Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis

Abstract

Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET.Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance.Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET.Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor

“…The default mode network has been generally linked to self-referential processing, affective cognition, and emotion regulation ( 26 ), and disturbances of the default mode network have been confirmed in many neurological and psychiatric disorders including ET ( 27 ). Using voxel-wise global brain connectivity mapping combined with MVPA, a study conducted by our team has shown that the major associated discriminative features in depressed ET were mainly located in the cerebellar-motor-prefrontal cortex circuits ( 12 ). Compared to the previous study, the present study was the first to combine local brain functional connectivity with the MVPA approach to identify depressed ET patients from non-depressed ET and HCs which may help deepen our understanding of the depression pathogenesis of ET from different perspectives.…”

“…Owing to multivariate properties, the MVPA can achieve greater sensitivity for discovering voxel-level subtle and spatially distributed changes of intrinsic brain activity, and are sensitive enough to perform classification at the single-subject level. Combining global brain connectivity of rs-fMRI with MVPA ( 12 ), our latest studies showed good classification performance to identify depressed ET patients. However, whether the MVPA based on local brain connectivity can realize automatic identification of depressed ET has not previously been investigated.…”

Abnormal Spontaneous Brain Activity and Cognitive Impairment in Obstructive Sleep Apnea

“…In recent years, a growing number of researchers have used multivariate pattern analysis of neuroimaging data to uncover patterns and assign individual observations to different categories. 36,37 To classify the patients in this study, we employed a multivariate pattern analysis approach noted as SVM. Here, we implement the SVM algorithm under pycharm based on the sklearn library in Python.…”

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

“…Moreover, ML-combined radiomics has shown to be a promising way to provide quantitative and objective supports for clinical diagnosis and prognosis and help to find a potential target for treatment, such as using ML algorithms based on diagnosis biomarkers from neuroimaging to identify ET from HCs to provide supporting evidence for clinically suspected ET diagnosis and guide the treatment of ET patients. Using ML algorithms combined with voxel-level local connectivity or frequency-dependent intrinsic brain activity analysis, our more recent studies revealed that these ML algorithms could achieve good classification performance to identify ET from healthy controls (HCs) (17)(18)(19). However, up to now, no studies have combined histogram analysis based on ALFF images of Rs-fMRI data with ML algorithms to identify ET patients.…”