Interhemispheric Resting-State Functional Connectivity Predicts Severity of Idiopathic Normal Pressure Hydrocephalus

Idiopathic normal pressure hydrocephalus (iNPH) is characterized by a clinical triad (gait disturbance, dementia, and urinary incontinence), and by radiological findings of enlarged ventricles reflecting disturbance of central spinal fluid circulation. A diagnosis of iNPH is sometimes challenging, and the pathophysiological mechanisms underlying the clinical symptoms of iNPH remain largely unknown. Here, we used an emerging MRI technique, resting-state functional connectivity MRI (rsfcMRI), to develop a subsidiary diagnostic technique and to explore the underlying pathophysiological mechanisms of iNPH. rsfcMRI data were obtained from 11 patients with iNPH and 11 age-matched healthy volunteers, yielding rsfcMRI-derived functional connectivity (FC) from both groups. A linear support vector machine classifier was trained to distinguish the patterns of FCs of the patients with iNPH from those of the healthy volunteers. After dimensional reduction, the support vector machine successfully classified the two groups with an accuracy of 80%. Moreover, we found that rsfcMRI-derived FC carried information to predict the severity of the triad in iNPH. FCs relevant to the classification of severity were mainly based on interhemispheric connectivity, suggesting that disruption of the corpus callosum fibers due to ventricular enlargement may explain the triad of iNPH. The present results support the usefulness of rsfcMRI as a tool to understand pathophysiology of iNPH, and also to help with its clinical diagnosis.