Biology subjects

Postuma, R. B.

Publications and source records attributed to Postuma, R. B..

3 recordsLinked to original sources

bioRxiv2017

White Matter Hyperintensities and Cognitive Decline in de Novo Parkinson’s Disease Patients

ObjectiveWhite Matter Hyperintensities (WMHs) are associated with cognitive decline in normative aging and Alzheimers disease. However, the pathogenesis of cognitive decline in Parkinsons disease (PD) is not directly related to vascular causes, and therefore the role of WMHs in PD remains unclear. If WMH has a higher impact on cognitive decline in PD, vascular pathology should be assessed and treated with a higher priority in this population. Here we investigate whether WMH leads to increased cognitive decline in PD, and if these effects relate to cortical thinning\n\nMethodsTo investigate the role of WMHs in PD, it is essential to study recently-diagnosed/non-treated patients. De novo PD patients and age-matched controls (NPD=365,NControl=174) with FLAIR/T2-weighted scans at baseline were selected from Parkinsons Progression Markers Initiative (PPMI). WMHs and cortical thickness were measured to analyse the relationship between baseline WMHs and future cognitive decline (follow-up:4.09{+/-}1.14 years) and cortical thinning (follow-up:1.05{+/-}0.10 years).\n\nResultsHigh WMH load (WMHL) at baseline in PD was associated with increased cognitive decline, significantly more than i) PDs with low WMHL and ii) controls with high WMHL. Furthermore, PD patients with higher baseline WMHL showed more cortical thinning in right frontal lobe than subjects with low WMHL. Cortical thinning of this region also predicted decline in performance on a cognitive test.\n\nInterpretationPresence of WMHs in de novo PD patients predicts greater future cognitive decline and cortical thinning than in normal aging. Recognizing WMHs as a potential predictor of cognitive deficit in PD provides an opportunity for timely interventions.

neuroscience↗

bioRxiv2017

Algorithmic complexity of EEG as a prognosis biomarker of neurodegeneration in idiopathic rapid eye movement behavior disorder (RBD)

ObjectiveIdiopathic REM sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinsons disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis.\n\nMethodsWe analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from[~] 5 minute recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression (LZW).\n\nResultsComplexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels.\n\nConclusionsPoor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude coupling.\n\nSignificanceAlgorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.\n\nIndex TermsBiomarkers, EEG, LZW, PD, LBD

neuroscience↗

bioRxiv2017

A clinical-anatomical signature of Parkinson’s Disease identified with partial least squares and magnetic resonance imaging

Parkinsons disease (PD) is a neurodegenerative disorder characterized by a wide array of motor and non-motor symptoms. It remains unclear whether neurodegeneration in discrete loci gives rise to discrete symptoms, or whether network-wide atrophy gives rise to the unique behavioural and clinical profile associated with PD. Here we apply a data-driven strategy to isolate large-scale, multivariate associations between distributed atrophy patterns and clinical phenotypes in PD. In a sample of N = 229 de novo PD patients, we estimate disease-related atrophy using deformation based morphometry (DBM) of T1 weighted MR images. Using partial least squares (PLS), we identify a network of subcortical and cortical regions whose collective atrophy is associated with a clinical phenotype encompassing motor and non-motor features. Despite the relatively early stage of the disease in the sample, the atrophy pattern encompassed lower brainstem, substantia nigra, basal ganglia and cortical areas, consistent with the Braak hypothesis. In addition, individual variation in this putative atrophy network predicted longitudinal clinical progression in both motor and non-motor symptoms. Altogether, these results demonstrate a pleiotropic mapping between neurodegeneration and the clinical manifestations of PD, and that this mapping can be detected even in de novo patients.

neuroscience↗