Brain Imaging and Behavior最新文献

Obesity is associated with intrinsic functional reorganization within the brain. However, limited research has utilized resting-state functional connectome models to predict body mass index (BMI) and explore the relationship between BMI-related resting-state functional connectivity (rsFC) and behavioral performance. Least absolute shrinkage and selection operator (LASSO) regression models were developed using the HCP500 dataset (440 subjects) to identify BMI-related rsFC patterns and predict BMI values. The model demonstrating the strongest predictive power was validated on the HCP900 dataset (309 subjects). Additional validation was performed using the HCP1200 (182 subjects), NKI (102 subjects), and MPI-LEMON (151 subjects) datasets. We examined the relationship between BMI-related rsFC sets and performance on the Dimensional Change Card Sort and Delay Discounting tests. Predicted BMI values were significantly correlated with actual BMI values across the HCP1200 and NKI datasets (HCP1200: r = 0.52, p = 8E-14, MAE = 3.30; NKI: r = 0.35, p = 0.0002, MAE = 4.17). The identified BMI-related rsFC sets encompassed brain circuits involved in hemostatic control, executive function, salience processing, motor planning, reward processing, and visual perception. Notably, these rsFC fingerprintings significantly accounted for scores on the delay discounting task. Our findings demonstrate that BMI can be predicted using a functional connectome-based model. Additionally, the identified BMI-related rsFC fingerprintings effectively explained scores on delay discounting tasks, providing new insights into the neural mechanisms associated with overweight and obesity.

Pre-surgical planning often involves task-based functional magnetic resonance imaging (fMRI) in the context of intractable epilepsy or brain tumors. Resting-state fMRI can be used for the same goal, with the advantage of being a simpler technique that does not require the patient to cooperate in complex cognitive tasks. However, the methods for resting-state fMRI analysis are not yet robust or of practical usage. This work proposes an algorithm for sorting components resulting from independent component analysis (ICA) that emphasizes the language resting-state network. We recruited 20 healthy volunteers and acquired resting-state and task-based fMRI using three linguistic tasks. Task data was processed using general linear model analysis, while resting-state networks were extracted using ICA. An automated IC sorting procedure was developed based on three characteristics: spatial similarity with a probability map, low/high frequency ratio, and IC reliability over several bootstrapping folds. Task-related activation consistent with the language network was identified at the subject-specific level. The algorithm is shown to sort ICs with the resting-state language maps appearing among the first three with an accuracy of 74%. Overall, the Dice coefficient showed a good overlap between the sorted ICs of relevance and the task language maps. Results showed that resting-state networks were more specific and less sensitive than task-based maps. We expect that the proposed algorithm for optimal sorting will contribute towards making ICA usage viable in the clinical context and become a reliable alternative method for pre-surgical planning.

Objectives: To characterize the network topology and peak width of skeletonized mean diffusivity (PSMD) and their correlation with cognitive impairment in obstructive sleep apnea (OSA) patients and to assess whether such impairments are reversible after treatment.

Methods: Ninety-one OSA patients and 30 healthy controls (HCs) participated. Patients were classified into mild group (n = 37) and moderate-severe group (n = 54) based on apnea-hypopnea index. Cognitive performances, including execution, visual memory, attention, and psychomotor speed were assessed. Network topological properties and PSMD, derived from resting-state functional MRI and diffusion imaging, were compared and correlated with their cognitive performance. Alterations in network topology, PSMD, and cognitive performance after treatment were assessed in a subcohort of patients.

Results: OSA patients had worse performance in the digit symbol test and Stroop color-word test than HCs, whereas the performance of moderate-severe OSA patients decreased more significantly. Mild OSA patients had compromised degree centrality of cognitive control, while moderate-severe OSA patients had compromised topological properties involving cognitive control, default mode, limbic, and auditory network relative to HCs, and had higher PSMD than mild OSA patients and HCs. Aberrant PSMD and functional nodal network metrics closely correlated with cognitive decline in OSA patients. Notably, functional network topology and cognitive performance partially improved in patients after treatment.

Conclusions: Progressive compromise of the PSMD and functional network topology may underlie the cognitive deficits in attention and processing speed in OSA patients. The disruption of functional network topology and cognitive performance are partially reversible in OSA patients after treatment.

