Association Between Gray Matter Volume Variations and Energy Utilization in the Brain: Implications for Developmental Stuttering.
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ABSTRACT:
SUBMITTER: Boley N
PROVIDER: S-EPMC8740693 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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 role of gonadal hormones in neural plasticity remains unclear. This study aimed to examine the effects of naturally fluctuating hormone levels over the menstrual cycle in healthy females. Gray matter, functional connectivity (FC) and white matter changes over the cycle were assessed by using functional magnetic resonance imaging (fMRI), resting state fMRI, and structural MRIs, respectively, and associated with serum gonadal hormone levels. Moreover, electrocutaneous sensitivity was evaluated in 14 women in four phases of their menstrual cycle (menstrual, follicular, ovulatory, and luteal). Electrocutaneous sensitivity was greater during follicular compared to menstrual phase. Additionally, pain unpleasantness was lower in follicular phase than other phases while pain intensity ratings did not change over the cycle. Significant variations in cycle phase effects on gray matter volume were found in the left inferior parietal lobule (IPL) using voxel-based morphometry. Subsequent Freesurfer analysis revealed greater thickness of left IPL during the menstrual phase when compared to other phases. Also, white matter volume fluctuated across phases in left IPL. Blood estradiol was positively correlated with white matter volume both in left parietal cortex and whole cortex. Seed-driven FC between left IPL and right secondary visual cortex was enhanced during ovulatory phase. A seed placed in right IPL revealed enhanced FC between left and right IPL during the ovulatory phase. Additionally, we found that somatosensory cortical gray matter was thinner during follicular compared to menstrual phase. We discuss these results in the context of likely evolutionary pressures selecting for enhanced perceptual sensitivity across modalities specifically during ovulation.
| S-EPMC7783210 | biostudies-literature
Project description:BackgroundThere is growing interest in neuromodulation-based therapeutics as tools for individuals with alcohol use disorder (AUD). Through electromagnetic induction, techniques such as transcranial magnetic stimulation (TMS) can noninvasively depolarize cortical cells in the induced electrical field and monosynaptic afferents. The ability of TMS to modulate the brain is dependent upon two factors, which may be compromised in individuals with AUD: (1) gray matter volume (GMV) at the site of stimulation and (2) scalp-to-cortex (STC) distance. This study tested the hypotheses that these aspects of neural architecture are compromised in AUD patients, and thus AUD patients may need a higher TMS dose to depolarize the cortex.MethodsHigh-resolution magnetic resonance images were acquired from 44 individuals with AUD and 44 age-matched healthy controls (n = 88). Whole-brain voxel-based morphometry was conducted. Subsequent region-of-interest analysis was performed at three EEG 10-20 sites commonly used in TMS for AUD: FP1 (left frontal pole), F3 (left DLPFC), and C3 (left motor cortex). STC distance and TMS electric fields were assessed at these EEG sites.ResultsIndividuals with AUD had significantly lower GMV in the bilateral orbitofrontal cortices, supramarginal gyri, and the left DLPFC (voxel-threshold p < 0.05, cluster-threshold p < 0.05) and within all 3 TMS target locations, F (1, 264) = 14.12, p = 0.0002. There was no significant difference in STC distance between the AUD and the healthy control group at any tested cortical location, F (3, 252) = 1.906, p = 0.129.ConclusionsIndividuals with AUD had significantly lower GMV in multiple areas of interest for TMS treatment; however, these volumetric reductions did not impact STC distance. Given previous studies that have shown TMS-evoked changes in cortical and subcortical activity to be dependent on GMV, these data suggest that individuals with AUD may require higher doses of TMS to sufficiently modulate the neural circuits of interest.
| S-EPMC8560006 | biostudies-literature
Project description:OBJECTIVE:To examine metacognitive ability (MC) following moderate to severe traumatic brain injury (TBI) using an empirical assessment approach and to determine the relationship between alterations in gray matter volume (GMV) and MC. METHOD:A sample of 62 individuals (TBI n = 34; healthy control [HC] n = 28) were included in the study. Neuroimaging and neuropsychological data were collected for all participants during the same visit. MC was quantified using an approach borrowed from signal detection theory (Type II area under the receiver operating characteristic curve calculation) to evaluate judgments during a modified version of the 3rd edition of the Wechsler Adult Intelligence Scale's Matrix Reasoning subtest where half of the items were presented randomly and half were presented in the order of increasing difficulty. Retrospective confidence judgments were collected on an item-by-item basis. Brain volumetric analyses were conducted using FreeSurfer software. RESULTS:Analyses of the modified Matrix Reasoning task data demonstrated that HCs significantly outperformed TBIs (ordered: d = .63; random: d = .58). There was a significant difference between groups for MC for the randomly presented stimuli (d = .54) but not the ordered stimuli. There was an association between GMV and MC in the TBI group between the right orbital region and MC (R2 = .11). In the HC group, there were associations between the left posterior (R2 = .17), left orbital (R2 = .29), and left dorsolateral (R2 = .21) regions and MC. CONCLUSIONS:These results are consistent with those of previous research on MC in the cognitive neurosciences, but this study demonstrates that injury may moderate the regional contributions to MC. (PsycINFO Database Record
| S-EPMC6058682 | biostudies-literature
Project description:IntroductionIndividuals with persistent developmental stuttering display deficits in aligning motor actions to external cues (i.e., sensorimotor synchronization). Diffusion imaging studies point to stuttering-associated differences in dorsal, not ventral, white matter pathways, and in the cerebellar peduncles. Here, we studied microstructural white matter differences between adults who stutter (AWS) and fluent speakers using two complementary approaches to: (a) assess previously reported group differences in white matter diffusivity, and (b) evaluate the relationship between white matter diffusivity and sensorimotor synchronization in each group.MethodsParticipants completed a sensorimotor synchronization task and a diffusion MRI scan. We identified the cerebellar peduncles and major dorsal- and ventral-stream language pathways in each individual and assessed correlations between sensorimotor synchronization and diffusion measures along the tracts.ResultsThe results demonstrated group differences in dorsal, not ventral, language tracts, in alignment with prior reports. Specifically, AWS had significantly lower fractional anisotropy (FA) in the left arcuate fasciculus, and significantly higher mean diffusivity (MD) in the bilateral frontal aslant tract compared to fluent speakers, while no significant group difference was detected in the inferior fronto-occipital fasciculus. We also found significant group differences in both FA and MD of the left middle cerebellar peduncle. Comparing patterns of association with sensorimotor synchronization revealed a novel double dissociation: MD within the left inferior cerebellar peduncle was significantly correlated with mean asynchrony in AWS but not in fluent speakers, while FA within the left arcuate fasciculus was significantly correlated with mean asynchrony in fluent speakers, but not in AWS.ConclusionsOur results support the view that stuttering involves altered connectivity in dorsal tracts and that AWS may rely more heavily on cerebellar tracts to process timing information. Evaluating microstructural associations with sensitive behavioral measures provides a powerful tool for discovering additional functional differences in the underlying connectivity in AWS.
