Biology subjects

Postuma, R. B.

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

6 recordsLinked to original sources

bioRxiv2025

T cell responses towards PINK1 and α-synuclein are elevated in prodromal Parkinson's disease

A role of the immune system in Parkinsons disease (PD) progression has long been suspected due to the increased frequency of activated glial cells and infiltrating T cells into the substantia nigra. It was previously reported that PD donors have increased T cell responses towards PINK1 and -synuclein (-syn), two Lewy body-associated proteins. Further, T cell reactivity towards -syn was highest closer to disease onset, highlighting that autoreactive T cells might play a role in PD pathogenesis. However, whether T cell autoreactivity is present during prodromal PD is unknown. Here, we investigated T cell responses towards PINK1 and -syn in donors at high risk of developing PD (i.e. prodromal PD: genetic risk, hyposmia, and or REM sleep behavior disorder), in comparison to PD and healthy control donors. T cell reactivity to these two autoantigens was detected in prodromal PD at levels comparable to those detected in individuals with clinically diagnosed PD. Aligned with the increased incidence of PD in males, we found that males with PD, but not females, had elevated T cell reactivity compared to healthy controls. However, among prodromal PD donors, males and females had elevated T cell responses. These differing trends in reactivity highlights the need for further studies of the impact of biological sex on neuroinflammation and PD progression.

neuroscience↗

bioRxiv2020

Integrated morphometric, molecular, and clinical characterization of Parkinson's disease pathology

Individuals with Parkinsons disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the "average" patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method--similarity network fusion--to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinsons disease from the Parkinsons Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.

neuroscience↗

bioRxiv2019

An MRI Atrophy Biomarker Predicts Global Prognosis in Early De Novo Parkinson's Disease

BackgroundCommonly used neuroimaging biomarkers in Parkinsons disease (PD) are useful for diagnosis but poor at predicting outcomes. We explored whether an atrophy pattern from whole-brain structural MRI, measured in the drug-naive early stage, could predict PD prognosis. Methods362 de novo PD patients with T1-weighted MRI (n=222 for the main analysis, 140 for the validation analysis) were recruited from the Parkinsons Progression Markers Initiative (PPMI). We investigated a previously identified PD-specific network atrophy pattern as a potential biomarker of disease severity and prognosis. Progression trajectories of motor function (MDS-UPDRS-part III), cognition (Montreal Cognitive Assessment (MoCA)), and a global composite outcome measure were compared between atrophy tertiles using mixed effect models. The prognostic value of the MRI atrophy measure was compared with 123I ioflupane single photon emission computed tomography, the postural-instability-gait-disturbance score, and cerebrospinal fluid markers. FindingsAfter 4.5 years follow-up, PD-specific atrophy network score at baseline significantly predicted change in UPDRS-part III (r=-0.197, p=0.003), MoCA (r=0.253, p=0.0002) and global composite outcome (r=-0.249, p=0.0002). Compared with the 3rd tertile (i.e. least atrophy), the tertile with the highest baseline atrophy (i.e. the 1st tertile) had a 3-point annual faster progression in UPDRS-part III (p=0.012), faster worsening of posture-instability gait scores (+0.21 further annual increase, p<0.0001), faster decline in MoCA (-0.74 further annual decline in MoCA, p=0.0372) and a +0.38 (p=0.0029) faster annual increase in the global composite z-score. All findings were replicated in a validation analysis using 1.5T MRI. By comparison, the other biomarkers were limited in their ability to predict prognosis either in the main or validation analysis. InterpretationA PD-specific network atrophy pattern predicts progression of motor, cognitive, and global outcome in PD, and is a stronger predictor of prognosis than any of the other tested biomarkers. Therefore, it has considerable potential as a prognostic biomarker for clinical trials of early PD.

neuroscience↗

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↗