Prosocial decisions in daily life are often influenced by cognitive constraints, such as time pressure and cognitive load, which can impact how we process information and make decisions that benefit others. Understanding how these constraints interact with our brain's intrinsic connectivity patterns and contribute to individual differences is crucial. This study investigates the neural mechanisms underlying the effects of cognitive constraints on prosocial decision-making. We developed a resting-state functional connectivity (rsFC) network model using machine learning regression to predict how cognitive constraints influence prosocial choices, while accounting for individual variability through intersubject representational similarity analysis (IS-RSA). Our findings reveal that the rsFC network-including regions involved in affective processing (insula, INS; amygdala, AMYG), empathy (temporo-parietal junction, TPJ; medial cingulate gyrus, MCG), and valuation (ventral striatum, VS; ventral prefrontal cortex, vmPFC)-predicts the impact of cognitive constraints on decision-making. Notably, rsFC between MCG and TPJ and bilateral TPJ connectivity showed intersubject variability that aligned with behavioral responses. These findings elucidate how cognitive constraints shape prosocial decision-making at the neural level, uncovering individual variability that advances theoretical understanding and offers practical implications for fostering prosociality in cognitively demanding contexts.

The specific role of sex differences in major depressive disorder remains unclear, this study aims to explore sex-related variations in resting-state functional connectivity of major depressive disorder patients and their association with gene expression profiles. This study included 971 patients and 897 healthy controls from the REST-meta-MDD project. We compared the functional connectivity between sexes and used the Allen Human Brain Atlas to conduct partial least squares regression analysis to identify genes associated with these functional connectivity differences in patients, followed by functional enrichment analysis. Compared to female patients, male patients exhibit increased functional connectivities between the default mode network and the frontoparietal network, while connectivities between the frontoparietal network and the visual network are reduced. Additionally, Spearman's correlation analysis identified specific patterns of functional connectivity differences that are closely associated with the Hamilton Depression Rating Scale scores in both sexes. Transcriptomic-neuroimaging analysis revealed that the expression of 1,777 genes is associated with functional connectivity differences between sexes. Enrichment analysis indicated that these genes are primarily involved in biological processes including ion channel activity, synaptic plasticity, neuronal differentiation, and synaptic development. Patients with major depressive disorder exhibited sex-related differences in functional connectivity, particularly between networks involved in self-referential thinking, emotional regulation, and cognitive control. Genes associated with these differences were primarily enriched in ion channel activity and neuronal processes, highlighting the importance of sex-specific neural mechanisms in major depressive disorder and their potential relevance for personalized treatment strategies.

Gastrointestinal symptoms are one of the most common non-motor symptoms in Parkinson's disease. This study aimed to investigate the neuroimaging mechanisms underlying gastrointestinal symptoms associated with Parkinson's disease using functional connectivity and voxel-based morphometry. The study included 50 healthy controls, 71 Parkinson's disease patients without gastrointestinal symptoms and 84 patients with gastrointestinal symptoms. Differences in gray matter volume among the three groups were assessed. Given a significant decrease in gray matter volume in the right cerebellar hemisphere, it was selected as the seed region for functional connectivity analysis.The Parkinson's disease patients with gastrointestinal symptoms showed significant differences in disease duration, levodopa equivalents daily dose, Hoehn and Yahr stage, unified Parkinson's disease rating scale part Ⅲ, Hamilton anxiety scale, Scales for Outcomes in Parkinson's disease-Autonomic, non-motor symptom scale, Montreal cognitive assessment, and orthostatic hypotension compared to the patients without gastrointestinal symptoms (p<0.05). Lower gray matter volume was observed in the group with gastrointestinal symptoms, particularly in the bilateral cerebellum hemisphere and the left superior temporal gyrus. Compared to the group without gastrointestinal symptoms, functional connectivity between the right cerebellar hemisphere and the right medial and lateral cingulate gyrus and left middle temporal lobe was significantly increased.Parkinson's disease patients with gastrointestinal symptoms present with a prolonged disease course and increased severity of both motor and non-motor symptoms. The gastrointestinal symptoms in Parkinson's disease patients may be associated with structural and functional brain alterations.

