Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

Abstract

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Fig. 1. Data-driven biomarker trajectory inference and staging.

A Neuropathological staging systems, such as the Braak stages for AD, represent the earliest attempts to identify characteristic pathophysiological progression patterns [61]. The accumulation of neurofibrillary tangles begins in transentorhinal regions (Stages I and II) and propagates along a stereotypical pattern to limbic (Stages III and IV) and neocortical (Stages V and VI) regions. The figure has been adapted with permission from [332]. B Using in vivo (imaging, fluid, and clinical) biomarkers from large observational studies, continuous-time disease progression models attempt to stitch together data points from many subjects to infer population trajectories along a latent disease time. With minimal a priori assumptions, these methods must account for inter-subject variability in disease onset and progression rate, as well as the potential existence of sub-populations with distinct trajectories. C Event-based modeling is another approach to characterizing biomarker alterations over disease progression. (Left) This method does not explicitly model the trajectory along a latent temporal variable, but instead identifies the most likely sequences of biomarker alterations, along with their uncertainty represented by the gray elements in this positional variance diagram. These markers can be any combination of features from different brain regions and modalities. (Right) Event-based modeling is the basis for simultaneous Subtyping and Stage Inference (SuStaIn), a method that identifies sub-populations with varying event sequences. For example, SuStaIn identified 3 subtypes of AD atrophy progression, corresponding to typical, cortical-dominant, and subcortical patterns. The figure was originally published in [132], is covered by the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) and has been adapted to show only 4 disease stages.

Fig. 2. Mechanistic models of pathophysiological interactions and network propagation.

Dynamical systems-based models are used to explicitly represent intra-region interactions between different physiological systems, and inter-region propagation of pathophysiology. A Dynamical systems models impose causal structure on the relationships between variables. They can be used to simulate the spatiotemporal evolution of brain dynamics, and to determine optimal therapeutic inputs. The figure on the right has been adapted from [263] and is under the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. B Network models of connectome-driven pathophysiology propagation. These models consider the spatiotemporal propagation of disease factors, such as misfolded proteins, from regional epicenters along brain networks (e.g., structural, functional, or vascular connectomes). C A network diffusion model noted the resemblance between eigenmodes of the structural connectome graph Laplacian and the disease-specific atrophy patterns observed in healthy ageing, AD and bvFTD. Figures have been adapted with permission from [168]. D An epidemic spreading model (ESM) frames proteinopathy dynamics in terms of regional production, clearance, misfolding and propagation of misfolded proteins, and replicates spatial progression patterns observed from PET imaging. The figure has been adapted with permission from [174]. E Although no approved α-synuclein PET tracer exists at the time of writing, this epidemiological model of neurotoxic protein propagation and subsequent atrophy in PD patients replicated empirical atrophy patterns. The figure has been adapted with permission from ref. [182].

Fig. 3. Linking cellular and molecular architecture with large-scale brain alterations.

A Imaging transcriptomics analyses typically use spatial correlations to identify cellular and molecular features co-localized with imaging alterations. B Dynamical systems models can explicitly incorporate properties such as a neurochemical architecture as a mediator of imaging-measured physiological interactions. The figure has been adapted with permission from [237]. C Biophysically constrained models consider cellular, mesoscale circuit, and macroscopic network effects at the appropriate scale. These models can incorporate synaptic mechanisms such as amyloid- and tau-mediated hyperexcitability [275] and serotonergic receptor-mediated gain modulation [260], and simulate their consequences on large-scale neuronal activity. The figure has been adapted with permission from [260].

Fig. 4. Using multi-factorial computational models to improve treatment selection and test mechanistic hypotheses.

Computational models of spatiotemporal pathophysiology progression can go beyond correlative analysis and infer disease-altered mechanisms. A Integrative in silico modeling of the progression of multiple biomarkers can be used to predict future disease progression and infer optimal therapeutic interventions at an individualized level [156]. B The role of the molecular architecture of the brain in various disease-affected alterations is an open question. Molecular pathways enriched in disease-affected tissue (e.g., where amyloid and tau accumulation alters functional activity) can be used to identify potential therapeutic targets [306]. C Computational modeling can benefit from diverse data sources, incorporating population-derived distributions of disease onset age with in vivo biomarker data [78], using homologous structures in other species to inform directed network propagation models [170], and validating model predictions using invasive experiments in animal models [312].