Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry
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 underst...
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MDPI AG
2024-11-01
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| author | Valeria Di Stefano Martina D’Angelo Francesco Monaco Annarita Vignapiano Vassilis Martiadis Eugenia Barone Michele Fornaro Luca Steardo Marco Solmi Mirko Manchia Luca Steardo |
| author_facet | Valeria Di Stefano Martina D’Angelo Francesco Monaco Annarita Vignapiano Vassilis Martiadis Eugenia Barone Michele Fornaro Luca Steardo Marco Solmi Mirko Manchia Luca Steardo |
| author_sort | Valeria Di Stefano |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-16f6af57068c4ea59b0928a5f70f4ef0 |
| institution | DOAJ |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-16f6af57068c4ea59b0928a5f70f4ef02025-08-20T02:53:37ZengMDPI AGBrain Sciences2076-34252024-11-011412119610.3390/brainsci14121196Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision PsychiatryValeria Di Stefano0Martina D’Angelo1Francesco Monaco2Annarita Vignapiano3Vassilis Martiadis4Eugenia Barone5Michele Fornaro6Luca Steardo7Marco Solmi8Mirko Manchia9Luca Steardo10Psychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, ItalyPsychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, ItalyDepartment of Mental Health, Azienda Sanitaria Locale Salerno, 84125 Salerno, ItalyDepartment of Mental Health, Azienda Sanitaria Locale Salerno, 84125 Salerno, ItalyDepartment of Mental Health, Azienda Sanitaria Locale (ASL) Napoli 1 Centro, 80145 Naples, ItalyDepartment of Psychiatry, University of Campania “Luigi Vanvitelli”, 80138 Naples, ItalyDepartment of Neuroscience, Reproductive Science and Odontostomatology, University of Naples Federico II, 80138 Naples, ItalyDepartment of Clinical Psychology, University Giustino Fortunato, 82100 Benevento, ItalyDepartment of Psychiatry, University of Ottawa, Ottawa, ON K1N 6N5, CanadaSection of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, 09124 Cagliari, ItalyPsychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, ItalySchizophrenia, 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.https://www.mdpi.com/2076-3425/14/12/1196schizophreniafMRIartificial intelligencedeep learningmachine learning |
| spellingShingle | Valeria Di Stefano Martina D’Angelo Francesco Monaco Annarita Vignapiano Vassilis Martiadis Eugenia Barone Michele Fornaro Luca Steardo Marco Solmi Mirko Manchia Luca Steardo Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry Brain Sciences schizophrenia fMRI artificial intelligence deep learning machine learning |
| title | Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry |
| title_full | Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry |
| title_fullStr | Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry |
| title_full_unstemmed | Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry |
| title_short | Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry |
| title_sort | decoding schizophrenia how ai enhanced fmri unlocks new pathways for precision psychiatry |
| topic | schizophrenia fMRI artificial intelligence deep learning machine learning |
| url | https://www.mdpi.com/2076-3425/14/12/1196 |
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