| S-EPMC8821245 | 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:Visual deprivation can induce alterations of regional spontaneous brain activity (RSBA). However, the effects of onset age of blindness on the RSBA and the association between the alterations of RSBA and brain structure are still unclear in the blind. In this study, we performed resting-state functional and structural magnetic resonance imaging on 50 sighted controls and 91 blind subjects (20 congenitally blind, 27 early blind, and 44 late blind individuals). Compared with the sighted control, we identified increased RSBA in the blind in primary and high-level visual areas and decreased RSBA in brain regions which are ascribed to sensorimotor and salience networks. In contrast, blind subjects exhibited significantly decreased gray matter volume (GMV) in the visual areas, while they exhibited significantly increased GMV in the sensorimotor areas. Moreover, the onset age of blindness was negatively correlated with the GMV of visual areas in blind subjects, whereas it exerted complex influences on the RSBA. Finally, significant negative correlations were shown between RSBA and GMV values. Our results demonstrated system-dependent, inverse alterations in RSBA and GMV after visual deprivation. Furthermore, the onset age of blindness has different effects on the reorganizations in RSBA and GMV.
| S-EPMC4629052 | biostudies-literature
Project description:The study aimed to investigate alterations in gray matter volume in individuals undergoing regular soccer training, using high-resolution structural data, while also examining the temporal precedence of such structural alterations. Both voxel-based morphometry and source-based morphometry (SBM) methods were employed to analyze volumetric changes in gray matter between the soccer and control groups. Additionally, a causal network of structural covariance (CaSCN) was built using granger causality analysis on brain structural data ordering by training duration. Significant increases in gray matter volume were observed in the cerebellum in the soccer group. Additionally, the results of the SBM analysis revealed significant increases in gray matter volume in the calcarine and thalamus of the soccer group. The analysis of CaSCN demonstrated that the thalamus had a prominent influence on other brain regions in the soccer group, while the calcarine served as a transitional node, and the cerebellum acted as a prominent node that could be easily influenced by other brain regions. In conclusion, our study identified widely affected regions with increased gray matter volume in individuals with regular soccer training. Furthermore, a temporal precedence relationship among these regions was observed.
| S-EPMC10963784 | biostudies-literature
Project description:Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; pcorr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.
| S-EPMC9829754 | biostudies-literature
Project description:BackgroundAlcohol use has been reliably associated with smaller subcortical and cortical regional gray matter volumes (GMVs). Whether these associations reflect shared predisposing risk factors or causal consequences of alcohol use remains poorly understood.MethodsData came from 3 neuroimaging samples (N = 2423), spanning childhood or adolescence to middle age, with prospective or family-based data. First, we identified replicable GMV correlates of alcohol use. Next, we used family-based and longitudinal data to test whether these associations may plausibly reflect a predispositional liability for alcohol use or a causal consequence of alcohol use. Finally, we used heritability, gene-set enrichment, and transcriptome-wide association study approaches to evaluate whether genome-wide association study-defined genomic risk for alcohol consumption is enriched for genes that are preferentially expressed in regions that were identified in our neuroimaging analyses.ResultsSmaller right dorsolateral prefrontal cortex (DLPFC) (i.e., middle and superior frontal gyri) and insula GMVs were associated with increased alcohol use across samples. Family-based and prospective longitudinal data suggest that these associations are genetically conferred and that DLPFC GMV prospectively predicts future use and initiation. Genomic risk for alcohol use was enriched in gene sets that were preferentially expressed in the DLPFC and was associated with replicable differential gene expression in the DLPFC.ConclusionsThese data suggest that smaller DLPFC and insula GMV plausibly represent genetically conferred predispositional risk factors for, as opposed to consequences of, alcohol use. DLPFC and insula GMV represent promising biomarkers for alcohol-consumption liability and related psychiatric and behavioral phenotypes.
| S-EPMC7412715 | biostudies-literature