Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Bae, Youngoh; Kumarasamy, Kunaraj; Ali, Issa; Korfiatis, Panagiotis; Akkus, Zeynettin; Erickson, Bradley J.

doi:10.1007/s10278-017-0020-4

Cited by 36 publications

(24 citation statements)

References 47 publications

(55 reference statements)

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“…Each functional volume contained 35 slices. To minimize the effects of scanner signal stabilization, the first five volumes of each subject were excluded from all analysesOrbitofrontal cortexThe systematically different medications for SCZ and MDD may have different effects on functional connectivity; the medicated patients were in stable condition and this may have an impact on the functional connectivity of cortical networksWang, 201726rs-fMRI 3T79 subjects:-48 drug-naïve AOS-31 HCSVMFC analysisLIBSVMROC92.4%Sn 89.6%Sp 96.8%Authors used regional homogeneity (ReHo), a measurement that reflects brain local functional connectivity or synchronization and indicates regional integration of information processingRight middle frontal gyrus, right superior medial prefrontal cortex, left superior temporal gyrusSVM analysis applied to an independent datasetBae, 201740fMRI 3T75 subjects:-21 SCZ-54 HCSVM92.1%±10.5Sn 92.0±15.8%Sp 92.1±8.1%Precision 94±6.3%Authors created anatomic labels for 90 ROIs from the image database. Any subject with fewer than 85 ROIs automatically labeled was excludedAnterior right cingulate cortex, superior right temporal region, inferior left parietal regionPossible influence of pharmacological treatment and disease stage on the investigated functional connections, they used only n-back tests without rs-fMRIQureshi, 201744rs-fMRI 3T144 subjects:-72 SCZ-72 HCELMSVMLDARF99.3%Sn 100%Sp 98, 6%Authors used measures including the mean cortical thickness, cortical thickness standard deviation, surface area, volume, mean curvature, white matter volume, subcortical segment volume, subcortical intensity, and overall brain volume and intensity as the structural featuresCortical thickness, Surface area, WM/subcortical/overall volume, curvature, global average functional connectivityAuthors developed an ELM, whose effectiveness was compared with that of more known ML methodsReavis, 201725fMRI 3T148 subjects:-50 SCZ-51 BD-47 HCMVPA41%N.A.Structural data were processed and parcellated into anatomical regions used to constrain ROI definitionsLateral occipital lobeThe paper shows MVPA can be used successfully to classify individual perceptual stimuli in SCZ and BD.…”

Section: Resultsmentioning

confidence: 99%

“…The accuracy was 92.1% using SVM and 10-fold cross-validation (sensitivity 92%, specificity 92.1%, precision 94%); this suggests important and significant differences regarding the regional brain activity through fMRI in both groups. The aim of their work was to demonstrate that brain connectivity was significantly altered in patients with SCZ compared with HC and that these differences could be efficiently and more easily detected by ML analysis 40…”

Section: Resultsmentioning

confidence: 99%

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<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

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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: 99%

Section: Resultsmentioning

confidence: 99%

<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

View full text Add to dashboard Cite

show abstract

“…Our study has some limitations. First, although the sample size is limited, it is relatively larger when compared to previous studies [ 1 , 21 , 31 , 50 , 65 ]. A larger sample size should be recruited in the future to replicate and enrich our findings.…”

Section: Discussionmentioning

confidence: 96%

“…Schizophrenia (SZ) is a severe psychiatric illness characterized by aberrant sensory perceptions, cognition, concrete thinking, and a restricted range of emotion, and it affects about 1% of the general population [1][2][3][4][5]. SZ is a heterogeneous disorder, and current diagnoses are based on subjective indicators such as self-report, observation, and clinical history, and its reliable diagnosis is challenging [1,4,6,7], and the pathological mechanism is still unclear [4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Shi

Zhang

et al. 2021

Disease Markers

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Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

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“…Moreover, the age affects network integrity in a more global way so it could be used as a specific flag of functional dysregulation in particular networks affected in SCZ ( 33 ). The results of Bae's study reported a decrease in the global and local network connectivity in SCZ patients compared with HC, especially in the superior right temporal region, in the anterior right cingulate cortex, and the inferior left parietal region with an accuracy of 92.1%, sensitivity of 92%, speciﬁcity of 92.1% and precision 94% ( 31 ). One of the largest studies on SCZ (200 patients vs 200 HC) reported a high diagnostic accuracy (84%) using data from several locations.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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 diseaserelated 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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Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Cited by 36 publications

References 47 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>

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

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

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