Human Brain Mapping最新文献

Given the historical underrepresentation of autistic females in neuroscience research, few neuroimaging studies have directly compared females and males with autism spectrum disorders (ASD) to explore both sex-independent and -specific neural features. This study employed a sliding-window approach to construct dynamic functional connectivity and investigated sex similarities and differences in modular variability (nodal level), edge variability (edge level), and state variability (brain state level) in brain connectomes among individuals with and without ASD. Ninety-eight autistic individuals (49 female, 49 male; full-scale IQ ≥ 70) and 98 typically developing individuals (TD; 49 female, 49 male), matched on sex, age, and full-scale IQ, were selected from the Autism Brain Imaging Data Exchange (ABIDE). Results showed that both autistic males and females exhibited reduced modular variability in the left middle frontal gyrus and diminished edge variability in the functional connectivity between the right olfactory cortex and the right paracentral lobule, compared to their TD peers. Notably, autistic individuals manifested a sex-opposite shift in the edge variability of functional connectivity between the left amygdala and the right anterior cingulate and paracingulate gyri. Furthermore, greater autistic symptom severity was associated with reduced maintenance of a high-connectivity brain state characterized by functional competition between the frontal cortex and sensory-perceptual or subcortical regions. These findings reveal both shared and sex-differentiated alterations in connectome dynamics in ASD, with the sex-specific patterns aligning with the gender incoherence model. Understanding these dynamic features may inform more individualized and sex-sensitive educational and social support for individuals with ASD.

Acquired apraxia of speech (AOS) is a disorder of speech motor planning/programming that is induced by a lesion to the left anterior ventral precentral sulcus. This study analyses neuroimaging data from AOS in order to propose and computationally test a mechanistic explanation of how the lesion leads to the characteristic of altered lexical stress in the disorder. Neuroimaging data from 31 participants with left hemisphere stroke (15 AOS) were reanalysed to guide a ‘lesioned’ version of the bilateral GODIVA neuro-computational model of speech production. Structural MRI and resting-state functional MRI measurements were used to decide the model's lesion extent and atypical neural processing, respectively. The ‘lesioned’ model was compared with a neurotypical model on the production of an exemplar utterance with different linguistic contexts. Analyses revealed the average lesion in the AOS participants extended over 22.25% of the left anterior ventral precentral sulcus. Functional connectivity in AOS was reduced between the lateral part of that region and the right motor cortex, as well as between the left and right motor cortices themselves. The version of the model that we altered in line with these findings produced lengthening of the second of two consecutive short syllables. The lengthened syllable was a word-initial unstressed syllable, and consequently, its contrastiveness with the adjacent stressed syllable of the word was reduced. The agreement between simulation results and previously reported acoustic measurements from actual AOS patients lends support to our mechanistic explanation. In conclusion, simulations of the GODIVA model provided empirical support for a mechanistic explanation indicating permanent sub-threshold cortical activity in AOS. As a result, the speech system becomes biased away from a motor control strategy based on motor programs and toward a strategy based on sensory feedback. This both lengthens brief syllables and interferes with the mechanism to shift between syllables, ultimately altering lexical stress. Analysis of the model's neural dynamics suggests the explanation can be generalised to various contexts where lexical stress is altered in AOS.

Deriving white matter (WM) bundles in vivo has thus far mainly been applied in research settings, leveraging high angular resolution, multi-shell diffusion MRI (dMRI) acquisitions that enable modern reconstruction methods. However, these advanced acquisitions are both time-consuming and costly to acquire. The ability to reconstruct WM bundles in the massive amounts of existing single-shelled, lower angular resolution data from legacy research studies and healthcare systems would offer much broader clinical applications and population-level generalizability. While legacy scans may offer a valuable, large-scale complement to contemporary research datasets, the reliability of white matter bundles derived from these scans remains unclear. Here, we leverage a large research dataset where each 64-direction dMRI scan was acquired as two independent 32-direction runs per subject. To investigate how a state-of-the-art bundle-specific reconstruction method generalizes to this data, we evaluated the test–retest reliability of WM bundles reconstructed from the two 32-direction scans across three orientation distribution function (ODF) reconstruction methods: generalized q-sampling imaging (GQI), constrained spherical deconvolution (CSD), and single-shell three-tissue CSD (SS3T). We found that the majority of WM bundles could be reliably extracted from dMRI scans that were acquired using the 32-direction, single-shell acquisition scheme. The mean Dice coefficient of reconstructed WM bundles was consistently higher within subject than between subject for all WM bundles and ODF reconstruction methods, illustrating preservation of person-specific anatomy. Further, when using features of the bundles to predict complex reasoning assessed using a computerized cognitive battery, we observed stable prediction accuracies (r: 0.15–0.36) across the test–retest data. Among the three ODF reconstruction methods, SS3T had a good balance between sensitivity and specificity when comparing the reconstructed bundles to atlas bundles, a high intra-class correlation of extracted features, more plausible bundles, and strong predictive performance. More broadly, these results demonstrate that bundle-specific reconstruction can achieve robust performance even on lower angular resolution, single-shell dMRI, with particular advantages for ODF methods optimized for single-shell data. This highlights the considerable potential for dMRI collected in healthcare settings and legacy research datasets to accelerate and expand the scope of WM research.

