Submit to Brain Sciences Review for Brain Sciences Propose a Special Issue

Journal Browser

► Journal Browser

Recent Advances in Neuroimaging and Neurophysiology in Psychiatry

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 16932

Share This Special Issue

Special Issue Editor

Prof. Dr. Jung Seok Choi

E-Mail Website
Guest Editor

Department of Psychiatry, Samsung Medical Center, Seoul, Korea
Interests: neuroimage; neurophysiology; neuromodulation; digital therapy; psychiatry

Special Issue Information

Dear Colleagues,

Psychiatric conditions represent a highly heterogeneous group of disorders that are associated with chronic distress and a decline in the quality of life for the patients and their surrounding environment. However, the Diagnostic and Statistical Manual (DSM) in its fifth iteration fails to capture the multiple biomarkers which characterize many psychiatric disorders, and in order to understand the pathophysiology of psychiatric disorders and find therapeutic targets, it is necessary to advance neuroimaging and neurophysiological methodologies.

This Special Issue aims to present cutting-edge studies of neuroimaging and neurophysiology in the field of psychiatric disorders. In order to provide a comprehensive perspective, we welcome original research papers and review papers describing the use of the latest brain science approaches including multi-modal analysis, connectivity analysis, machine learning approaches or neural plasticity associated with neuromodulation in psychiatric disorders. This Special Issue will provide innovative investigations to understand neurobiological mechanisms and set the groundwork for the development of the personalized treatment of psychiatric disorders.

Prof. Dr. Jung Seok Choi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

neuroimaging
neurophysiology
multi-modal
connectivity
machine learning
neuromodulation
neural plasticity

Published Papers (5 papers)

Download All Papers

Research

Jump to: Review

13 pages, 766 KiB

Open AccessArticle

Psychopathology and Integrity of the Superior Longitudinal Fasciculus in Deficit and Nondeficit Schizophrenia

by Piotr Podwalski, Ernest Tyburski, Krzysztof Szczygieł, Krzysztof Rudkowski, Katarzyna Waszczuk, Wojciech Andrusewicz, Jolanta Kucharska-Mazur, Anna Michalczyk, Monika Mak, Katarzyna Cyranka, Błażej Misiak, Leszek Sagan and Jerzy Samochowiec

Brain Sci. 2022, 12(2), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12020267 - 14 Feb 2022

Cited by 6 | Viewed by 2442

Abstract

The superior longitudinal fasciculus (SLF) is a white matter bundle that connects the frontal areas with the parietal areas. As part of the visuospatial attentional network, it may be involved in the development of schizophrenia. Deficit syndrome (DS) is characterized by primary and enduring negative symptoms. The present study assessed SLF integrity in DS and nondeficit schizophrenia (NDS) patients and examined possible relationships between it and psychopathology. Twenty-six DS patients, 42 NDS patients, and 36 healthy controls (HC) underwent psychiatric evaluation and diffusion tensor imaging (DTI). After post-processing, fractional anisotropy (FA) values within the SLF were analyzed. Psychopathology was assessed with the Positive and Negative Syndrome Scale, Brief Negative Symptom Scale, and Self-evaluation of Negative Symptoms. The PANSS proxy for the deficit syndrome was used to diagnose DS. NDS patients had lower FA values than HC. DS patients had greater negative symptoms than NDS patients. After differentiating clinical groups and HC, we found no significant correlations between DTI measures and psychopathological dimensions. These results suggest that changes in SLF integrity are related to schizophrenia, and frontoparietal dysconnection plays a role in its etiopathogenesis. We confirmed that DS patients have greater negative psychopathology than NDS patients. These results are preliminary; further studies are needed. Full article

(This article belongs to the Special Issue Recent Advances in Neuroimaging and Neurophysiology in Psychiatry)

► Show Figures

Figure 1

12 pages, 2684 KiB

Open AccessArticle

Associations between Heart Rate Variability and Brain Activity during a Working Memory Task: A Preliminary Electroencephalogram Study on Depression and Anxiety Disorder

by Deokjong Lee, Woohyun Kwon, Jaeseok Heo and Jin Young Park

Brain Sci. 2022, 12(2), 172; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12020172 - 28 Jan 2022

Cited by 5 | Viewed by 3058

Abstract

Heart rate variability (HRV) has been suggested to reflect executive function and related neural activity. Executive dysfunction has been suggested to play an important role in the pathophysiology of emotional disorders. The purpose of this study was to investigate whether HRV showed a significant correlation with electroencephalogram (EEG) during a working memory performance in patients with depressive or anxiety disorder. A retrospective analysis was conducted with data from 61 patients with depressive disorder (43 women and 18 men) and 59 patients with anxiety disorder (35 women and 24 men). HRV was measured in the resting state, and EEG was recorded in the resting state and during the execution of a working memory task. It was performed in patients with depressive and anxiety disorder, and the paired sample t-test between resting state and task performance, as well as the partial correlation analysis between HRV and EEG, was conducted. Both depressed and anxious patients showed weaker beta relative power during the working memory task compared to the rest period. The resting-state EEG did not correlate with HRV parameters in both groups. In depressed patients, HRV showed a positive correlation with delta power during the task and a negative correlation with beta relative power during the task. In patients with anxiety disorder, HRV showed a significant positive correlation with theta power of the right frontal region during the task. Our results suggest that HRV would be related to executive-function-related neural activity in patients with depressive or anxiety disorder. Future studies with more subjects, including healthy controls, are needed to verify the correlation between HRV and EEG and to come up with a more comprehensive picture of neurobiological changes in emotional disorders. Full article

