Gray Matter-Based Age Prediction Characterizes Different Regional Patterns.
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ABSTRACT:
SUBMITTER: Zuo N
PROVIDER: S-EPMC7811971 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
Gray Matter-Based Age Prediction Characterizes Different Regional Patterns.
Zuo Nianming N Hu Tianyu T Liu Hao H Sui Jing J Liu Yong Y Jiang Tianzi T
Neuroscience bulletin 20200819 1
Similar Datasets
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: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: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.
| S-EPMC8633367 | biostudies-literature
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.
| S-EPMC4534410 | biostudies-literature
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.
| S-EPMC8449116 | biostudies-literature
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.
| S-EPMC2754583 | biostudies-literature
Project description:Cognitive aging varies tremendously across individuals and is often accompanied by regionally specific reductions in gray matter (GM) volume, even in the absence of disease. Rhesus monkeys provide a primate model unconfounded by advanced neurodegenerative disease, and the current study used a recognition memory test (delayed non-matching to sample; DNMS) in conjunction with structural imaging and voxel-based morphometry (VBM) to characterize age-related differences in GM volume and brain-behavior relationships. Consistent with expectations from a long history of neuropsychological research, DNMS performance in young animals prominently correlated with the volume of multiple structures in the medial temporal lobe memory system. Less anticipated correlations were also observed in the cingulate and cerebellum. In aged monkeys, significant volumetric correlations with DNMS performance were largely restricted to the prefrontal cortex and striatum. Importantly, interaction effects in an omnibus analysis directly confirmed that the associations between volume and task performance in the MTL and prefrontal cortex are age-dependent. These results demonstrate that the regional distribution of GM volumes coupled with DNMS performance changes across the lifespan, consistent with the perspective that the aged primate brain retains a substantial capacity for structural reorganization.
| S-EPMC8889942 | biostudies-literature
Project description:ImportancePsychotic illness is associated with anatomically distributed gray matter reductions that can worsen with illness progression, but the mechanisms underlying the specific spatial patterning of these changes is unknown.ObjectiveTo test the hypothesis that brain network architecture constrains cross-sectional and longitudinal gray matter alterations across different stages of psychotic illness and to identify whether certain brain regions act as putative epicenters from which volume loss spreads.Design, settings, and participantsThis case-control study included 534 individuals from 4 cohorts, spanning early and late stages of psychotic illness. Early-stage cohorts included patients with antipsychotic-naive first-episode psychosis (n = 59) and a group of patients receiving medications within 3 years of psychosis onset (n = 121). Late-stage cohorts comprised 2 independent samples of people with established schizophrenia (n = 136). Each patient group had a corresponding matched control group (n = 218). A sample of healthy adults (n = 356) was used to derive representative structural and functional brain networks for modeling of network-based spreading processes. Longitudinal illness-related and antipsychotic-related gray matter changes over 3 and 12 months were examined using a triple-blind randomized placebo-control magnetic resonance imaging study of the antipsychotic-naive patients. All data were collected between April 29, 2008, and January 15, 2020, and analyses were performed between March 1, 2021, and January 14, 2023.Main outcomes and measuresCoordinated deformation models were used to estimate the extent of gray matter volume (GMV) change in each of 332 parcellated areas by the volume changes observed in areas to which they were structurally or functionally coupled. To identify putative epicenters of volume loss, a network diffusion model was used to simulate the spread of pathology from different seed regions. Correlations between estimated and empirical spatial patterns of GMV alterations were used to quantify model performance.ResultsOf 534 included individuals, 354 (66.3%) were men, and the mean (SD) age was 28.4 (7.4) years. In both early and late stages of illness, spatial patterns of cross-sectional volume differences between patients and controls were more accurately estimated by coordinated deformation models constrained by structural, rather than functional, network architecture (r range, >0.46 to <0.57; P < .01). The same model also robustly estimated longitudinal volume changes related to illness (r ≥ 0.52; P < .001) and antipsychotic exposure (r ≥ 0.50; P < .004). Network diffusion modeling consistently identified, across all 4 data sets, the anterior hippocampus as a putative epicenter of pathological spread in psychosis. Epicenters of longitudinal GMV loss were apparent in posterior cortex early in the illness and shifted to the prefrontal cortex with illness progression.Conclusion and relevanceThese findings highlight a central role for white matter fibers as conduits for the spread of pathology across different stages of psychotic illness, mirroring findings reported in neurodegenerative conditions. The structural connectome thus represents a fundamental constraint on brain changes in psychosis, regardless of whether these changes are caused by illness or medication. Moreover, the anterior hippocampus represents a putative epicenter of early brain pathology from which dysfunction may spread to affect connected areas.
| S-EPMC10512169 | biostudies-literature