Personalizing functional Magnetic Resonance Protocols for Studying Neural Substrates of Motor Deficits in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder characterized by a large number of motor and non-motor deficits, which significantly contribute to reduced quality of life. Despite the definition of the broad spectrum of clinical characteristics, mechanisms triggering illness, the nature of its progression and a character of therapeutic effects still remain unknown. The enormous advances in magnetic resonance imaging (MRI) in the last decades have significantly affected the research attempts to uncover the functional and structural abnormalities in PD and have helped to develop and monitor various treatment strategies, of which dopamine replacement strategies, mainly in form of levodopa, has been the gold standard since the late seventies and eighties. Motor, task-related functional MRI (fMRI) has been extensively used to assess the pathological state of the motor circuitry in PD. Several studies employed motor paradigms and fMRI to review the functional brain responses of participants to levodopa treatment. Interestingly, they provided conflicting results. Wide spectrum of symptoms, variability and asymmetry of the disease presentation, several treatment approaches and their divergent outcomes make PD enormously heterogeneous. In this work we hypothesized that not considering the disease heterogeneity might have been an adequate cause for the discrepant results in aforementioned studies. We show that not accounting for the disease variability might indeed compromise the results and invalidate the consequent interpretations. Accordingly, we propose and formalize a statistical approach to account for the intra and inter subject variability. This might help to minimize this bias in future motor fMRI studies revealing the functional brain dysfunction and contribute to the understanding of still unknown pathophysiological mechanisms underlying PD

Modulatory effects of levodopa on cerebellar connectivity in Parkinson’s disease

Levodopa has been the mainstay of symptomatic therapy for Parkinson’s disease (PD) for the last five decades. However, it is associated with the development of motor fluctuations and dyskinesia, in particular after several years of treatment. The aim of this study was to shed light on the acute brain functional reorganization in response to a single levodopa dose. Functional magnetic resonance imaging (fMRI) was performed after an overnight withdrawal of dopaminergic treatment and 1 h after a single dose of 250 mg levodopa in a group of 24 PD patients. Eigenvector centrality was calculated in both treatment states using resting-state fMRI. This offers a new data-driven and parameter-free approach, similar to Google’s PageRank algorithm, revealing brain connectivity alterations due to the effect of levodopa treatment. In all PD patients, levodopa treatment led to an improvement of clinical symptoms as measured with the Unified Parkinson’s Disease Rating Scale motor score (UPDRS-III). This therapeutic effect was accompanied with a major connectivity increase between cerebellar brain regions and subcortical areas of the motor system such as the thalamus, putamen, globus pallidus, and brainstem. The degree of interconnectedness of cerebellar regions correlated with the improvement of clinical symptoms due to the administration of levodopa. We observed significant functional cerebellar connectivity reorganization immediately after a single levodopa dose in PD patients. Enhanced general connectivity (eigenvector centrality) was associated with better motor performance as assessed by UPDRS-III score. This underlines the importance of considering cerebellar networks as therapeutic targets in PD

Different brain areas require different analysis models: fMRI observations in Parkinson’s disease

Foreseeing how specific brain areas respond in time to a stimulus can be a prerequisite for a successfully conceived fMRI experiment. We demonstrate that in medicated Parkinson’s disease patients, putamen's activation peaks around the onset of tapping but does not persist throughout the tapping block, whereas sustained activation is observed in the motor cortex. Consequently, in the widely used tapping paradigm “On vs. Off L-DOPA”, the drug effect remains undetected if statistical analysis apply a block design instead of an event-related one. Ignoring this information can lead to fallacious conclusions which suggests using different models to investigate different brain regions

Improving brain imaging in Parkinson's disease by accounting for simultaneous motor output

Parkinson's disease leads to a variety of movement impairments. While studying the disease with fMRI, the main motivation for the research becomes one of its major obstacles: the motor output is unpredictable. Therefore it is troublesome to access, inside the scanner, performances of motor tasks and reliably relate them to brain measurements. We proposed to overcome this by expanding the patients’ number and restricting statistical criteria from a previous study which used a glove with non-magnetic sensors during scanning. Our results revealed basal ganglia not observed in the previous study confirming the usefulness of the device in fMRI studies

Cerebral blood flow predicts differential neurotransmitter activity

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans

Improving fMRI in Parkinson's disease by accounting for realistic motor output

In Parkinson's disease (PD), the motor loop functioning and the patients’ motor output are unpredictable, due to brain compensatory mechanisms initiated up to decades before diagnosis. Consequently, the accuracy of motor tasks during fMRI is impeded, and deviations of the movement performance affect results. Kinematic modeling based on simultaneous measurements with MR-compatible gloves has been previously proposed as means to address this problem and outperform conventional boxcar modeling (Standard). Here, we adopted this approach in a larger cohort along with conservative statistics employing family-wise error (FWE) correction at the voxel level (p< 0.05) to be less prone to produce false positives

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

BACKGROUND: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32&nbsp;Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ &gt; 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS: This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects