A rapidly growing number of studies on autism spectrum disorder (ASD) have used resting-state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there remains a pressing need to determine the primary factors influencing replicability. Here, we used resting-state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group-averaged functional connectomes and group-level differences (ASD vs. control) across 33 denoising pipelines and four independently-acquired datasets. The group-averaged connectomes were highly consistent across pipelines (r = 0.92 ± 0.06) and sites (r = 0.88 ± 0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76 ± 0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07 ± 0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across-site similarity remained low even when considering the data at a large-scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.
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http://dx.doi.org/10.1002/hbm.24879 | DOI Listing |
Connect Tissue Res
January 2025
Graduate School of Engineering, Kogakuin University, Hachioji, Tokyo, Japan.
Objective: This study aimed to investigate the collagen fiber structure of the subcutaneous fascia, a connective tissue layer between the skin and epimysium.
Methods: Fascia samples with varying extensibility were examined using biochemical and microscopic methods.
Results: Loose fascia, the more extensible type, displayed sparsely distributed collagen fibers, while dense fascia showed tightly packed collagen fiber bundles.
Geroscience
January 2025
Department of Surgery, Immanuel Clinic Rüdersdorf, University Clinic of Brandenburg Medical School, Berlin, Germany.
Aging is a multi-organ disease, yet the traditional approach has been to study each organ in isolation. Such organ-specific studies have provided invaluable information regarding its pathomechanisms. However, an overall picture of the whole-body network (WBN) during aging is still incomplete.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFBrain Imaging Behav
January 2025
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang, Wuhan, Hubei, 430071, China.
This study investigates post-stroke cognitive impairment (PSCI) by utilizing spectral dynamic causal modeling (spDCM) to examine changes in effective connectivity (EC) within the default mode, executive control, dorsal attention, and salience networks. Forty-one PSCI patients and 41 demographically matched healthy controls underwent 3D-T1WI and resting-state functional magnetic resonance imaging on a 3.0T MRI.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy.
Predicting disease trajectories in patients with major depressive disorder (MDD) can allow designing personalized therapeutic strategies. In this study, we aimed to show that measuring patients' plasticity - that is the susceptibility to modify the mental state - identifies at baseline who will recover, anticipating the time to transition to wellbeing. We conducted a secondary analysis in two randomized clinical trials, STAR*D and CO-MED.
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