Human Brain Mapping最新文献

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study seeks to address this gap by utilizing a matrix-based entropy functional for estimating total correlation, which serves as a mathematical framework for capturing multivariate information. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of multivariate interaction patterns within the human brain across multiple scales. Additionally, this approach holds significant promise for psychiatric research on schizophrenia, offering a novel framework for investigating higher-order triadic brain network interactions associated with the disorder. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding changes in higher-order brain networks in schizophrenia. This framework not only advances our understanding of complex brain functions but also opens new avenues for investigating the pathophysiology of schizophrenia, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, this method for analyzing multiway interactions is applicable across signal analysis domains. In this study, we apply this approach to neural signals in schizophrenia, demonstrating its ability to reveal complex multiway interaction patterns and provide new insights into brain connectivity beyond traditional pairwise analyses in the context of brain disorders.

Sickle cell disease (SCD) is an inherited blood disorder caused by a mutation in the beta-globin gene, resulting in chronic complications, including cognitive decline—particularly in executive functions. Neuroimaging studies have identified structural and functional abnormalities associated with SCD; however, the directionality of information flow between brain networks and how disruptions in these interactions contribute to cognitive deficits remains poorly understood. This study employed Granger causality (GC) analysis to investigate effective connectivity and information flow between brain regions and resting-state networks using ultra-high-field 7T MRI in adult patients with SCD (n = 51) and age-, sex-, and race-matched controls (n = 44). We first performed a whole-brain network analysis, followed by an examination of specific brain regions within the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), and ventral attention network (VAN). For each analysis, we computed both the magnitude and directionality of information flow to capture the strength and directional influence of connectivity between brain regions. While patients with SCD exhibited a higher magnitude of information flow compared to controls, this difference was only statistically significant when computed at the brain region level, not at the resting-state network level. In terms of directionality, afferent flow from DAN and VAN to ECN was significantly greater in patients with SCD than in controls. Subtype analysis revealed that patients with severe SCD demonstrated significantly higher magnitude of information flow than those with mild SCD and controls. We also observed subtype-specific differences in afferent flow to ECN: mild SCD patients showed significant flow from VAN, while severe SCD patients showed significant flow from DAN. Additionally, multiple regression analyzes assessing correlations between information flow and cognitive performance showed that controls had higher R² values than patients with SCD, suggesting reduced network efficiency in SCD. This study is the first to apply GC-based effective connectivity analysis in SCD, revealing unique pathways of information exchange in patients with SCD, potentially as compensatory mechanisms for disease-related structural and functional disruptions. These findings provide novel insights into how SCD impacts brain network organization and cognitive function, emphasizing the importance of investigating network-level dynamics in this population.

Accurate estimation of the total intracranial volume (TIV) is essential in brain magnetic resonance imaging (MRI) studies, particularly for multi-site longitudinal investigations. This study assessed the validity and reliability of segmentation-based TIV (sbTIV) implemented in FreeSurfer version 7.2 for large-scale multi-site MRI data, by comparing it with the widely used estimated TIV (eTIV). We analyzed 6524 structural MRI scans from two multi-site projects in Japan, consisting of 30 procedures across 21 sites, 13 MRI machine types, 3 vendors, and 4 protocol categories. We tested the intraclass correlation coefficients (ICCs) between eTIV and sbTIV for each procedure and identified procedural factors affecting these ICCs using a general linear model. Machine- and protocol-specific biases were considered by a traveling subject harmonization approach. To specifically examine the reliability and validity of the longitudinal scans, we employed a general linear mixed model (GLMM). Overall agreement between eTIV and sbTIV was good (ICC = 0.78) but varied across procedures (0.62–0.94). The 1.0 mm isotropic protocol showed the highest reliability. Notably, there was poor consistency in participants with eTIV values of 120,000 mm³ or smaller (ICC = 0.053). sbTIV demonstrated superior cross-procedural consistency in adolescent and adult longitudinal scans compared to eTIV. In longitudinal scans, sbTIV showed greater sex difference and sex-specific increase for adolescents, and greater consistency for adults, compared to eTIV. sbTIV offers more robust and reliable estimation compared to eTIV, particularly for multi-site longitudinal studies. These findings highlight the need for careful consideration when interpreting previous multi-site studies using eTIV.

