dparsf toolbox (MathWorks Inc)
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dparsf toolbox
Dparsf Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/dparsf/Database+Toolbox/pmc10635709__JPN___230091___at___1-10-7-10
Average 96 stars, based on 2340 article reviews
Dparsf Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/dparsf/Database+Toolbox/pmc10635709__JPN___230091___at___1-10-7-10
Average 96 stars, based on 2340 article reviews
dparsf toolbox - by Bioz Stars,
2026-09
96/100 stars
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other:Article Title: Structural and functional whole brain changes in autism spectrum disorder at different age stages. Article Snippet: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by persistent deficits in social interaction and communication, along with restricted, repetitive patterns of behavior and interests [1].. The global prevalence of ASD has risen steadily over the past two decades, with current estimates indicating that approximately 1 in 54 children are diagnosed with the disorder [2].. Despite the increasing prevalence of ASD and studies indicating that the symptom undergoes age-related heterogenous when comparing ASD to neurotypical controls (NC), its underlying neurobiological mechanisms remain incompletely understood, particularly regarding how structural Tao Zhang zhangtao1698@xhu.edu.cn Article Title: Dynamic functional connections analysis with spectral learning for brain disorder detection. Article Snippet: Dynamic functional connections (dFCs), can reveal neural activities, which provides an insightful way of mining the temporal patterns within the human brain and further detecting brain disorders.. However, most existing studies focus on the dFCs estimation to identify brain disorders by shallow temporal features and methods, which cannot capture the inherent temporal patterns of dFCs effectively.. To address this problem, this study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of dFCs and further detect the brain disorders. Article Title: FROG: A Fine-Grained Spatiotemporal Graph Neural Network With Self-Supervised Guidance for Early Diagnosis of Alzheimer's Disease Article Snippet: Functional magnetic resonance imaging (fMRI) has demonstrated significant potential in the early diagnosis and study of pathological mechanisms of Alzheimer’s disease (AD).. To fit subtle crossspatiotemporal interaction and learn pathological feature from fMRI, we proposed a fine-grained spatiotemporal graph neural network with self-supervised learning (SSL) for diagnosis and biomarker extraction of early AD.. First, Considering the spatiotemporal interaction of the brain, we designed two masks that leverage the spatial correlation and temporal repeability of the fMRI. Article Title: Functional connectivity changes in the prefrontal-thalamic-cerebellar circuit in adolescents with first-episode Schizophrenia. Article Snippet: 1 Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China 2 Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China 3 International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China 4 Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China 5 Xinxiang Key Laboratory of Child and Adolescent Psychiatry, Xinxiang 453002, China 6 Brain Institute, Henan Academy of Innovations in Medical Science, Xinxiang 453002, China 7 The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China Abstract Background The role of functional connectivity (FC) changes in the prefrontal-thalamic-cerebellar circuit in schizophrenia has received widespread attention.. Currently, domestic and international studies on this circuit have focused on adults, while the FC of this circuit in the brains of adolescent patients with schizophrenia (AOS) has been less reported.. Methods We recruited 124 subjects who underwent resting-state functional magnetic resonance imaging scans at baseline, using key brain regions in the prefrontal-thalamic-cerebellar circuit as seed regions, to investigate the FC in AOS versus healthy controls. Article Title: Diverse frontoparietal connectivity supports semantic prediction and integration in sentence comprehension Article Snippet: Research Articles | Behavioral/Cognitive Diverse frontoparietal connectivity supports semantic prediction and integration in sentence comprehension https://doi.org/10.1523/JNEUROSCI.1404-24.2024 Received: 21 June 2024 Revised: 14 September 2024 Accepted: 5 November 2024 Copyright © 2024 He et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.. This Early Release article has been peer reviewed and accepted, but has not been through the composition and copyediting processes.The final version may differ slightly in style or formatting and will contain links to any extended data. Article Title: Multi-Site rs-fMRI Domain Alignment for Autism Spectrum Disorder Auxiliary Diagnosis Based on Hyperbolic Space Article Snippet: Increasing the volume of training data can enable the auxiliary diagnostic algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and stable models.. However, due to the significant heterogeneity and domain shift in rs-fMRI data across different sites, the accuracy of auxiliary diagnosis remains unsatisfactory.. Moreover, there has been limited exploration of multisource domain adaptation models on ASD recognition, and many existing models lack inherent interpretability, as they do not explicitly incorporate prior neurobiological knowledge such as the hierarchical structure of functional brain networks. Article Title: Functional Connectivity Changes in the Prefrontal-thalamic- cerebellar Circuit in Adolescents with First-Episode Schizophrenia Article Snippet: Chao-Gan, Y. and Yu-Feng, Article Title: Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression. Article Snippet: Yan C, |
