Gray Matter-Based Age Prediction Characterizes Different Regional Patterns.
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ABSTRACT:
SUBMITTER: Zuo N
PROVIDER: S-EPMC7811971 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE ε4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.
| S-EPMC6800321 | biostudies-literature
Project description:Reduced gray matter (GM) volume may represent a hallmark of major depressive disorder (MDD) neuropathology, typified by wide-ranging distribution of structural alteration. In the study, we aimed to replicate and extend our previous finding of profound and widespread GM loss in MDD, and evaluate the diagnostic accuracy of a structural biomarker derived from GM volume in an interconnected pattern across the brain. In a sub-study of the International Study to Predict Optimized Treatment in Depression (iSPOT-D), two cohorts of clinically defined MDD participants "Test" (n = 98) and "Replication" (n = 131) were assessed alongside healthy controls (n = 66). Using 3T MRI T1-weighted volumes, GM volume differences were evaluated using voxel-based morphometry. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to evaluate an MDD diagnostic biomarker based on a precise spatial pattern of GM loss constructed using principal component analysis. We demonstrated a highly conserved symmetric widespread pattern of reduced GM volume in MDD, replicating our previous findings. Three bilateral dominant clusters were observed: Cluster 1: midline/cingulate (GM reduction: Test: 6.4%, Replication: 5.3%), Cluster 2: medial temporal lobe (GM reduction: Test: 8.2%, Replication: 11.9%), Cluster 3: prefrontal cortex (GM reduction: Test: 12.1%, Replication: 23.2%). We developed a biomarker reflecting the global pattern of GM reduction, achieving good diagnostic classification performance (AUC: Test = 0.75, Replication = 0.84). This study establishes that a highly specific pattern of reduced GM volume is a feature of MDD, suggestive of a structural basis for this disease. We introduce and validate a novel diagnostic biomarker based on this pattern.
| S-EPMC6656728 | biostudies-literature
Project description:BackgroundDespite decades of research, there is continued uncertainty regarding whether autism is associated with a specific profile of gray matter (GM) structure. This inconsistency may stem from the widespread use of voxel-based morphometry (VBM) methods that combine indices of GM density and GM volume. If GM density or volume, but not both, prove different in autism, the traditional VBM approach of combining the two indices may obscure the difference. The present study measures GM density and volume separately to examine whether autism is associated with alterations in GM volume, density, or both.MethodsDifferences in MRI-based GM density and volume were examined in 6-25 year-olds with a diagnosis of autism spectrum disorder (n = 213, 80.8% male, IQ 47-154) and a typically developing (TD) sample (n = 190, 71.6% male, IQ 67-155). High-resolution T1-weighted anatomical images were collected on a single MRI scanner. Regional density and volume were estimated via whole-brain parcellation comprised of 1625 parcels. Parcel-wise analyses were conducted using generalized additive models while controlling the false discovery rate (FDR, q < 0.05). Volume differences in the 68-region Desikan-Killiany atlas derived from Freesurfer were also examined, to establish the generalizability of findings across methods.ResultsNo density differences were observed between the autistic and TD groups, either in individual parcels or whole brain mean density. Increased volume was observed in autism compared to the TD group when controlling for Age, Sex, and IQ, both at the level of the whole brain (total volume) and in 45 parcels (2.8% of total parcels). Parcels with greater volume included inferior, middle, and superior temporal gyrus, inferior and superior frontal gyrus, precuneus, and fusiform gyrus. Converging evidence from a standard Freesurfer pipeline also identified greater volume in a number of overlapping regions.LimitationsThe method for determining "density" is not a direct measure of neuronal density, and this study cannot reveal underlying cellular differences. While this study represents possibly the largest single-site sample of its kind, it is underpowered to detect very small differences.ConclusionsThese results provide compelling evidence that autism is associated with regional GM volumetric differences, which are more prominent than density differences. This underscores the importance of examining volume and density separately, and suggests that direct measures of volume (e.g. region-of-interest or tensor-based morphometry approaches) may be more sensitive to autism-relevant differences in neuroanatomy than concentration/density-based approaches.
