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Volume 14
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10.3390/brainsci14121196
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Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry

by
Valeria Di Stefano
1,
Martina D’Angelo
1,*,
Francesco Monaco
2,3,
Annarita Vignapiano
2,3,
Vassilis Martiadis
4,
Eugenia Barone
5,
Michele Fornaro
6,
Luca Steardo
7,8,
Marco Solmi
9,10,11,12,13,
Mirko Manchia
14,15,16 and
Luca Steardo Jr.
1
1
Psychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy
2
Department of Mental Health, Azienda Sanitaria Locale Salerno, 84125 Salerno, Italy
3
European Biomedical Research Institute of Salerno (EBRIS), 84125 Salerno, Italy
4
Department of Mental Health, Azienda Sanitaria Locale (ASL) Napoli 1 Centro, 80145 Naples, Italy
5
Department of Psychiatry, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
6
Department of Neuroscience, Reproductive Science and Odontostomatology, University of Naples Federico II, 80138 Naples, Italy
7
Department of Clinical Psychology, University Giustino Fortunato, 82100 Benevento, Italy
8
Department of Physiology and Pharmacology “Vittorio Erspamer”, SAPIENZA University of Rome, 00185 Rome, Italy
9
Department of Psychiatry, University of Ottawa, Ottawa, ON K1N 6N5, Canada
10
On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, ON K1H 8L6, Canada
11
Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON K1N 6N5, Canada
12
School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON K1N 6N5, Canada
13
Department of Child and Adolescent Psychiatry, Charité-Universitätsmedizin, 10117 Berlin, Germany
14
Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, 09124 Cagliari, Italy
15
Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09123 Cagliari, Italy
16
Department of Pharmacology, Dalhousie University, Halifax, NS B3H 4R2, Canada
 
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*
Author to whom correspondence should be addressed.
Brain Sci. 2024, 14(12), 1196; https://doi.org/10.3390/brainsci14121196
Submission received: 23 October 2024 / Revised: 24 November 2024 / Accepted: 25 November 2024 / Published: 27 November 2024
(This article belongs to the Section Psychiatric Diseases)
Versions Notes

Abstract

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia’s structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder’s heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI’s integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry.
Keywords: schizophrenia; fMRI; artificial intelligence; deep learning; machine learning schizophrenia; fMRI; artificial intelligence; deep learning; machine learning

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MDPI and ACS Style

Di Stefano, V.; D’Angelo, M.; Monaco, F.; Vignapiano, A.; Martiadis, V.; Barone, E.; Fornaro, M.; Steardo, L.; Solmi, M.; Manchia, M.; et al. Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry. Brain Sci. 2024, 14, 1196. https://doi.org/10.3390/brainsci14121196

AMA Style

Di Stefano V, D’Angelo M, Monaco F, Vignapiano A, Martiadis V, Barone E, Fornaro M, Steardo L, Solmi M, Manchia M, et al. Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry. Brain Sciences. 2024; 14(12):1196. https://doi.org/10.3390/brainsci14121196

Chicago/Turabian Style

Di Stefano, Valeria, Martina D’Angelo, Francesco Monaco, Annarita Vignapiano, Vassilis Martiadis, Eugenia Barone, Michele Fornaro, Luca Steardo, Marco Solmi, Mirko Manchia, and et al. 2024. "Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry" Brain Sciences 14, no. 12: 1196. https://doi.org/10.3390/brainsci14121196

APA Style

Di Stefano, V., D’Angelo, M., Monaco, F., Vignapiano, A., Martiadis, V., Barone, E., Fornaro, M., Steardo, L., Solmi, M., Manchia, M., & Steardo Jr., L. (2024). Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry. Brain Sciences, 14(12), 1196. https://doi.org/10.3390/brainsci14121196

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