Altered brain function in persistent postural perceptual dizziness: A study on resting state functional connectivity. Issue 8 (15th April 2018)

• Record Type: Journal Article
• Authors: Lee, Jin‐Ok; Lee, Eek‐Sung; Kim, Ji‐Soo; Lee, Young‐Beom; Jeong, Yong; Choi, Byung Se; Kim, Jae‐Hyoung; Staab, Jeffrey P.
• Is Part Of: Human brain mapping. Volume 39:Issue 8(2018)
• Page Start: 3340
• Page End: 3353
• Publication Date: 2018-04-15
• Subjects: functional connectivity -- persistent postural perceptual dizziness -- resting state functional imaging -- support vector machine -- vertigo; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24080
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: This study used resting state functional magnetic resonance imaging (rsfMRI) to investigate whole brain networks in patients with persistent postural perceptual dizziness (PPPD). We compared rsfMRI data from 38 patients with PPPD and 38 healthy controls using whole brain and region of interest analyses. We examined correlations among connectivity and clinical variables and tested the ability of a machine learning algorithm to classify subjects using rsfMRI results. Patients with PPPD showed: (a) increased connectivity of subcallosal cortex with left superior lateral occipital cortex and left middle frontal gyrus, (b) decreased connectivity of left hippocampus with bilateral central opercular cortices, left posterior opercular cortex, right insular cortex and cerebellum, and (c) decreased connectivity between right nucleus accumbens and anterior left temporal fusiform cortex. After controlling for anxiety and depression as covariates, patients with PPPD still showed decreased connectivity between left hippocampus and right inferior frontal gyrus, bilateral temporal lobes, bilateral insular cortices, bilateral central opercular cortex, left parietal opercular cortex, bilateral occipital lobes and cerebellum (bilateral lobules VI and V, and left I–IV). Dizziness handicap, anxiety, and depression correlated with connectivity in clinically meaningful brain regions. The machine learning algorithm correctly classified patients and controls with a sensitivity of 78.4%, specificity of 76.9%, and area under the curve = 0.88 using 11 connectivity parameters. Patients with PPPD showed reduced connectivity among the areas involved in multisensory vestibular processing and spatial cognition, but increased connectivity in networks linking visual and emotional processing. Connectivity patterns may become an imaging biomarker of PPPD.