Correlating Gray Matter Volume with Individual Difference in the Flanker Interference Effect.
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ABSTRACT:
SUBMITTER: Chen C
PROVIDER: S-EPMC4554993 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sleep deprivation disrupted functional and structural brain areas which are associated with cognition and emotion in healthy participants. However, the effect of age on the structural changes after sleep restriction remains unclear. In the current study, gray matter volume was calculated in 43 young adults and 37 old adults before and after sleep restriction. Two-way mixed analysis of variance (between-subject factor: deprivation; within-subject factor: age) was then employed to investigate differences in gray matter volume changes between young and old adults. Gaussian random field theory was applied for multiple comparison correction. Results revealed that sleep restriction decreases gray matter volume in the right thalamus, left precuneus, and postcentral gyrus. More importantly, we found a significant deprivation × age interaction effect mainly in the right dorsal/ventral anterior insula where the gray matter volume increased in young adults after sleep restriction but showed no difference in old adults. These findings highlight the crucial role of the anterior insula in the neural mechanisms underlying sleep lose, especially among young adults. The current work provided structural evidence for describing emotional dysfunction and suggests the potential effect of age on functional and structural changes after sleep restriction.
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Project description:BackgroundThe anterior insula cortex is considered to be both the structural and functional link between experience, affect, and behaviour. Magnetic resonance imaging (MRI) studies have shown changes in anterior insula gray matter volume (GMV) in psychosis, bipolar, depression and anxiety disorders in older patients, but few studies have investigated insula GMV changes in young people. This study examined the relationship between anterior insula GMV, clinical symptom severity and neuropsychological performance in a heterogeneous cohort of young people presenting for mental health care.MethodsParticipants with a primary diagnosis of depression (n = 43), bipolar disorder (n = 38), psychosis (n = 32), anxiety disorder (n = 12) or healthy controls (n = 39) underwent structural MRI scanning, and volumetric segmentation of the bilateral anterior insula cortex was performed using the FreeSurfer application. Statistical analysis examined the linear and quadratic correlations between anterior insula GMV and participants' performance in a battery of clinical and neuropsychological assessments.ResultsCompared to healthy participants, patients had significantly reduced GMV in the left anterior insula (t = 2.05, p = .042) which correlated with reduced performance on a neuropsychological task of attentional set-shifting (ρ = .32, p = .016). Changes in right anterior insula GMV was correlated with increased symptom severity (r = .29, p = .006) and more positive symptoms (r = .32, p = .002).ConclusionsBy using the novel approach of examining a heterogeneous cohort of young depression, anxiety, bipolar and psychosis patients together, this study has demonstrated that insula GMV changes are associated with neurocognitive deficits and clinical symptoms in such young patients.
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Project description:BackgroundVascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer's disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).MethodsA total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.ResultsMulti-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.ConclusionsBased on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.
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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:A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
| S-EPMC7473979 | biostudies-literature
Project description:The anti-apoptotic protein B-cell CLL/lymphoma 2 (Bcl-2) gene is a major regulator of neural plasticity and cellular resilience. Recently, the Bcl-2 rs956572 single nucleotide polymorphism was proposed to be a functional allelic variant that modulates cellular vulnerability to apoptosis. Our cross-sectional study investigated the genetic effect of this Bcl-2 polymorphism on age-related decreases in gray matter (GM) volume across the adult lifespan. Our sample comprised 330 healthy volunteers (191 male, 139 female) with a mean age of 56.2±22.0 years (range: 21-92). Magnetic resonance imaging and genotyping of the Bcl-2 rs956572 were performed for each participant. The differences in regional GM volumes between G homozygotes and A-allele carriers were tested using optimized voxel-based morphometry. The association between the Bcl-2 rs956572 polymorphism and age was a predictor of regional GM volumes in the right cerebellum, bilateral lingual gyrus, right middle temporal gyrus, and right parahippocampal gyrus. We found that the volume of these five regions decreased with increasing age (all P<.001). Moreover, the downward slope was steeper among the Bcl-2 rs956572 A-allele carriers than in the G-homozygous participants. Our data provide convergent evidence for the genetic effect of the Bcl-2 functional allelic variant in brain aging. The rs956572 G-allele, which is associated with significantly higher Bcl-2 protein expression and diminished cellular sensitivity to stress-induced apoptosis, conferred a protective effect against age-related changes in brain GM volume, particularly in the cerebellum.