This study aims to investigate glymphatic system dysfunction in idiopathic Parkinson's disease (PD) using a dual-cohort design, focusing on its associations with freezing of gait (FOG) and cognitive decline. A cross-sectional analysis was conducted on 43 PD patients with FOG, 106 without FOG, and 46 healthy controls. A longitudinal study followed 146 early-stage PD patients from the Parkinson's Progression Markers Initiative database over five years, with 65 developing FOG. Covariate analysis was performed, controlling for variables like gender, age, and education. Survival analysis compared cognitive decline between FOG and stable groups. Random forest analysis identified key predictors of FOG development. The cross-sectional study demonstrated significantly enlarged normalized choroid plexus volume in PD patients with FOG compared to healthy controls. Both FOG and non-FOG groups showed increased perivascular space enlargement in the basal ganglia and centrum semiovale, as well as reduced average analysis along the perivascular space index compared to healthy controls. PD patients with FOG exhibited more pronounced disease progression and cognitive decline than those without FOG. Glymphatic markers were associated with age, cognitive scores, and gait performance. The longitudinal study showed slightly more severe motor symptoms and accelerated cognitive decline in the FOG group during follow-up. Random forest analysis identified age, cognitive scales, and glymphatic function metrics as robust predictors of FOG development. These findings highlight the potential significance of brain glymphatic system function in the development of freezing of gait and cognitive decline in PD patients, offering novel neuroimaging biomarkers for early detection. These authors have contributed equally to this work and share first authorship.

Emotion is present in all aspects of human life and serves as a crucial foundation for communication and interaction. Emotional processing (EP) is a complex phenomenon involving dynamic interactions among various brain regions. Despite significant progress in EP research, important challenges remain-particularly in understanding the temporal dynamics of emotion. In this study, we investigated alterations in dynamic functional connectivity (dFC) patterns during an emotional processing task, using fMRI data from 100 healthy participants in the Human Connectome Project (HCP). The brain was parcellated into 90 regions of interest (ROIs) and grouped into six networks and ten well-known brain regions using the AAL atlas. We applied dFC analysis based on sliding window correlation (SWC) and k-means clustering to identify discrete connectivity states. To define the optimum number of states, we employed non-supervised validity criteria silhouette measure. Additionally, we estimated mean dwell times and transition probability matrices between states in both face and shape conditions using a hidden Markov model (HMM). Within these states, we observed state-dependent alterations in within and between regional connectivity between the face and shape conditions. Our findings revealed three distinct dFC states and among them, dFC state with the most significant differences in probability of transitions included brain regions involved in, frontoparietal, limbic and visual networks. Across all three states, several key bilateral regions exhibited significant changes in dFC, involved in limbic (amygdala, hippocampus, parahippocampal and rectus), default mode (anterior cingulate gyrus, median cingulate gyrus, posterior cingulate gyrus and angular), frontoparietal (inferior parietal gyrus, superior parietal gyrus, and middle frontal gyrus), visual (inferior occipital gyrus, fusiform, cuneus, precuneus, lingual and calcarine), temporal-parietal (paracentral lobule, precentral, postcentral, superior temporal gyrus, temporal pole superior and insula), and subcortical (caudate, putamen, pallidum and thalamus) networks. Also, we identified three dFC states between ten brain regions -frontal-central-parietal, frontal-temporal-occipital, and global state.

For over two decades, medical imaging modalities have played crucial roles in clinical diagnosis. Extracting comprehensive information from a single modality often proves challenging for ensuring clinical accuracy. Consequently, multi-modal medical image fusion methods integrate images from diverse modalities into a single fused image, enhancing information quality and diagnostic reliability. In recent years, deep learning for multi-modal medical image segmentation has emerged as a vibrant research area, yielding promising outcomes. This paper conducts a thorough survey and comparative analysis of advancements in deep learning techniques for multi-modal medical image segmentation from 2019 to 2025. It aims to provide a comprehensive overview of deep learning-based approaches and fusion strategies for integrating information from different imaging modalities. Additionally, the survey highlights how various deep learning models enhance segmentation accuracy and reliability. Common challenges in medical image segmentation are discussed, along side current research trends in the field.