Sex differences in decreased limbic and cortical grey matter volume in cocaine dependence: a voxel-based morphometric study

AbstractBackgroundIn patients with schizophrenia, distributed abnormalities are observed in grey matter volume. A recent hypothesis posits that these distributed changes are indicative of a plastic reorganisation process occurring in response to a functional defect in neuronal information transmission. We investigated the structural covariance across various brain regions in early-stage schizophrenia to determine if indeed the observed patterns of volumetric loss conform to a coordinated pattern of structural reorganisation.MethodsStructural magnetic resonance imaging scans were obtained from 40 healthy adults and 41 age, gender and parental socioeconomic status matched patients with schizophrenia. Volumes of grey matter tissue were estimated at the regional level across 90 atlas-based parcellations. Group-level structural covariance was studied using a graph theoretical framework.ResultsPatients had distributed reduction in grey matter volume, with high degree of localised covariance (clustering) compared with controls. Patients with schizophrenia had reduced centrality of anterior cingulate and insula but increased centrality of the fusiform cortex, compared with controls. Simulating targeted removal of highly central nodes resulted in significant loss of the overall covariance patterns in patients compared with controls.ConclusionRegional volumetric deficits in schizophrenia are not a result of random, mutually independent processes. Our observations support the occurrence of a spatially interconnected reorganisation with the systematic de-escalation of conventional ‘hub’ regions. This raises the question of whether the morphological architecture in schizophrenia is primed for compensatory functions, albeit with a high risk of inefficiency.

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Sex classification using long-range temporal dependence of resting-state functional MRI time series

10.1101/809954 ◽

2019 ◽

Cited By ~ 1

Author(s):

Elvisha Dhamala ◽

Keith W. Jamison ◽

Mert R. Sabuncu ◽

Amy Kuceyeski

Keyword(s):

Sex Differences ◽

Long Range ◽

Resting State ◽

Grey Matter ◽

Machine Learning Algorithms ◽

Grey Matter Volume ◽

Temporal Dependence ◽

Matter Volume ◽

Human Connectome Project ◽

Males And Females

AbstractA thorough understanding of sex differences, if any, that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit differences in clinical and behavioural phenotypes between males and females. In this work, we evaluate sex differences in regional temporal dependence of resting-state brain activity using 195 male-female pairs (aged 22-37) from the Human Connectome Project. Male-female pairs are strictly matched for total grey matter volume. We find that males have more persistent long-range temporal dependence than females in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies of up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, frontal cortex, and occipital cortex. Additionally, we find that even after males and females are strictly matched on total grey matter volume, significant regional volumetric sex differences persist in many cortical and subcortical regions. Our results indicate males have larger cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger cingulates, precunei, frontal cortices, and parietal cortices. Sex classification based on regional volume achieves accuracies of up to 85%; cerebellum, cingulate cortex, and temporal cortex are the most important features. These findings highlight the important role of strict volume matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between males and females identifies a potential neurobiological substrate underlying sex differences in functional brain activation patterns and the behaviours with which they correlate.

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Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction

Scientific Reports ◽

10.1038/s41598-020-69361-9 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Carla Sanchis-Segura ◽

Maria Victoria Ibañez-Gual ◽

Naiara Aguirre ◽

Álvaro Javier Cruz-Gómez ◽

Cristina Forn

Keyword(s):

Sex Differences ◽

Prediction Accuracy ◽

Grey Matter ◽

Grey Matter Volume ◽

Intracranial Volume ◽

Machine Learning Classifiers ◽

Matter Volume ◽

Morphological Differences ◽

Gray Matter Volumes ◽

Single Predictor

Abstract Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power-corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV-variation and result in sex-differences that are “small” (∣d∣ < 0.3). Moreover, as assessed using a totally independent sample, sex differences in PCP and residuals adjusted-data showed higher replicability ($$\approx $$ ≈ 93%) than scaling and proportions adjusted-data $$( \approx $$ ( ≈ 68%) or raw data ($$\approx $$ ≈ 45%). The replicated effects were meta-analyzed together and confirmed that, when TIV-variation is adequately controlled, volumetric sex differences become “small” (∣d∣ < 0.3 in all cases). Finally, we assessed the utility of TIV-corrected/ TIV-uncorrected GMVOL features in predicting individuals’ sex with 12 different machine learning classifiers. Sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted-data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals’ methods, prediction accuracy dropped to $$\approx $$ ≈ 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL

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Sex Differences in Brain Structure Account for the Sexual Distinction on Balanced Time Perspective

10.21203/rs.3.rs-1174014/v1 ◽

2021 ◽

Author(s):

Tao Chen ◽

Zhi Li ◽

Ji-fang Cui ◽

Jia Huang ◽

Muireann Irish ◽

...

Keyword(s):

Sex Differences ◽

Cortical Thickness ◽

Grey Matter ◽

Time Perspective ◽

Supplementary Motor Area ◽

Motor Area ◽

Human Cognition ◽

Grey Matter Volume ◽

Matter Volume ◽

Balanced Time Perspective

Abstract Sex differences in behaviour and cognition have been widely observed, however, little is known about such differences in maintaining a balanced time perspective or their potential underlying neural substrates. To answer the above questions, two studies were conducted. In Study 1, time perspective was assessed in 1,913 college students, including 771 males and 1,092 females, and demonstrated that females had a significantly more balanced time perspective than males. In Study 2, 58 males and 47 females underwent assessment of time perspective and structural brain imaging. Voxel-based morphometry analysis and cortical thickness analysis were used to analyse the structural imaging data. Results showed that compared with males, females demonstrated a more balanced time perspective, which primarily related to lower grey matter volume in left precuneus, right cerebellum, right putamen and left supplementary motor area. Analysis of cortical thickness failed to reveal any significant sex differences. Furthermore, the sex difference in grey matter volume of left precuneus, right cerebellum, right putamen and left supplementary motor area could account for the difference in balanced time perspective between males and females. The findings deepen our understanding of sex differences in human cognition and their potential neural signature, and may inform tailored interventions to support a balanced time perspective in daily life.

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