(This article belongs to the Special Issue Recent Advances in Neuroimaging and Neurophysiology in Psychiatry)

► Show Figures

Graphical abstract

17 pages, 2752 KiB

Open AccessArticle

Early Schizophrenia and Bipolar Disorder Patients Display Reduced Neural Prepulse Inhibition

by Rodrigo San-Martin, Maria Inês Zimiani, Milton Augusto Vendramini de Ávila, Rosana Shuhama, Cristina Marta Del-Ben, Paulo Rossi Menezes, Francisco José Fraga and Cristiane Salum

Brain Sci. 2022, 12(1), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12010093 - 11 Jan 2022

Cited by 4 | Viewed by 2294

Abstract

Background: Altered sensorimotor gating has been demonstrated by Prepulse Inhibition (PPI) tests in patients with psychosis. Recent advances in signal processing methods allow assessment of neural PPI through electroencephalogram (EEG) recording during acoustic startle response measures (classic muscular PPI). Simultaneous measurements of muscular (eye-blink) and neural gating phenomena during PPI test may help to better understand sensorial processing dysfunctions in psychosis. In this study, we aimed to assess simultaneously muscular and neural PPI in early bipolar disorder and schizophrenia patients. Method: Participants were recruited from a population-based case-control study of first episode psychosis. PPI was measured using electromyography (EMG) and EEG in pulse alone and prepulse + pulse with intervals of 30, 60, and 120 ms in early bipolar disorder (n = 18) and schizophrenia (n = 11) patients. As control group, 15 socio-economically matched healthy subjects were recruited. All subjects were evaluated with Rating Scale, Hamilton Rating Scale for Depression, and Young Mania Rating Scale questionnaires at recruitment and just before PPI test. Wilcoxon ranked sum tests were used to compare PPI test results between groups. Results: In comparison to healthy participants, neural PPI was significantly reduced in PPI 30 and PPI60 among bipolar and schizophrenia patients, while muscular PPI was reduced in PPI60 and PPI120 intervals only among patients with schizophrenia. Conclusion: The combination of muscular and neural PPI evaluations suggested distinct impairment patterns among schizophrenia and bipolar disorder patients. Simultaneous recording may contribute with novel information in sensory gating investigations. Full article

(This article belongs to the Special Issue Recent Advances in Neuroimaging and Neurophysiology in Psychiatry)

► Show Figures

Figure 1

11 pages, 946 KiB

Open AccessArticle

Relationship between Resting-State Alpha Coherence and Cognitive Control in Individuals with Internet Gaming Disorder: A Multimodal Approach Based on Resting-State Electroencephalography and Event-Related Potentials

by Minkyung Park, So Young Yoo, Ji-Yoon Lee, Ja Wook Koo, Ung Gu Kang and Jung-Seok Choi

Brain Sci. 2021, 11(12), 1635; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci11121635 - 11 Dec 2021

Cited by 3 | Viewed by 2834

Abstract

The human brain is constantly active, even at rest. Alpha coherence is an electroencephalography (EEG) rhythm that regulates functional connectivity between different brain regions. However, the relationships between resting-state alpha coherence and N2/P3 components associated with response inhibition and cognitive processes have not been investigated in addictive disorders. The present study investigated the relationships between alpha coherence during the resting state and N2/P3 components of event-related potentials during the Go/Nogo task in healthy controls (HCs) and patients with Internet gaming disorder (IGD). A total of 64 young adults (HC: n = 31; IGD: n = 33) participated in this study. Alpha coherence values at left fronto-central and bilateral centro-temporal electrode sites were significantly correlated with P3 latency in HCs, whereas inverse correlations were observed in patients with IGD. Furthermore, significant differences were observed in the correlation values between the groups. Our results suggest that patients with IGD lack dynamic interactions of functional connectivity between the fronto-centro-temporal regions during the resting state and the event-related potential (ERP) index during cognitive tasks. The findings of this study may have important implications for understanding the neurophysiological mechanisms linking resting-state EEG and task-related ERPs underlying IGD. Full article

(This article belongs to the Special Issue Recent Advances in Neuroimaging and Neurophysiology in Psychiatry)

► Show Figures

Figure 1

Review

Jump to: Research

21 pages, 3837 KiB

Open AccessEditor’s ChoiceReview

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

by Alfred Lenin Fred, Subbiahpillai Neelakantapillai Kumar, Ajay Kumar Haridhas, Sayantan Ghosh, Harishita Purushothaman Bhuvana, Wei Khang Jeremy Sim, Vijayaragavan Vimalan, Fredin Arun Sedly Givo, Veikko Jousmäki, Parasuraman Padmanabhan and Balázs Gulyás

Brain Sci. 2022, 12(6), 788; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12060788 - 15 Jun 2022

Cited by 15 | Viewed by 5559

Abstract

Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNR_MEG = 2.2 db, SNR_EEG < 1 db) and spatial resolution (SR_MEG = 2–3 mm, SR_EEG = 7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics. Full article

(This article belongs to the Special Issue Recent Advances in Neuroimaging and Neurophysiology in Psychiatry)

► Show Figures