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Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP , confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a progressive neurodegenerative disorder characterized by the intracellular accumulation of insoluble alpha-synuclein aggregates into Lewy bodies and neurites. Increasing evidence indicates that Parkinson's disease progression results from the spread of pathologic alpha-synuclein through neuronal networks. However, the exact mechanisms underlying the propagation of abnormal proteins in the brain are only partially understood. The objective of this study was first to describe the long-term spatiotemporal distributions of Lewy-related pathology in mice injected with alpha-synuclein preformed fibrils and then to recreate these patterns using a computational model that simulates in silico the spread of pathologic alpha-synuclein. In this study, 87 2-3-month-old non-transgenic mice were injected with alpha-synuclein preformed fibrils to generate a comprehensive post-mortem dataset representing the long-term spatiotemporal distributions of hyperphosphorylated alpha-synuclein, an established marker of Lewy pathology, across the 426 regions of the Allen Mouse Brain Atlas. The mice were injected into either the caudoputamen, nucleus accumbens or hippocampus, and followed over 24 months with pathologic alpha-synuclein quantified at seven intermediate time points. The pathologic patterns observed at each time point in this high-resolution dataset were then compared to those generated using a Susceptible-Infected-Removed (SIR) computational model, an agent-based model that simulates the spread of pathologic alpha-synuclein for every brain region taking simultaneously into account the effect of regional brain connectivity and Snca gene expression. Our histopathological findings showed that differentially targeted seeding of pathological alpha-synuclein resulted in unique propagation patterns over 24 months and that most brain regions were permissive to pathology. We found that the SIR model recreated the observed distributions of pathology over 24 months for each injection site. Null models showed that both Snca gene expression and connectivity had a significant influence on model fit. In sum, our study demonstrates that the combination of normal alpha-synuclein concentration and brain connectomics contributes to making brain regions more vulnerable to the pathological process, providing support for a prion-like spread of pathologic alpha-synuclein. We propose that this rich dataset and the related computational model will help test new hypotheses regarding mechanisms that may alter the spread of pathologic alpha-synuclein in the brain. [ABSTRACT FROM AUTHOR]

Parkinson's disease varies in severity and age of onset. One source of this variability is sex. Males are twice as likely as females to develop Parkinson's disease, and tend to have more severe symptoms and greater speed of progression. However, to date, there is little information in large cohorts on sex differences in the patterns of neurodegeneration. Here we used MRI and clinical information from the Parkinson Progression Markers Initiative to measure structural brain differences between sexes in Parkinson's disease after regressing out the expected effect of age and sex. We derived atrophy maps from deformation-based morphometry of T1-weighted MRI and connectivity from diffusion-weighted MRI in de novo Parkinson's disease patients (149 males: 83 females) with comparable clinical severity, and healthy control participants (78 males: 39 females). Overall, even though the two patient groups were matched for disease duration and severity, males demonstrated generally greater brain atrophy and disrupted connectivity. Males with Parkinson's disease had significantly greater tissue loss than females in 11 cortical regions including bilateral frontal and left insular lobe, right postcentral gyrus, left inferior temporal and cingulate gyrus and left thalamus, while females had greater atrophy in six cortical regions, including regions in the left frontal lobe, right parietal lobe, left insular gyrus and right occipital cortex. Local efficiency of white matter connectivity showed greater disruption in males in multiple regions such as basal ganglia, hippocampus, amygdala and thalamus. These findings support the idea that development of Parkinson's disease may involve different pathological mechanisms and yield distinct prognosis in males and females, which may have implications for research into neuroprotection, and stratification for clinical trials. [ABSTRACT FROM AUTHOR]