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Project description:BackgroundAdolescence is a critical time for brain development. Findings from previous studies have been inconsistent, failing to distinguish the influence of pubertal status and aging on brain maturation. The current study sought to address these inconsistencies, addressing the trajectories of pubertal development and aging by longitudinally tracking structural brain development during adolescence.MethodsTwo cohorts of healthy children were recruited (cohort 1: 9-10 years old; cohort 2: 12-13 years old at baseline). MRI data were acquired for gray matter volume and white matter tract measures. To determine whether age, pubertal status, both or their interaction best modelled longitudinal data, we compared four multi-level linear regression models to the null model (general brain growth indexed by total segmented volume) using Bayesian model selection.ResultsData were collected at baseline (n = 116), 12 months (n = 97) and 24 months (n = 84) after baseline. Findings demonstrated that the development of most regional gray matter volume, and white matter tract measures, were best modelled by age. Interestingly, precentral and paracentral regions of the cortex, as well as the accumbens demonstrated significant preference for the pubertal status model. None of the white matter tract measures were better modelled by pubertal status.LimitationsThe major limitation of this study is the two-cohort recruitment. Although this allowed a faster coverage of the age span, a complete per person trajectory over 6 years of development (9-15 years) could not be investigated.ConclusionsComparing the impact of age and pubertal status on regional gray matter volume and white matter tract measures, we found age to best predict longitudinal changes. Further longitudinal studies investigating the differential influence of puberty status and age on brain development in more diverse samples are needed to replicate the present results and address mechanisms underlying norm-variants in brain development.
| S-EPMC7981330 | biostudies-literature
Project description:BackgroundPrevious studies have identified brain areas related to cognitive abilities and personality, respectively. In this exploratory study, we extend the application of modern neuroimaging techniques to another area of individual differences, vocational interests, and relate the results to an earlier study of cognitive abilities salient for vocations.FindingsFirst, we examined the psychometric relationships between vocational interests and abilities in a large sample. The primary relationships between those domains were between Investigative (scientific) interests and general intelligence and between Realistic ("blue-collar") interests and spatial ability. Then, using MRI and voxel-based morphometry, we investigated the relationships between regional gray matter volume and vocational interests. Specific clusters of gray matter were found to be correlated with Investigative and Realistic interests. Overlap analyses indicated some common brain areas between the correlates of Investigative interests and general intelligence and between the correlates of Realistic interests and spatial ability.ConclusionsTwo of six vocational-interest scales show substantial relationships with regional gray matter volume. The overlap between the brain correlates of these scales and cognitive-ability factors suggest there are relationships between individual differences in brain structure and vocations.
| S-EPMC3476449 | biostudies-literature
Project description:BackgroundBoth gray-matter (GM) atrophy and lesions occur from the earliest stages of Multiple Sclerosis (MS) and are one of the major determinants of long-term clinical outcomes. Nevertheless, the relationship between focal and diffuse GM damage has not been clarified yet. Here we investigate the regional distribution and temporal evolution of cortical thinning and how it is influenced by the local appearance of new GM lesions at different stages of the disease in different populations of MS patients.MethodsWe studied twenty MS patients with clinically isolated syndrome (CIS), 27 with early relapsing-remitting MS (RRMS, disease duration <5 years), 29 with late RRMS (disease duration ≥ 5 years) and 20 with secondary-progressive MS (SPMS). The distribution and evolution of regional cortical thickness and GM lesions were assessed during 5-year follow-up.ResultsThe results showed that new lesions appeared more frequently in hippocampus and parahippocampal gyri (9.1%), insula (8.9%), cingulate cortex (8.3%), superior frontal gyrus (8.1%), and cerebellum (6.5%). The aforementioned regions showed the greatest reduction in thickness/volume, although (several) differences were observed across subgroups. The correlation between the appearance of new cortical lesions and cortical thinning was stronger in CIS (r2 = 50.0, p<0.001) and in early RRMS (r2 = 52.3, p<0.001), compared to late RRMS (r2 = 25.5, p<0.001) and SPMS (r2 = 6.3, p = 0.133).ConclusionsWe conclude that GM atrophy and lesions appear to be different signatures of cortical disease in MS having in common overlapping spatio-temporal distribution patterns. However, the correlation between focal and diffuse damage is only moderate and more evident in the early phase of the disease.