Structural changes in the cerebellum contribute to cognitive decline due to aging and Alzheimer's disease (AD). However, it is unclear whether age and AD pathology are associated with structural alterations in the cerebellum among cognitively unimpaired individuals and how these alterations relate to cognition. This study examined the association of age and cerebrospinal fluid (CSF) AD biomarkers (amyloid beta [Aβ₄₂/Aβ₄₀], phosphorylated tau [p-tau₁₈₁]) with cerebellar gray matter (GM) and white matter (WM) volumes and cerebellar WM microstructure, measured via magnetic resonance imaging (MRI) among 176 cognitively unimpaired middle-aged and older adults (mean age = 66.70, range = 34–89). Cognition was measured with executive function and visuospatial composite scores. Older age was associated with lower cerebellar GM and WM volumes (ps < 0.01) and greater mean diffusivity (MD) in the cerebellar peduncles (p < 0.01). In contrast, more abnormal Aβ levels were associated with lower MD in three regions of interest, including the middle cerebellar peduncle (MCP, p < 0.01), a composite of superior, middle, and inferior peduncles (p < 0.05), and within-cerebellar WM (p < 0.05). Patterns were similar when comparing biomarker positive versus negative groups, particularly for the MCP. Further, lower MD in the peduncles and cerebellar WM was associated with better executive function and visuospatial composite scores (ps < 0.05), whereas cerebellar volumetric measures were not related to cognition. Results suggest that older age is associated with microstructural and volumetric cerebellar GM and WM alterations. In contrast, Aβ levels are associated with WM microstructural properties in cognitively unimpaired individuals. These findings highlight the importance of cerebellar WM microstructure to cognition and are consistent with, and expand on, previous reports that have linked more abnormal amyloid levels to WM microstructure in cerebral tracts. They also suggest that cerebellar WM alterations may be markers of preclinical AD.

Mild traumatic brain injury (mTBI) can result in persistent cognitive deficits (particularly in attention, processing speed, and working memory), even years after the injury. The majority of behavioural studies have focussed on averaged cognitive performance scores, such as average reaction time or accuracy scores after mTBI. However, less is understood about how mTBI affects intraindividual variability (IIV) in cognitive performance across repeated sessions or measurement occasions over time. In this study, we investigate IIV in cognitive performance in chronic mTBI patients (n = 11) relative to healthy controls (n = 22). Participants underwent a single behavioural testing session (incorporating the Rivermead Post-Concussion Symptom Questionnaire and a computerised processing speed task) and a multi-shell diffusion MRI scan. This was followed by a 30-day ecological momentary assessment (EMA) protocol using a smartphone app which measured symptoms and cognitive performance on a daily basis. Our results revealed that mTBI patients exhibited higher IIV than controls in both single-session trial-by-trial and daily EMA measures. Higher daily IIV in cognitive performance coincided with higher daily fluctuations in post-concussive symptoms. Additionally, mTBI patients showed reduced white matter organization, as indexed by fixel-wise fibre density and fibre density cross-section, in the left superior longitudinal fasciculus-II compared to controls. Finally, trial-by-trial IIV was positively associated with white matter alterations in the SLF-II in mTBI. Our findings suggest that mTBI results in dynamic performance deficits that persist into the chronic phase of injury. In addition, the white matter organization of a major fronto-parietal tract seems to play an important role in supporting the consistency of cognitive performance over time, highlighting its potential as a biomarker for understanding cognitive dynamics in healthy adults and clinical populations.

Recently, shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and clinical phenotypes. However, conventional methods for computing shape measures are computationally expensive and time-consuming for large-scale datasets due to reliance on voxel-based representations. To address these limitations, we introduce Tract2Shape, a novel multimodal deep learning framework that integrates geometric streamline features (as point clouds) with scalar data descriptors (as tabular data) from tractography to predict 10 white matter tractography shape measures. We propose a Siamese architecture in which each subnetwork incorporates a dual-encoder design, enabling each encoder to learn modality-specific representations. To enhance model efficiency, we utilize a dimensionality reduction algorithm for the model to predict five primary shape components. The model is trained and evaluated on two independently acquired datasets: the Human Connectome Project minimally preprocessed young adults (HCP-YA) dataset and the Parkinson's Progression Markers Initiative (PPMI) dataset. Tract2Shape is trained and tested on the HCP-YA dataset, with performance compared against state-of-the-art models. To assess robustness and generalization, we further evaluate the model on the unseen PPMI dataset. Tract2Shape outperforms state-of-the-art deep learning models across all 10 shape measures, achieving the highest average Pearson's r and the lowest normalized mean squared error (nMSE) on the HCP-YA dataset. The ablation study shows that both multimodal input and PCA benefit performance. On the unseen testing PPMI dataset, Tract2Shape maintains a high Pearson's r and low nMSE, demonstrating strong generalizability in cross-dataset evaluation. In comparison with traditional voxel-representation-based shape computation, Tract2Shape achieves a 99.2% improvement in efficiency (< 0.1 s per subject). Tract2Shape enables fast, accurate, and generalizable prediction of white matter shape measures from tractography data, supporting scalable analysis across datasets. This framework lays a promising foundation for future large-scale white matter shape analysis.

