Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper.

An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works.

Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future

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Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review.

/pdf/an-overview-of-artificial-intelligence-techniques-for-1awnlgr4.pdf

An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works

Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia

It has previously been shown that cerebello-thalamo-cortical (CTC) hyperconnectivity is likely a state-independent neural signature for psychosis. However, the potential clinical utility of this change has not yet been evaluated. Here, using fMRI and clinical data acquired from 214 untreated first-episode patients with schizophrenia (62 of whom were clinically followed-up at least once at the 12th and 24th months after treatment initiation) and 179 healthy controls, we investigated whether CTC hyperconnectivity would serve as an individualized biomarker for diagnostic classification and prediction of long-term treatment outcome. Cross-validated LASSO regression was conducted to estimate the accuracy of baseline CTC connectivity for patient-control classification, with the generalizability of classification performance tested in an independent sample including 42 untreated first-episode patients and 65 controls. Associations between baseline CTC connectivity and clinical outcomes were evaluated using linear mixed model and leave-one-out cross validation. We found significantly increased baseline CTC connectivity in patients (P = .01), which remained stable after treatment. Measures of CTC connectivity discriminated patients from controls with moderate classification accuracy (AUC = 0.68, P < .001), and the classification model had good generalizability in the independent sample (AUC = 0.70, P < .001). Higher CTC connectivity at baseline significantly predicted poorer long-term symptom reduction in negative symptoms (R = 0.31, P = .01) but not positive or general symptoms. These findings provide initial evidence for the putative "CTC hyperconnectivity" anomaly as an individualized diagnostic and prognostic biomarker for schizophrenia, and highlight the potential of this measure in precision psychiatry.

Cerebello-Thalamo-Cortical Hyperconnectivity Classifies Patients and Predicts Long-Term Treatment Outcome in First-Episode Schizophrenia.

Objective To develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke. Materials and methods A total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically. Results Of the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH. Conclusion This model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.

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Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke

Previous neuroimaging studies have shown that the cortical functional dysconnectivity contributed to the etiology of schizophrenia (SCZ). However, the interhemispheric functional interactions in SCZ remain not fully understood. The clinical heterogeneity of SCZ might lead to the variations in the findings and the effect of medication had a profound influence on the patient’s functional features. In this study, we were aimed at investigating the interhemispheric functional interactions in drug-naive adult-onset first episode psychosis (FEP) patients. Using voxel-mirrored homotopic connectivity (VMHC) analysis for functional magnetic resonance imaging (fMRI) data, we found decreased VMHC values in precuneus, posterior cingulate cortex (PCC), pallidum gyrus, inferior temporal gyrus and superior temporal gyrus in FEP patients. Additionally, the peak VMHC value of PCC was negatively correlated with the PANSS depression factor score. And the peak VMHC value of precuneus was positively correlated with the PANSS positive factor score. The results indicated that the disrupted interhemispheric functional connectivity of posterior default mode network (DMN) was related to emotion and consciousness dysregulation in FEP patients.

Abnormal interhemispheric functional interactions in drug-naïve adult-onset first episode psychosis patients

Objective Primary Trigeminal Neuralgia (PTN) is a common and refractory neurological disease. Conventional vascular compression theory could not completely explain the etiology and pathogenesis of PTN. This study used diffusion tensor imaging (DTI) to demonstrate the microstructural changes of root entry zone (REZ) region in PTN patients. Materials and methods DTI sequences was performed on PTN patients and healthy controls (HCs). Clinical data included affected side, disease course and visual analogue scale (VAS) were collected. Quantitative DTI variables such as FA, MD, AD and RD of the root entry/Exit zone (REZ) were measured and compared in PTN/HCs, affected/unaffected side, and pre/post operation groups. The PCoA was established to conduct overall differences between PTN group and the HCs. Results A total of 17 patients with PTN (mean age 59.29 ± 8.53; 5 men) and 34 HCs (mean age 57.70 ± 6.37; 10 men) were included. Lower FA value of the affected side of PTN group was observed compared to the unaffected side and the HCs (p = 0.001), whereas the values of MD, AD and RD were significantly increased (p < 0.001). Moreover, the decrease of FA value was recovered post operation. PCoA results of the comprehensive indexes can significantly distinguish PTN group from HCs (r = 0.500, p < 0.001). Conclusion Quantitative variables derived from DTI in REZ had significantly different profiles between PTN patients and HCs, which were associated with VAS situation and the disease course of PTN. The comprehensive index established on DTI variables were of great potential to reveal the microstructure changes in PTN patients and predict the therapeutic effect.

/pdf/a-preliminary-study-of-diffusion-tensor-imaging-in-root-1m00ni6b.pdf

A preliminary study of diffusion tensor imaging in root entry zone of primary trigeminal neuralgia

Middle temporal corpus callosum impairment as a predictor of eight-week treatment outcome of drug-naïve first-episode psychosis patients: A pilot longitudinal study.

Multimodal classification of drug-naïve first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging

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