Structural deficits in white matter fibre have been linked to psychosis. However, it remains unclear whether these aberrations are present in individuals that experience non-clinical psychotic-like experiences, predating illness onset. While previous research demonstrates that alterations in white matter in schizotypy are consistent with those in clinical psychosis, these studies often dichotomise healthy samples into high and low schizotypy, which may reduce statistical sensitivity. Previous research is also confounded by the investigation of diffusion MRI parameters that fail to account for complex crossing fibre populations. In this work, we treat psychotic-like experiences as a continuous variable, and applied Fixel-Based Analysis (FBA), a framework for investigating microstructural and morphological effects in brain white matter using diffusion-weighted imaging data. Across two independent cohorts of healthy participants with varied psychotic-like experiences including data from the IMAGEN consortium (Study 1 n = 41; Study 2 n = 1098), we hypothesized that greater psychotic-like experiences would be associated with FBA metrics sensitive to microstructural fibre density and/or cross-sectional morphological effects. Contrary to our hypothesis, we did not find significant correlations between psychotic-like experiences and FBA metrics across either dataset (FWE p < 0.05). Bayesian analysis of tract-aggregated data showed substantial evidence of no association (Bayes factor < 1/3) between psychotic-like experiences and fibre density, nor cross-sectional morphology, across several white matter tracts of interest, pre-defined from prior neuroimaging literature. These findings suggest that the relationship between non-clinical psychotic-like experiences and white matter microstructure may not be as robust as previously thought. This raises the possibility that white matter alterations across the psychosis spectrum echo clinical diagnostic thresholding, with observable effects in clinical but not sub-clinical presentations. Our findings show no association between whole-brain fibre-specific properties of white matter microstructure and sub-clinical psychotic-like experiences. Further, we show evidence for the lack of an association within tract-aggregated fibre-specific metrics. Future research should integrate longitudinal designs to explore whether fibre-specific white matter attributes provide clinically meaningful insight into the risk of psychosis onset.

Pupillary responses are windows into human cognition, but their neural substrates are poorly understood. We studied brain–pupil coupling through intracranial recordings and pupillometry in 13 children and youth with epilepsy (ages 9–18) during an attentional set-shifting task. Time-resolved mixed-effects modelling identified associations between pupil diameter, neural activity and cognitive performance. We first showed that pupillary dynamics are closely linked to cognitive performance, with task-stage dependencies. Larger pupil sizes prior to stimulus onset were associated with faster reaction times, whereas smaller pupil sizes during and after stimulus presentation were linked to better performance. Next, linear models identified associations between band-limited power in task-relevant neural networks and pupil size changes during the task. Finally, deep learning models based on intracranial neural activity captured patterns predictive of changes in pupil size in five of seven participants that generalised to recordings from a separate day. Using salience-based gradient mapping, we identified a network of task-relevant cortical and subcortical regions whose engagement was consistently associated with higher model performance in predicting pupil dynamics during attentional set-shifting. Our findings suggest pupillary responses are coordinated with goal-oriented cognitive processing, providing a basis for modelling cognitive functions through pupillary dynamics.

Previous studies have investigated the common and specific neural correlates underlying visuo-orthographic, phonological, and semantic processing in word reading. However, it remains unclear how those networks represent different types of lexical information and how such representations and the interactions between networks are modulated by task-induced processing demands. To address this issue, 32 native Chinese participants were scanned with fMRI while performing a localizer task, and two reading tasks designed to elicit high demands on visuo-orthographic processing (i.e., structural judgment task) and semantic processing (i.e., familiarity judgment task). Activation analyses identified both common and specific neural networks involved in visual, phonological, and semantic processing. Representational similarity analysis (RSA) further revealed that the common network represented multiple types of lexical information, whereas the specific networks selectively represented particular lexical information corresponding to their respective processing type. Moreover, processing demands modulated lexical representations of common and specific networks in distinct ways: the common network exhibited flexible representational patterns, representing task-relevant lexical information under high processing demands, whereas the specific networks showed process-dependent selectivity, representing corresponding lexical information only under high processing demands. Functional connectivity analyses further indicated that processing demands could modulate connectivity patterns among networks, particularly between the common and specific networks. These findings highlight the distinct functional roles of common and specific networks, providing a new perspective on the complementary contributions of functionally overlapping and specialized systems in word reading.