Parkinson's disease varies widely in clinical manifestations, course of progression and biomarker profiles from person to person. Identification of distinct Parkinson's disease subtypes is of great priority to illuminate underlying pathophysiology, predict progression and develop more efficient personalized care approaches. There is currently no clear way to define and divide subtypes in Parkinson's disease. Using data from the Parkinson's Progression Markers Initiative, we aimed to identify distinct subgroups via cluster analysis of a comprehensive dataset at baseline (i.e. cross-sectionally) consisting of clinical characteristics, neuroimaging, biospecimen and genetic information, then to develop criteria to assign patients to a Parkinson's disease subtype. Four hundred and twenty-one individuals with de novo early Parkinson's disease were included from this prospective longitudinal multicentre cohort. Hierarchical cluster analysis was performed using data on demographic and genetic information, motor symptoms and signs, neuropsychological testing and other non-motor manifestations. The key classifiers in cluster analysis were a motor summary score and three non-motor features (cognitive impairment, rapid eye movement sleep behaviour disorder and dysautonomia). We then defined three distinct subtypes of Parkinson's disease patients: 223 patients were classified as 'mild motor-predominant' (defined as composite motor and all three non-motor scores below the 75th percentile), 52 as 'diffuse malignant' (composite motor score plus either ≥1/3 non-motor score >75th percentile, or all three non-motor scores >75th percentile) and 146 as 'intermediate'. On biomarkers, people with diffuse malignant Parkinson's disease had the lowest level of cerebrospinal fluid amyloid-β (329.0 ± 96.7 pg/ml, P = 0.006) and amyloid-β/total-tau ratio (8.2 ± 3.0, P = 0.032). Data from deformation-based magnetic resonance imaging morphometry demonstrated a Parkinson's disease-specific brain network had more atrophy in the diffuse malignant subtype, with the mild motor-predominant subtype having the least atrophy. Although disease duration at initial visit and follow-up time were similar between subtypes, patients with diffuse malignant Parkinson's disease progressed faster in overall prognosis (global composite outcome), with greater decline in cognition and in dopamine functional neuroimaging after an average of 2.7 years. In conclusion, we introduce new clinical criteria for subtyping Parkinson's disease based on a comprehensive list of clinical manifestations and biomarkers. This clinical subtyping can now be applied to individual patients for use in clinical practice using baseline clinical information. Even though all participants had a recent diagnosis of Parkinson's disease, patients with the diffuse malignant subtype already demonstrated a more profound dopaminergic deficit, increased atrophy in Parkinson's disease brain networks, a more Alzheimer's disease-like cerebrospinal fluid profile and faster progression of motor and cognitive deficits. [ABSTRACT FROM AUTHOR]

White matter tracts, which play a crucial role in the coordination of information flow between different regions of grey matter, are particularly vulnerable to multiple sclerosis. Many studies have shown that the white matter lesions in multiple sclerosis are associated with focal abnormalities of grey matter, but little is known about the alterations in the coordinated patterns of cortical morphology among regions in the disease. Here, we used cortical thickness measurements from structural magnetic resonance imaging to investigate the relationship between the white matter lesion load and the topological efficiency of structural cortical networks in multiple sclerosis. Network efficiency was defined using a ‘small-world’ network model that quantifies the effectiveness of information transfer within brain networks. In this study, we first classified patients (n = 330) into six subgroups according to their total white matter lesion loads, and identified structural brain networks for each multiple sclerosis group by thresholding the corresponding inter-regional cortical thickness correlation matrix, followed by a network efficiency analysis with graph theoretical approaches. The structural cortical networks in multiple sclerosis demonstrated efficient small-world architecture regardless of the lesion load, an organization that maximizes the information processing at a relatively low wiring cost. However, we found that the overall small-world network efficiency in multiple sclerosis was significantly disrupted in a manner proportional to the extent of total white matter lesions. Moreover, regional efficiency was also significantly decreased in specific brain regions, including the insula and precentral gyrus as well as regions of prefrontal and temporal association cortices. Finally, we showed that the lesions also altered many cortical thickness correlations in the frontal, temporal and parietal lobes. Our results suggest that the white matter lesions in multiple sclerosis might be associated with aberrant neuronal connectivity among widely distributed brain regions, and provide structural (morphological) evidence for the notion of multiple sclerosis as a disconnection syndrome. [ABSTRACT FROM PUBLISHER]