Major depressive disorder (MDD) exhibits substantial neurobiological heterogeneity that complicates treatment selection and mechanistic understanding. While conventional group-level analyses identify diverse structural alterations, they obscure clinically relevant individual differences. We employed heterogeneity through discriminant analysis (HYDRA) clustering to decompose morphometric inverse divergence (MIND) network patterns into distinct neuroanatomical subtypes and examined their molecular underpinnings. We analyzed MIND network data from 240 Japanese individuals with MDD and 367 healthy controls using unsupervised clustering. Subtype-specific alterations were mapped onto neurotransmitter receptor density distributions, and transcriptomic data from the Allen Human Brain Atlas were integrated using partial least squares regression. Two neuroanatomically distinct subtypes emerged. Subtype 1 (n = 78) exhibited widespread increases in MIND strength across all Yeo networks, with predominant serotonergic, dopaminergic, and GABAergic associations. Gene expression analysis revealed SST and CUX2 correlations, with enrichment for metal ion homeostasis and circadian rhythm pathways. Subtype 2 (n = 162) showed reduced MIND strength in dorsal attention, somatomotor, frontoparietal, limbic, and default networks, with glutamatergic, cannabinoid, and dopaminergic dysfunction. This subtype demonstrated negative CRH correlations and enrichment for glutamatergic signaling and calcium/cAMP-mediated processes. Our findings demonstrate systematic decomposition of MDD heterogeneity into distinct neuroanatomical subtypes with unique molecular signatures. The identification of subtype-specific neurotransmitter profiles and transcriptomic architectures provides mechanistic insights into MDD heterogeneity, offering potential for biomarker-guided treatment selection and personalized therapeutic approaches.

Brain function and its failures arise from dynamical patterns of neuronal activity shaped by synaptic neurotransmission. Both neurotransmitter receptor expression and neuronal population dynamics show a remarkable regional variability across the human cortex. We leverage this functional specialisation to characterise the relationship between receptor architectonics and electrophysiological signals. Using dynamic causal modelling (DCM), we fitted neural mass models to a normative set of intracranial EEG data. Subsequently, Bayesian model comparison helped to evaluate whether models improved when equipped with constraints on synaptic connectivity, based on regional neurotransmitter receptor densities. The results show that dynamic causal models generated region-specific intracranial EEG spectra accurately. Incorporating prior information on normative receptor distributions further improved model evidence, indicating that regional variation in receptor density explains variations in synaptic connectivity and ensuing cortical population dynamics. The output is a cortical atlas of neurobiologically informed intracortical synaptic connectivity parameters. These can serve as empirical priors in future, patient-specific models. In summary, we show that molecular cortical characteristics—that is, receptor densities—enrich and inform generative, biophysically plausible models of coupled neuronal populations. This work helps to explain regional variations in human electrophysiology, provides a methodological foundation to integrate multimodal data, and serves as a normative resource for future DCM studies of electrophysiology.

The growing availability of large neuroimaging datasets, such as the UK Biobank, provides new opportunities to improve robustness and reproducibility in brain imaging research. However, little is known about the extent to which MRI processing pipelines influence results. Using 39,655 T1-weighted MRI scans from the UK Biobank, we systematically compared five widely used gray-matter representations derived from three major software packages: FSL (volume-based), CAT12/SPM (volume- and surface-based), and FreeSurfer (cortical and subcortical surface-based). We assessed their impact on morphometricity (trait variance explained by brain features), susceptibility to imaging confounders, false positives, association findings, and prediction accuracy across 29 diverse traits, including lifestyle, metabolic, and disease-related variables. We found that all pipelines were sensitive to imaging confounders such as head motion, brain position, and signal-to-noise ratio, and many produced non-normal voxel or vertex distributions. FSL and FreeSurfer generally yielded higher morphometricity estimates, but each captured partially unique signals, leading to inconsistencies in brain regions identified across methods. Volume-based approaches tended to outperform surface-based ones, detecting more significant clusters, achieving higher replication rates, and producing stronger predictive performance. Small clusters (single voxels or vertices) were less reliable, suggesting caution in their interpretation. Among all methods, FSLVBM emerged as the most consistent all-rounder, maximizing morphometricity, replicability, and predictive accuracy. Our results highlight the strengths and limitations of commonly used processing pipelines, offering benchmarks to guide researchers in method selection. They further suggest that combining multiple pipelines may improve brain-based prediction by leveraging unique, complementary signals, and that careful treatment of imaging confounders is essential for robust large-scale neuroimaging analyses.

Self-enhancement motivates individuals to prefer positive or expected social feedback over negative or unexpected feedback, thereby eliciting corresponding emotional experiences. Emotion regulation strategies that aim to reduce negative experiences and enhance positive ones often face the dilemma of prioritizing one outcome at the expense of the other. Modest individuals, characterized by the low self-focus perspective, may demonstrate advantages in managing emotional experiences derived from self-relevant social feedback. In this study, participants with high and low levels of modesty were scanned with functional magnetic resonance imaging while receiving social feedback of different valences and congruencies, with feedback indicating whether others liked participants. Results showed that highly modest individuals were less likely to use expressive suppression as an emotion regulation strategy. At the neural level, trait modesty modulated brain activity in the inferior parietal lobe and left superior temporal gyrus under unexpected conditions compared to expected conditions, as well as in the ventral anterior cingulate cortex, ventral medial prefrontal cortex, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex under acceptance versus rejection conditions. Psychophysiological interaction analysis and brain-behavior correlation analyses further explored the mechanisms of modesty, helping individuals reduce negative experiences and enhance positive experiences. Our findings reveal the cognitive processing patterns and brain activity of modest individuals when dealing with social feedback and provide insights into how individuals can better cope with social feedback.