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Project description:Although brain gray matter (GM) plastically changes during short-term training, it is still unclear whether brain structures are stable for short periods (several months). Therefore, this study aimed to re-test the short-term variability of GM volumes and to clarify the effect of factors (gender and BDNF-genotype) expected to contribute to such variability. The subjects comprised 41 young healthy adults. T1-weighted images were acquired twice with an interval of approximately 4 months using a 3 T-MRI scanner. Voxel-based morphometry (VBM) was used to calculate GM volumes in 47 regions. The intraclass correlation coefficient (ICC) and Test-retest variability (%TRV) were used as indices of variability. As a result, the ICCs in 43 regions were excellent (ICC > 0.90) and those in 3 regions were good (ICC > 0.80), whereas the ICC in the thalamus was moderate (ICC = 0.694). Women had a higher %TRV than men in 5 regions, and %TRV of the Val66Val group was higher than that of the Met carrier group in 2 regions. Moreover, the Female-Val66Val group had a higher %TRV than the Male-Met carrier group in 3 regions. These results indicate that although the short-term variability of GM volumes is small, it is affected by within-subject factors.
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Project description:ObjectiveChefs exert expert motor and cognitive performances on a daily basis. Neuroimaging has clearly shown that that long-term skill learning (i.e., athletes, musicians, chess player or sommeliers) induces plastic changes in the brain thus enabling tasks to be performed faster and more accurately. How a chef's expertise is embodied in a specific neural network has never been investigated.MethodsEleven Italian head chefs with long-term brigade management expertise and 11 demographically-/ psychologically- matched non-experts underwent morphological evaluations.ResultsVoxel-based analysis performed with SUIT, as well as, automated volumetric measurement assessed with Freesurfer, revealed increased gray matter volume in the cerebellum in chefs compared to non-experts. The most significant changes were detected in the anterior vermis and the posterior cerebellar lobule. The magnitude of the brigade staff and the higher performance in the Tower of London test correlated with these specific gray matter increases, respectively.ConclusionsWe found that chefs are characterized by an anatomical variability involving the cerebellum. This confirms the role of this region in the development of similar expert brains characterized by learning dexterous skills, such as pianists, rock climbers and basketball players. However, the nature of the cellular events underlying the detected morphological differences remains an open question.
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Project description:Bilateral vestibular failure (BVF) is a severe chronic disorder of the labyrinth or the eighth cranial nerve characterized by unsteadiness of gait and disabling oscillopsia during head movements. According to animal data, vestibular input to the hippocampus is proposed to contribute to spatial memory and spatial navigation. Except for one seminal study showing the association of impaired spatial navigation and hippocampal atrophy, patient data in BVF are lacking. Therefore, we performed a voxel-wise comparison of the hippocampal gray matter volume (GMV) in a clinically representative sample of 27 patients with incomplete BVF and 29 age- and gender-matched healthy controls to test the hypothesis of hippocampal atrophy in BVF. Although the two groups did not generally differ in their hippocampal GMV, a reduction of GMV in the bilateral hippocampal CA3 region was significantly correlated with increased vestibulopathy-related clinical impairment. We propose that GMV reduction in the hippocampus of BVF patients is related to the severity of vestibular-induced disability which is in line with combined hippocampal atrophy and disorders of spatial navigation in complete vestibular deafferentation due to bilateral nerve section. Clinically, however, the most frequent etiologies of BVF cause incomplete lesions. Accordingly, hippocampus atrophy and deficits in spatial navigation occur possibly less frequently than previously suspected. Hum Brain Mapp 37:1998-2006, 2016. © 2016 Wiley Periodicals, Inc.
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Project description:Diet and nutrition play a key role in the promotion and maintenance of good health, as they are important modifiable risk factors for chronic diseases. A growing number of studies indicate that optimal food intake and optimal physical activity are essential for the gray matter volume (GMV). However, the precise definition of "optimal" is extremely difficult and a topic of several studies. In the current research, we used the magnetic resonance imaging (MRI)-based normalized GMV (nGMV), for monitoring brain conditions based on GMV. By analyzing the relationship between the nGMV of 171 healthy Japanese participants and the results of a brief self-administered diet history questionnaire (BDHQ), we found that while nGMV was high in the participants with high intake of milk and yogurt, it was low in the participants of "alcohol and animal foods dietary pattern" (high intake of alcohol and animal foods). On the other hand, another food pattern "vegetable-animal balanced dietary pattern" (balanced intake of vegetables and animal foods) has no significant association with nGMV, indicating that although a diet consisting of a good balance of vegetables and animal foods may not lead to brain atrophy, it might not positively contribute to a higher nGMV. nGMV, as an objective measure of the association between food intake and the brain, might provide useful information for "optimal" food intake for GMV.
| S-EPMC6828934 | biostudies-literature