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Neuro-Psychiatric Disorders: Updates on Diagnosis and Treatment
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Neuro-Psychiatric Disorders: Updates on Diagnosis and Treatment

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Mental Health".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3200

Special Issue Editor

Dr. Georgios Floros

E-Mail Website
Guest Editor
2nd Department of Psychiatry, Aristotle University of Thessaloniki, 54624 Thessaloniki, Greece
Interests: behavioral addictions; psychopathology; personality disorders; psychotherapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuropsychiatric disorders are specific, clinically diagnosed conditions with poorly defined neurobiological bases. They greatly affect an individual's thoughts, perceptions, emotions, and/or overt behavior and may lead to loss of functioning and severe distress. Recent advances in genome research and neuroimaging have led to considerable advances in our understanding of the hereditary and acquired factors related to the etiopathology of those disorders. Hence, we are slowly starting to be able to more precisely link the underlying pathology to the overt clinical signs, helping us to provide an accurate diagnosis and an effective therapeutic plan.

This Special Issue aims to present the latest advances in the diagnostic domain and how they relate to the provision of better treatment options for the benefit of our patients.

Dr. Georgios Floros
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. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly 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

  • neuropsychiatric disorder
  • diagnosis
  • treatment
  • neurobiology
  • organic psychiatry

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Published Papers (2 papers)

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12 pages, 1165 KiB  
Article
Development and Validation of Machine Learning Models to Predict Postoperative Delirium Using Clinical Features and Polysomnography Variables
by Woo-Seok Ha, Bo-Kyu Choi, Jungyeon Yeom, Seungwon Song, Soomi Cho, Min-Kyung Chu, Won-Joo Kim, Kyoung Heo and Kyung-Min Kim
J. Clin. Med. 2024, 13(18), 5485; https://doi.org/10.3390/jcm13185485 - 16 Sep 2024
Viewed by 1147
Abstract
Background: Delirium affects up to 50% of patients following high-risk surgeries and is associated with poor long-term prognosis. This study employed machine learning to predict delirium using polysomnography (PSG) and sleep-disorder questionnaire data, and aimed to identify key sleep-related factors for improved interventions [...] Read more.
Background: Delirium affects up to 50% of patients following high-risk surgeries and is associated with poor long-term prognosis. This study employed machine learning to predict delirium using polysomnography (PSG) and sleep-disorder questionnaire data, and aimed to identify key sleep-related factors for improved interventions and patient outcomes. Methods: We studied 912 adults who underwent surgery under general anesthesia at a tertiary hospital (2013–2024) and had PSG within 5 years of surgery. Delirium was assessed via clinical diagnoses, antipsychotic prescriptions, and psychiatric consultations within 14 days postoperatively. Sleep-related data were collected using PSG and questionnaires. Machine learning predictions were performed to identify postoperative delirium, focusing on model accuracy and feature importance. Results: This study divided the 912 patients into an internal training set (700) and an external test set (212). Univariate analysis identified significant delirium risk factors: midazolam use, prolonged surgery duration, and hypoalbuminemia. Sleep-related variables such as fewer rapid eye movement (REM) episodes and higher daytime sleepiness were also linked to delirium. An extreme gradient-boosting-based classification task achieved an AUC of 0.81 with clinical variables, 0.60 with PSG data alone, and 0.84 with both, demonstrating the added value of PSG data. Analysis of Shapley additive explanations values highlighted important predictors: surgery duration, age, midazolam use, PSG-derived oxygen saturation nadir, periodic limb movement index, and REM episodes, demonstrating the relationship between sleep patterns and the risk of delirium. Conclusions: The artificial intelligence model integrates clinical and sleep variables and reliably identifies postoperative delirium, demonstrating that sleep-related factors contribute to its identification. Predicting patients at high risk of developing postoperative delirium and closely monitoring them could reduce the costs and complications associated with delirium. Full article
(This article belongs to the Special Issue Neuro-Psychiatric Disorders: Updates on Diagnosis and Treatment)
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22 pages, 6422 KiB  
Systematic Review
Metaanalysis of Repetitive Transcranial Magnetic Stimulation (rTMS) Efficacy for OCD Treatment: The Impact of Stimulation Parameters, Symptom Subtype and rTMS-Induced Electrical Field
by Fateme Dehghani-Arani, Reza Kazemi, Amir-Homayun Hallajian, Sepehr Sima, Samaneh Boutimaz, Sepideh Hedayati, Saba Koushamoghadam, Razieh Safarifard and Mohammad Ali Salehinejad
J. Clin. Med. 2024, 13(18), 5358; https://doi.org/10.3390/jcm13185358 - 10 Sep 2024
Viewed by 1666
Abstract
Background: Repetitive transcranial magnetic stimulation (rTMS) has recently demonstrated significant potential in treating obsessive-compulsive disorder (OCD). However, its effectiveness depends on various parameters, including stimulation parameters, OCD subtypes and electrical fields (EFs) induced by rTMS in targeted brain regions that are less [...] Read more.
Background: Repetitive transcranial magnetic stimulation (rTMS) has recently demonstrated significant potential in treating obsessive-compulsive disorder (OCD). However, its effectiveness depends on various parameters, including stimulation parameters, OCD subtypes and electrical fields (EFs) induced by rTMS in targeted brain regions that are less studied. Methods: Using the PRISMA approach, we examined 27 randomized control trials (RCTs) conducted from 1985 to 2024 using rTMS for the treatment of OCD and conducted several meta-analyses to investigate the role of rTMS parameters, including the EFs induced by each rTMS protocol, and OCD subtypes on treatment efficacy. Results: A significant, medium effect size was found, favoring active rTMS (gPPC = 0.59, p < 0.0001), which was larger for the obsession subscale. Both supplementary motor area (SMA) rTMS (gPPC = 0.82, p = 0.048) and bilateral dorsolateral prefrontal cortex (DLPFC) rTMS (gPPC = 1.14, p = 0.04) demonstrated large effect sizes, while the right DLPFC showed a significant moderate effect size for reducing OCD severity (gPPC = 0.63, p = 0.012). These protocols induced the largest EFs in dorsal cognitive, ventral cognitive and sensorimotor circuits. rTMS protocols targeting DLPFC produced the strongest electrical fields in cognitive circuits, while pre-supplementary motor area (pre-SMA) and orbitofrontal cortex (OFC) rTMS protocols induced larger fields in regions linked to emotional and affective processing in addition to cognitive circuits. The pre-SMA rTMS modulated more circuits involved in OCD pathophysiology—sensorimotor, cognitive, affective, and frontolimbic—with larger electrical fields than the other protocols. Conclusions: While rTMS shows moderate overall clinical efficacy, protocols targeting ventral and dorsal cognitive and sensorimotor circuits demonstrate the highest potential. The pre-SMA rTMS appears to induce electrical fields in more circuits relevant to OCD pathophysiology. Full article
(This article belongs to the Special Issue Neuro-Psychiatric Disorders: Updates on Diagnosis and Treatment)
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