Origins of atrophy in Parkinson linked to early onset and local transcription patterns

Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease

Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.

Mitochondrial function-associated genes underlie cortical atrophy in prodromal synucleinopathies

Isolated rapid eye movement sleep behaviour disorder (iRBD) is a sleep disorder characterized by the loss of rapid eye movement sleep muscle atonia and the appearance of abnormal movements and vocalizations during rapid eye movement sleep. It is a strong marker of incipient synucleinopathy such as dementia with Lewy bodies and Parkinson's disease. Patients with iRBD already show brain changes that are reminiscent of manifest synucleinopathies including brain atrophy. However, the mechanisms underlying the development of this atrophy remain poorly understood. In this study, we performed cutting-edge imaging transcriptomics and comprehensive spatial mapping analyses in a multicentric cohort of 171 polysomnography-confirmed iRBD patients [67.7 ± 6.6 (49-87) years; 83% men] and 238 healthy controls [66.6 ± 7.9 (41-88) years; 77% men] with T1-weighted MRI to investigate the gene expression and connectivity patterns associated with changes in cortical thickness and surface area in iRBD. Partial least squares regression was performed to identify the gene expression patterns underlying cortical changes in iRBD. Gene set enrichment analysis and virtual histology were then done to assess the biological processes, cellular components, human disease gene terms, and cell types enriched in these gene expression patterns. We then used structural and functional neighbourhood analyses to assess whether the atrophy patterns in iRBD were constrained by the brain's structural and functional connectome. Moreover, we used comprehensive spatial mapping analyses to assess the specific neurotransmitter systems, functional networks, cytoarchitectonic classes, and cognitive brain systems associated with cortical changes in iRBD. All comparisons were tested against null models that preserved spatial autocorrelation between brain regions and compared to Alzheimer's disease to assess the specificity of findings to synucleinopathies. We found that genes involved in mitochondrial function and macroautophagy were the strongest contributors to the cortical thinning occurring in iRBD. Moreover, we demonstrated that cortical thinning was constrained by the brain's structural and functional connectome and that it mapped onto specific networks involved in motor and planning functions. In contrast with cortical thickness, changes in cortical surface area were related to distinct genes, namely genes involved in the inflammatory response, and to different spatial mapping patterns. The gene expression and connectivity patterns associated with iRBD were all distinct from those observed in Alzheimer's disease. In summary, this study demonstrates that the development of brain atrophy in synucleinopathies is constrained by specific genes and networks.

Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis

With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.

Network-constrained technique to characterize pathology progression rate in Alzheimer's disease

Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer’s disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient’s longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer’s Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject’s degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.

Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis

Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.

Multimodal phenotypic axes of Parkinson's disease

Individuals with Parkinson’s 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 Parkinson’s disease from the Parkinson’s 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 the 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.