Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data

Saccà, Valeria; Sarica, Alessia; Novellino, Fabiana; Barone, Stefania; Tallarico, Tiziana; Filippelli, Enrica; Granata, Alfredo; Chiriaco, Carmelina; Bossio, Roberto Bruno; Valentino, Paola; Quattrone, Aldo

doi:10.1007/s11682-018-9926-9

Cited by 64 publications

(45 citation statements)

References 37 publications

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“…In our study, we found that the linear SVM classifier achieved the best performance in all classification process, which suggested that the linear SVM was indeed a good classifier for fMRI data. This finding was consistent with majority of previous studies (Craddock et al, 2009 ; Wang et al, 2016 ; Saccà et al, 2017 ). For example, using the linear SVM classifier, patients with depression were successfully distinguished from healthy volunteers (R. Cameron Craddock), One recent study achieved high accuracy in object categories classification task using functional connections from task-related functional neuroimaging as features and SVM as the classifier.…”

Section: Discussionsupporting

confidence: 94%

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Geng

Liu

et al. 2018

Front. Neurosci.

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Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.

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Section: Discussionsupporting

confidence: 94%

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Geng

Liu

et al. 2018

Front. Neurosci.

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“…57,58 ML specifically for early diagnosis was specified by seven studies for the later onset degenerative conditions MS and RA. 48,[59][60][61][62][63][64] Other diagnostic applications included distinguishing coeliac disease from an at-risk group 65,66 and differentiating those who have complications in T1D. 67,68 Random forests and support vector machine most frequently utilised.…”

Section: Diagnosismentioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

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“…Thus, investigating sex differences at the level of BOLD fluctuation may reveal if there is strong evidence of sex differences. Recently, machine learning (ML) techniques have been used widely to perform classification and regression on neuroscience data (Al Zoubi, Awad, & Kasabov, 2018;Al Zoubi, Ki Wong, et al, 2018;Campbell et al, 2020;Cohen, Chen, Parker Jones, Niu, & Wang, 2020;Du, Fu, & Calhoun, 2018;Garner et al, 2019;Kazeminejad & Sotero, 2019;Saccà et al, 2019). Some works focused on using ML for classifying subjects into male and female using functional (Ktena et al, 2018;Zhang, Dougherty, Baum, White, & Michael, 2018) and structural data (Chekroud et al, 2016;Feis, Brodersen, von Cramon, Luders, & Tittgemeyer, 2013;Rosenblatt, 2016).…”

Section: Introductionmentioning

confidence: 99%

Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

Misaki

Tsuchiyagaito

Zotev

et al. 2020

Preprint

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Sex is an important biological variable often used in analyzing and describing the functional organization of the brain during cognitive and behavioral tasks. Several prior studies have shown that blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) functional connectivity (FC) can be used to differentiate sex among individuals. Herein, we demonstrate that sex can be further classified with high accuracy using the intrinsic BOLD signal fluctuations from resting-state fMRI (rs-fMRI). We adopted the amplitude of low-frequency fluctuation (ALFF), and the fraction of ALFF (fALFF) features from the automated anatomical atlas (AAL) and Power's functional atlas as an input to different machine learning (ML) methods. Using datasets from five independently acquired subject cohorts and with eight fMRI scanning sessions, we comprehensively assessed unbiased performance using nested-cross validation for within-sample and across sample accuracies. The results demonstrated high prediction accuracies for the Human Connectome Project (HCP) dataset (area under cure (AUC) > 0.89). The yielded accuracies suggest that sex difference is embodied and well-pronounced in the low-frequency BOLD signal fluctuation. The performance degrades with the heterogeneity of the cohort and suggests that other factors,.e.g. psychiatric disorders and demographics influences the BOLD signal and may interact with the classification of sex. In addition, the results revealed high learning generalizability with the HCP scan, but not across different datasets. The intraclass correlation coefficient (ICC) across HCP scans showed moderate-to-good reliability based on atlas selection (ICC = 0.65 [0.63-0.67] and ICC= 0.78 [0.76-0.80].). We also assessed the effect of scan duration on the predictability of sex and showed that sex differences could be detected even with a short rs-fMRI scan (e.g., 2 minutes). Moreover, we provided statistical maps of the brain regions differentially recruited by or predicting sex using Shapely values and determined an overlap with previous reports of brain response due to sex differences. Altogether, our analysis suggests that sex differences are well-pronounced in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation.

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Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data

Cited by 64 publications

References 37 publications

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

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