Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI Study

Abstract

Background: Dysfunctional integration of distributed brain networks is believed to be the cause of schizophrenia, and resting-state functional connectivity analyses of schizophrenia have attracted considerable attention in recent years. Unfortunately, existing functional connectivity analyses of schizophrenia have been mostly limited to linear associations.Objective: The objective of the present study is to evaluate the discriminative power of non-linear functional connectivity and identify its changes in schizophrenia.Method: A novel measure utilizing the extended maximal information coefficient was introduced to construct non-linear functional connectivity. In conjunction with multivariate pattern analysis, the new functional connectivity successfully discriminated schizophrenic patients from healthy controls with relative higher accuracy rate than the linear measure.Result: We found that the strength of the identified non-linear functional connections involved in the classification increased in patients with schizophrenia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain.Conclusion: The classification results suggest that the non-linear functional connectivity provided useful discriminative power in diagnosis of schizophrenia, and the inverse but similar spatial distributed changes between the non-linear and linear measure may indicate the underlying compensatory mechanism and the complex neuronal synchronization underlying the symptom of schizophrenia.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

“…Recently, machine learning, which is a type of multivariate analysis that can automatically discriminate individuals within a sample group, was used to classify several neuropsychiatric disorders and addictions, such as SZ, [19–24] Alzheimer's disease , [25–28] depression, [29–31] attention-deficit hyperactivity disorder, [32–34] and smoking. [35] …”

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

“…For example, previous studies showed the use of functional connectivity-based features for classification of schizophrenia and bipolar patients at the individual level (Arbabshirani et al, 2013b; Shen et al, 2010; Su et al, 2013). Shen et al (2010) used an atlas-based method to extract mean time-courses of 116 brain regions in the resting-state for both healthy controls and schizophrenia subjects.…”

Classification of Cognitive Level of Patients with Leukoaraiosis on the Basis of Linear and Non-Linear Functional Connectivity

“…However, non-linear FC has been shown to be more effective to study interactions between brain regions, because the brain is a complex system, and non-linear relationships between the activity of different brain regions is to be expected (34, 64, 65). eMIC, which has been used to evaluate non-linear relationships between two brain regions, captures subtle changes in FC and uses more discriminative information for classification (36). The results of the present study indicate that for the analysis of different levels of cognitive impairment in LA, eMIC-based FC (capturing non-linear dependencies) provides more information and is more effective than other measures.…”

“…eMIC has been employed to estimate non-linear FC in a non-linear connectivity analysis of schizophrenia. This previous study has shown that non-linear FC had discriminative power in the diagnosis of schizophrenia (36). The authors suggested that non-linear FC might provide crucial information for disease identification.…”