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Project description:Although regular physical exercise has multiple positive benefits for the general population, excessive exercise may lead to exercise dependence (EXD), which is harmful to one's physical and mental health. Increasing evidence suggests that stress is a potential risk factor for the onset and development of EXD. However, little is known about the neural substrates of EXD and the underlying neuropsychological mechanism by which stress affects EXD. Herein, we investigate these issues in 86 individuals who exercise regularly by estimating their cortical gray matter volume (GMV) utilizing a voxel-based morphometry method based on structural magnetic resonance imaging. Whole-brain correlation analyses and prediction analyses showed negative relationships between EXD and GMV of the right orbitofrontal cortex (OFC), left subgenual cingulate gyrus (sgCG), and left inferior parietal lobe (IPL). Furthermore, mediation analyses found that the GMV of the right OFC was an important mediator between stress and EXD. Importantly, these results remained significant even when adjusting for sex, age, body mass index, family socioeconomic status, general intelligence and total intracranial volume, as well as depression and anxiety. Collectively, the results of the present study provide crucial evidence of the neuroanatomical basis of EXD and reveal a potential neuropsychological pathway in predicting EXD in which GMV mediates the relationship between stress and EXD.
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Project description:Gender identity-one's sense of being a man or a woman-is a fundamental perception experienced by all individuals that extends beyond biological sex. Yet, what contributes to our sense of gender remains uncertain. Since individuals who identify as transsexual report strong feelings of being the opposite sex and a belief that their sexual characteristics do not reflect their true gender, they constitute an invaluable model to understand the biological underpinnings of gender identity. We analyzed MRI data of 24 male-to-female (MTF) transsexuals not yet treated with cross-sex hormones in order to determine whether gray matter volumes in MTF transsexuals more closely resemble people who share their biological sex (30 control men), or people who share their gender identity (30 control women). Results revealed that regional gray matter variation in MTF transsexuals is more similar to the pattern found in men than in women. However, MTF transsexuals show a significantly larger volume of regional gray matter in the right putamen compared to men. These findings provide new evidence that transsexualism is associated with distinct cerebral pattern, which supports the assumption that brain anatomy plays a role in gender identity.
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Project description:Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.
| S-EPMC4535628 | biostudies-literature
Project description:Healthy brain development involves changes in brain structure and function that are believed to support cognitive maturation. However, understanding how structural changes such as grey matter thinning relate to functional changes is challenging. To gain insight into structure-function relationships in development, the present study took a data driven approach to define age-related patterns of variation in gray matter volume (GMV), cerebral blood flow (CBF) and blood-oxygen level dependent (BOLD) signal variation (fractional amplitude of low-frequency fluctuations; fALFF) in 59 healthy children aged 7-18 years, and examined relationships between modalities. Principal components analysis (PCA) was applied to each modality in parallel, and participant scores for the top components were assessed for age associations. We found that decompositions of CBF, GMV and fALFF all included components for which scores were significantly associated with age. The dominant patterns in GMV and CBF showed significant (GMV) or trend level (CBF) associations with age and a strong spatial overlap, driven by increased signal intensity in default mode network (DMN) regions. GMV, CBF and fALFF additionally showed components accounting for 3-5% of variability with significant age associations. However, these patterns were relatively spatially independent, with small-to-moderate overlap between modalities. Independence of age effects was further demonstrated by correlating individual subject maps between modalities: CBF was significantly less correlated with GMV and fALFF in older children relative to younger. These spatially independent effects of age suggest that the parallel decline observed in global GMV and CBF may not reflect spatially synchronized processes. Hum Brain Mapp 38:2398-2407, 2017. © 2017 Wiley Periodicals, Inc.
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