Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity

Abstract

Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders.

Joint prediction of multiple scores captures better individual traits from brain images

“…Learning the diagnosis jointly with related quantitative clinical scores yields more accurate and more stable prediction of schizophrenia status. Overall, these results confirm previous successes predicting schizophrenia from rsfMRI (Savio and Graña, 2015;Rashid et al, 2016). …”

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

“…6 less than). FNC has also been widely used to identify group differences or even individual subject classification, 30–32 and differences in FNC can profitably be analyzed for associations with symptoms or quantitative characteristics.…”

“…They have already shown improved sensitivity to identifying group differences, brain arousal state, or diagnostic classifications from resting-state fMR imaging data. 30,62 An example of FNC states estimated from resting fMR imaging collected concurrently with electroencephalogram (EEG) data is shown in Fig. 12.…”

“…The states are ordered according to EEG measures of drowsiness and it is apparent that the connectivity patterns are affected by the arousal state (in particular, anticorrelated connectivity, indicated in blue, diminishes with increasing drowsiness). 75,76 In terms of group differences, recent work using ICA has shown that patients schizophrenia tend to spend more time occupying a weakly connected dynamic state versus controls 62 and also that dynamic FNC estimates seem to be more sensitive for classification of bipolar patients and patients with schizophrenia 30 than static measures.…”

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

“…Investigators have pursued DFC methods in a wide variety of brain disorders, including schizophrenia 38,131,132133134 , bipolar disorder 131 , autism spectrum disorder 135,136 , major depression, mild cognitive impairment 137138 , Alzheimer’s disease 139 and dementia with Lewy bodies 95 , post-traumatic stress disorder 78 , epilepsy 61,140 and multiple sclerosis, among others 79 . Encouragingly, these studies have begun to suggest that dynamic features are more sensitive or specific than static connectivity in differentiating between healthy and control populations.…”