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EEG Research in Psychiatry: A Step towards Precision Medicine in...
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EEG Research in Psychiatry: A Step towards Precision Medicine in Mental Health

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Mechanisms of Diseases".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4181

Special Issue Editors

Prof. Dr. Richard E. Frye

E-Mail Website
Guest Editor
1. Rossignol Medical Center, Phoenix, AZ 85050, USA
2. Southwest Autism Research and Resource Center, Phoenix, AZ 85006, USA
3. Autism Discovery and Treatment Foundation, Phoenix, AZ 85050, USA
Interests: neurodevelopment disorders; metabolic disorders; autism; mitochondrial disorders; folate metabolism; redox metabolism
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Amico

E-Mail Website
Guest Editor
1. Neotherapy, Second Level, 2225 N Commerce Pkwy Suite #6, Weston, FL 33326, USA
2. Texas Center for Lifestyle Medicine, 333 West Loop N. Ste 250, Houston, TX 77024, USA
Interests: EEG-based diagnosis and treatment of affective disorders
Dr. Steve Rondeau

E-Mail Website
Guest Editor Assistant
1. Axon EEG Solutions, CEO, Fort Collins, CO 80528, USA
2. Wholeness Center, Private Practice, Fort Collins, CO 80528, USA
Interests: clinical psychiatric EEG databases; artificial intelligence; machine learning; predicting suicide; pharmaco-EEG

Special Issue Information

Dear Colleagues,

Selection of the most effective treatments for mental health disorders is still largely based on subjective symptom detection. In this context, the systematic implementation of evidence-based diagnostic methods could significantly assist mental health professionals in more efficiently identifying neurobehavioral anomalies and suitable therapeutic targets, potentially reducing the patient suffering induced by the current trial-and-error approach.

Emerging technology now allows for the identification of affective disorders such as depression, anxiety and obsessive-compulsive disorder through the combined use of computerized diagnostic batteries, artificial intelligence and comparisons against normative templates, which can potentially enhance the disease categorization process and therefore promote the development of more effective therapeutic interventions.

The aim of this Special Issue is to describe and encourage the utilization of electroencephalography (EEG) in mental health practice. We welcome original research studies as well as reviews attempting to identify and discuss imbalances in the EEG waveform that mental health professionals can familiarize themselves with and systematically consider to more confidently and comprehensively assess complex neurobehavioral syndromes.

Prof. Dr. Richard E. Frye
Dr. Francesco Amico
Guest Editors

Dr. Steve Rondeau
Guest Editor Assistant

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. Journal of Personalized Medicine 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 2600 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

  • pharmaco-EEG
  • EEG biomarkers
  • artificial intelligence
  • precision medicine
  • machine learning
  • clinical database
  • psychiatry
  • personalized medicine

Published Papers (2 papers)

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Research

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16 pages, 4493 KiB  
Article
Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices
by Yoshihiro Noda, Kento Sakaue, Masataka Wada, Mayuko Takano and Shinichiro Nakajima
J. Pers. Med. 2024, 14(1), 101; https://doi.org/10.3390/jpm14010101 - 17 Jan 2024
Viewed by 1228
Abstract
Depression is the disorder with the greatest socioeconomic burdens. Its diagnosis is still based on an operational diagnosis derived from symptoms, and no objective diagnostic indicators exist. Thus, the present study aimed to develop an artificial intelligence (AI) model to aid in the [...] Read more.
Depression is the disorder with the greatest socioeconomic burdens. Its diagnosis is still based on an operational diagnosis derived from symptoms, and no objective diagnostic indicators exist. Thus, the present study aimed to develop an artificial intelligence (AI) model to aid in the diagnosis of depression from electroencephalography (EEG) data by applying machine learning to resting-state EEG and transcranial magnetic stimulation (TMS)-evoked EEG acquired from patients with depression and healthy controls. Resting-state EEG and single-pulse TMS-EEG were acquired from 60 patients and 60 healthy controls. Power spectrum analysis, phase synchronization analysis, and phase-amplitude coupling analysis were conducted on EEG data to extract feature candidates to apply different types of machine learning algorithms. Furthermore, to address the limitation of the sample size, dimensionality reduction was performed in a manner to increase the quality of information by featuring robust neurophysiological metrics that showed significant differences between the two groups. Then, nine different machine learning models were applied to the data. For the EEG data, we created models combining four modalities, including (1) resting-state EEG, (2) pre-stimulus TMS-EEG, (3) post-stimulus TMS-EEG, and (4) differences between pre- and post-stimulus TMS-EEG, and evaluated their performance. We found that the best estimation performance (a mean area under the curve of 0.922) was obtained using receiver operating characteristic curve analysis when linear discriminant analysis (LDA) was applied to the combination of the four feature sets. This study showed that by using TMS-EEG neurophysiological indices as features, it is possible to develop a depression decision-support AI algorithm that exhibits high discrimination accuracy. Full article
(This article belongs to the Special Issue EEG Research in Psychiatry: A Step towards Precision Medicine in Mental Health)
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Review

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13 pages, 621 KiB  
Review
Resting State EEG Correlates of Suicide Ideation and Suicide Attempt
by Francesco Amico, Richard E. Frye, Scott Shannon and Steve Rondeau
J. Pers. Med. 2023, 13(6), 884; https://doi.org/10.3390/jpm13060884 - 24 May 2023
Viewed by 2198
Abstract
Suicide is a global phenomenon that impacts individuals, families, and communities from all income groups and all regions worldwide. While it can be prevented if personalized interventions are implemented, more objective and reliable diagnostic methods are needed to complement interview-based risk assessments. In [...] Read more.
Suicide is a global phenomenon that impacts individuals, families, and communities from all income groups and all regions worldwide. While it can be prevented if personalized interventions are implemented, more objective and reliable diagnostic methods are needed to complement interview-based risk assessments. In this context, electroencephalography (EEG) might play a key role. We systematically reviewed EEG resting state studies of adults with suicide ideation (SI) or with a history of suicide attempts (SAs). After searching for relevant studies using the PubMed and Web of Science databases, we applied the PRISMA method to exclude duplicates and studies that did not match our inclusion criteria. The selection process yielded seven studies, which suggest that imbalances in frontal and left temporal brain regions might reflect abnormal activation and correlate with psychological distress. Furthermore, asymmetrical activation in frontal and posterior cortical regions was detected in high-risk depressed persons, although the pattern in the frontal region was inverted in non-depressed persons. The literature reviewed suggests that SI and SA may be driven by separate neural circuits and that high-risk persons can be found within non-depressed populations. More research is needed to develop intelligent algorithms for the automated detection of high-risk EEG anomalies in the general population. Full article
(This article belongs to the Special Issue EEG Research in Psychiatry: A Step towards Precision Medicine in Mental Health)
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