New Perspectives in Treatment of Psychiatric Disorders: Focus on Neuroimaging

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Psychiatric Diseases".

Deadline for manuscript submissions: closed (19 October 2024) | Viewed by 2058

Special Issue Editors


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Guest Editor
Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
Interests: psychosis; cognition; functioning; rehabilitation; autism

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Guest Editor
Neuroradiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia and ASST Spedali Civili Hospital, 25121 Brescia, Italy
Interests: neuroimaging; machine learning; fMRI; eye tracking; movement; cognitive functions
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Special Issue Information

Dear Colleagues,

While pharmacotherapy remains the primary approach in psychiatric treatment, new non-pharmacological and integrated approaches are gaining increasing scientific relevance. The integration of research in neuroimaging and such treatments holds the potential to enhance the management of neuropsychiatric conditions significantly.

In this context, neuroimaging may have the potential to pave the way for a deeper understanding of the connections between treatment, brain functioning, and structure.

This Special Issue aims to update the recent advances in the treatment of psychiatric disorders and neuroimaging and their implications for effective interventions.

Original studies, meta-analyses and reviews are accepted, and contributions that delve into advanced imaging technologies, cutting-edge data analysis methods, and novel therapies are most welcomed.

Dr. Giacomo Deste
Dr. Daniele Corbo
Guest Editors

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Keywords

  • neuroimaging
  • integrated therapy
  • neuropsychopharmacology
  • neuropsychiatric disorders
  • fMRI
  • TMS
  • tdcs

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Published Papers (1 paper)

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Research

17 pages, 4003 KiB  
Article
Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study
by Shiting Qian, Qinqin Yang, Congbo Cai, Jiyang Dong and Shuhui Cai
Brain Sci. 2024, 14(5), 507; https://doi.org/10.3390/brainsci14050507 - 17 May 2024
Viewed by 1485
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In [...] Read more.
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD. Full article
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