Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

doi:10.1117/12.2043240

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…They identified 23 ROI of the resting-state functional language network that are considered important in language comprehension and production (eg, Wernicke’s area, Broca’s area, inferior parietal, inferior temporal). They reached an accuracy of 83.6%, a sensitivity of 81.5% and a specificity of 85.7% 31…”

Section: Resultsmentioning

confidence: 95%

<p>Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review</p>

Filippis¹,

Carbone²,

Gaetano³

et al. 2019

NDT

106

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Background Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. Objectives A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. Methods We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75–90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. Results Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). Conclusion The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.

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Section: Resultsmentioning

confidence: 95%

<p>Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review</p>

Filippis¹,

Carbone²,

Gaetano³

et al. 2019

NDT

106

View full text Add to dashboard Cite

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The Necessity of Leave One Subject Out (LOSO) Cross Validation for EEG Disease Diagnosis

Kunjan

Grummett

Pope

et al. 2021

Lecture Notes in Computer Science

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Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

et al. 2017

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Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.

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Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

Cited by 4 publications

References 10 publications

<p>Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review</p>

<p>Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review</p>

The Necessity of Leave One Subject Out (LOSO) Cross Validation for EEG Disease Diagnosis

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

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