Special Issue "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: 25 January 2024 | Viewed by 1742

Special Issue Editors

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

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.


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

Published Papers (1 paper)

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Resting State EEG Correlates of Suicide Ideation and Suicide Attempt
J. Pers. Med. 2023, 13(6), 884; https://doi.org/10.3390/jpm13060884 - 24 May 2023
Viewed by 1266
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
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