide covariance patterns from 35 healthy young and 23 healthy older adults using MRI-derived measures of CBF and GMV related to three cognitive composite scores (i.e., memory, fluid ability, and speed/attention). These brain-cognitive relationships were consistent across the age range, and not the result of epiphenomenal associations with age and each imaging modality provided its own unique information. The CBF and GMV patterns each accounted for unique aspects of cognition and accounted for nearly all the age-related variance in the cognitive composite scores. The findings suggest that measures derived from multiple imaging modalities explain larger amounts of variance in cognition providing a more complete understanding of the aging brain.