Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

Chyzhyk, Darya; Savio, Alexandre; Graña, Manuel

doi:10.1016/j.neunet.2015.04.002

Cited by 75 publications

(40 citation statements)

References 59 publications

(58 reference statements)

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“…In recent years, the number of neurobiological literatures using fMRI to study SZ disease has increased significantly. fMRI is usually applied to discover anomalous patterns present in activation maps [i.e., Regional Homogeneity (REHO), Amplitude of Low Frequency Fluctuations (ALFF), fractional Amplitude of Low Frequency Fluctuations (FALFF)] (Guo et al, 2014;Chyzhyk et al, 2015;Huang et al, 2018) of SZ. These activation maps are widely used as potential clinical biomarkers for the diagnosis of SZ.…”

Section: Introductionmentioning

confidence: 99%

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Xiang

Wang

Tan

et al. 2020

Front. Bioeng. Biotechnol.

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Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.

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Section: Introductionmentioning

confidence: 99%

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Xiang

Wang

Tan

et al. 2020

Front. Bioeng. Biotechnol.

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“…It is particularly in these kinds of applications where we expect the conformal analysis to be most useful. The classification accuracy achieved with SCBconf appeared to outperform recent published analyses of the same data (Chyzhyk et al 2015; Kim et al 2016). However, note that the direct comparison of the classification performance with these works is not fair since it is subject to the differences in variable extraction (different variables were used), data processing (different subjects were excluded) and evaluation (different cross-validation folds were used).…”

Section: Resultsmentioning

confidence: 63%

Sign-consistency based variable importance for machine learning in brain imaging

Gómez-Verdejo

Parrado-Hernández

Tohka

2017

Preprint

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An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables for single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous.Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. Preprint submitted to ElsevierApril 5, 2017. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/124453 doi: bioRxiv preprint first posted online The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.

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“…Various machine‐learning techniques have been applied to mass rs‐fcMRI data to objectively identify disorder‐specific abnormalities in mental disorders, with which automatic case–control classifications were employed. Table summarizes the previous attempts for schizophrenia, MDD, ADHD, and ASD . The take‐home messages of this summary are as follows: (i) irrespective of disorder type, classification accuracy is, overall, 80–90%, comparable to those based on structural MRI data; (ii) in many studies, especially for schizophrenia and MDD, the sample per group (case or control) is typically comprised of fewer than 100 participants; (iii) for all schizophrenia and MDD studies, the imaging data were acquired at a single site, whereas for many ADHD and ASD studies, the imaging data came from multiple sites, thanks to the recent multicenter imaging campaigns for these disorders; (iv) inter‐regional functional connectivity and the associated graph metrics are popular features used for classification; (v) head motions during scanning have been known to introduce artifacts in the functional connectivity estimate, the effects of which are controlled by regression, masking (scrubbing), or independent component analysis; (vi) BOLD signal fluctuations of non‐neuronal origins, such as respiration and cardiac activity, are removed by regressing out the signals in white matter and cerebrospinal fluid, although further inclusion of global signal fluctuation into the regressor is not unanimous among the studies due to the recent controversy; (vii) support vector machine (SVM) and its variants are popular prediction methods, although some studies use classifiers with embedded regularization frameworks, such as least absolute shrinkage and selection operator (LASSO); (viii) leave‐one‐out and k‐fold cross‐validation procedures are popular methods for model evaluation; and (ix) for all but one study, the generalization capability of a classification scheme is untested in an independent cohort.…”

Section: Data‐driven Approach For Making Predictions In Clinical Settmentioning

confidence: 99%

Computational neuroscience approach to biomarkers and treatments for mental disorders

Yahata

Kasai

Kawato

2017

Psychiatry Clin Neurosci

105

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Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations into computational neuroscience that have undertaken either theory-or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorderspecific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies have explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorderspecific features have been used for automatic casecontrol classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing 'theranostics' for the first time in clinical psychiatry.

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Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

Cited by 75 publications

References 59 publications

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks

Sign-consistency based variable importance for machine learning in brain imaging

Computational neuroscience approach to biomarkers and treatments for mental disorders

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