The dopamine transporter (DAT) mediates the reuptake of extracellular dopamine into presynaptic neurons. We investigated the effects of glucose loading on the striatal DAT in healthy young adults who underwent ¹⁸F-FP-CIT PET scans and completed a sweet taste questionnaire (STQ). Thirty-five healthy participants were enrolled in this study. Each participant visited the institution three times for three brain PET scans (two ¹⁸F-FP-CIT PET scans after the infusion of glucose or placebo and one ¹⁸F-Fluorodeoxyglucose PET scan). All participants underwent the 12-item self-reporting STQ to evaluate their reactions to eating sweet foods, cravings for sweet foods, and degree of control over eating sweet foods (STQ 1: sensitivity to the mood-altering effect of sweet foods, and STQ 2: impaired control over eating sweet foods). In the caudate, glucose-loaded DAT availability was significantly higher than placebo-loaded DAT availability, and in the putamen, there was a trend toward higher DAT availability following glucose loading. The STQ was consistently positively related to glucose-loaded DAT availability, not with placebo-loaded DAT availability. In conclusion, changes in striatal DAT availability after glucose loading suggest an association with attitudes toward sweet foods in healthy young males. This may indicate that individuals with higher DAT availability after glucose loading experience rapid clearance of synaptic dopamine after consuming sweet foods, potentially leading to a desire for additional sweet foods.

Listening to music is a ubiquitous human activity, but little is known about its functional cerebral correlates. We investigated the dynamics of fMRI-based brain activation patterns associated with two musical compositions and examined whether these patterns are modulated by the degree of musical expertise. Specifically, 24 aspiring professionals and 17 amateur musicians listened to a baroque composition by J. S. Bach and an early modern piece by A. Webern. Using measures of dynamic and static functional connectivity and graph theory, we identified two distinct brain states: one characterized by higher modularity (greater segregation), and the other by higher global efficiency (greater integration). Participants spent more time in the segregated state while listening to Bach, and more frequently shifted to the integrated state during Webern's piece. An anticorrelation was observed between segregation and music complexity as measured by permutation entropy, indicating that music with higher complexity elicited more integrated brain states. Individuals with greater musical expertise demonstrated higher global efficiency during the Webern piece and engaged more frontal, temporal, and parietal regions as functional hubs. These findings suggest that musical form and expertise jointly shape the brain's functional organization during naturalistic music listening.

Altered brain connectivity in the default mode network (DMN) has frequently been reported in Autism Spectrum Disorder (ASD) patients compared to typically developing control (TC) participants. Most of these studies have focused on a specific age group or mixed-age groups with ASD. This study investigates age-related changes in effective connectivity (EC) within the DMN in individuals with ASD compared to TC. Using resting-state functional magnetic resonance imaging (MRI) data from the ABIDE-I and ABIDE-II databases, we analyzed 591 ASD and 725 TC participants across three age cohorts: children (≤ 12 years), adolescents (12–18 years), and adults (≥ 18 years). Spectral Dynamic Causal Modeling was employed to estimate EC within the DMN, focusing on eight regions of interest: posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), left/right inferior parietal cortex (lIPC/rIPC), left/right middle temporal cortex (lMTC/rMTC), and left/right hippocampus (lHIP/rHIP). Parametric Empirical Bayes (PEB) analysis was used to assess group differences and age-related changes in EC, while controlling for covariates such as gender, handedness, eye status, and head motion. Key findings revealed significant group differences in EC between ASD and TC across all age groups. In children, ASD exhibited both hyper- and hypo-connectivity in various DMN connections, with most connections showing increased EC in ASD. Adolescents and adults with ASD displayed a mixed pattern of group differences in EC, though the majority of connections showed hypo-connectivity in ASD. Age-by-group interactions observed in children and adolescents not adults, highlighted nonlinear developmental trajectories, with significant differences in EC patterns between ASD and TC. Additionally, in children and adults several extrinsic and intrinsic connections were associated significantly with diagnostic observation schedule (ADOS) symptom severity, such as overall ASD symptoms, communication and stereotyped behaviors, which these connections may serve as a neural marker of symptom severity in ASD. These findings underscore the dynamic nature of EC abnormalities in ASD across the lifespan, suggesting that early hyper-connectivity may transition to hypo-connectivity in later developmental stages. The study highlights the potential of EC as a biomarker for ASD and emphasizes the importance of age-specific approaches in understanding the neural underpinnings of the disorder. Future research with larger datasets is needed to validate these findings and further explore the clinical relevance of EC in ASD diagnostics and interventions.