Resting‐state Functional MRI in Parkinsonian Syndromes

Abstract

Background Background: Functional MRI (fMRI) has been widely used to study abnormal patterns of functional connectivity at rest in patients with movement disorders such as idiopathic Parkinson's disease (PD) and atypical parkinsonisms. Methods Methods: This manuscript provides an educational review of the current use of resting-state fMRI in the field of parkinsonian syndromes. ResultsResults: Resting-state fMRI studies have improved the current knowledge about the mechanisms underlying motor and non-motor symptom development and progression in movement disorders. Even if its inclusion in clinical practice is still far away, resting-state fMRI has the potential to be a promising biomarker for early disease detection and prediction. It may also aid in differential diagnosis and monitoring brain responses to therapeutic agents and neurorehabilitation strategies in different movement disorders. ConclusionsConclusions: There is urgent need to identify and validate prodromal biomarkers in PD patients, to perform further studies assessing both overlapping and disease-specific fMRI abnormalities among parkinsonian syndromes, and to continue technical advances to fully realize the potential of fMRI as a tool to monitor the efficacy of chronic therapies.

Functional connectivity studies in migraine: what have we learned?

“…As it is clear from Additional file 1 the findings are scattered and show very little overlap (Additional file 1). Moreover, none of the reported FC changes may be specific for migraine as other studies reported similar or exact same network changes in several other conditions, including fibromyalgia [33], Parkinsonian syndromes [34, 35] altered consciousness states [36], systemic lupus [37] and chronic hepatitis C virus infection [38]. Thus, it can be suspected that this FC method is at all not reproducible, which may be due to lack of sensitivity and specificity.…”

Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings

“…Parkinson’s disease (PD) is the second most common neurodegenerative disorder and is characterized by dopamine depletion in the nigro-striatal system leading to progressive functional impairment (Poewe et al, 2017). Widespread functional rearrangements related to the development of motor and non-motor symptoms occur over the clinical progression in PD patients (Filippi et al, 2019). Several RS fMRI studies have identified alterations of the cerebello-thalamo-cortical circuit as a key hallmark of PD (Helmich et al, 2010; Hacker et al, 2012; Agosta et al, 2014; Akram et al, 2017), with reduced activation of the posterior putamen correlating with motor impairment as the most consistent finding (Herz et al, 2014).…”

“…Functional connectivity (FC) quantifies the temporal correlation of functional activation in different brain regions and can be expressed in terms of pairwise Pearson’s correlation coefficients, covariance, or mutual information between time series, revealing specific networks (Smitha et al, 2017). FC has been recognized as an important biomarker for better understanding the pathophysiological mechanisms of numerous neurodegenerative diseases, including Alzheimer’s disease (AD) (Filippi et al, 2017), Parkinson’s disease (PD) (Baggio et al, 2015; Filippi et al, 2019), and frontotemporal dementia (FTD) (Filippi et al, 2017).…”

Resting state connectivity within the basal ganglia and gait speed in older adults with cerebral small vessel disease and locomotor risk factors

“…Higher co-activation at rest among regions is interpreted as a measure of higher intrinsic connectivity or cohesiveness between those regions. Initial evidence suggests that higher connectivity between basal ganglia and other regions predicts better motor performance in patients with Parkinsonian Syndromes ( Filippi et al, 2019 ) and in healthy adults ( Boyne et al, 2018 , Zwergal et al, 2012 ). However, prior studies have not accounted for the contribution of impairment of other age-related factors influencing locomotion including muscle strength ( McLean et al, 2014 ), vision ( Chaudhry et al, 2010 ), joint pain ( White et al, 2013 ), and obesity ( Vincent et al, 2010 ) as well as WMH and brain volume ( Rosso et al, 2017 ).…”