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.

Network model of pathology spread recapitulates neurodegeneration and selective vulnerability in Huntington's Disease.

Huntington's Disease (HD), an autosomal dominant genetic disorder caused by a mutation in the Huntingtin gene (HTT), displays a stereotyped topography in the human brain and a stereotyped progression, initially appearing in the striatum. Like other degenerative diseases, spatial topography of HD is divorced from where implicated genes are expressed, a dissociation whose mechanistic underpinning is not currently understood. Cell autonomous molecular factors characterized by gene expression signatures, including proteolytic and post translational modifications, play a role in vulnerability to disease. Non-autonomous mechanisms, likely involving the brain's anatomic or functional connectivity patterns, might also be responsible for selective vulnerability in HD. Leveraging a large dataset of 635 subjects from a multinational study, this paper tests various cell-autonomous and non-autonomous models that can explain HD topography. We test whether the expression patterns of implicated genes is sufficient to explain regional HD atrophy, or whether the network transmission of protein products is required to explain them. We find that network models are capable of predicting, to a high degree, observed atrophy in human subjects. Lastly, we propose a model of anterograde network transmission, and show that it is the most parsimonious yet most likely to explain observed atrophy patterns in HD. Collectively, these data indicate that pathology spread in HD may be mediated by the brain's intrinsic structural network organization. This is the first study to systematically and quantitatively test multiple hypotheses of pathology spread in living human subjects with HD.

Models of Network Spread and Network Degeneration in Brain Disorders.

Network analysis can provide insight into key organizational principles of brain structure and help identify structural changes associated with brain disease. Though static differences between diseased and healthy networks are well characterized, the study of network dynamics, or how brain networks change over time, is increasingly central to understanding ongoing brain changes throughout disease. Accordingly, we present a short review of network models of spread, network dynamics, and network degeneration. Borrowing from recent suggestions, we divide this review into two processes by which brain networks can change: dynamics on networks, which are functional and pathological consequences taking place atop a static structural brain network; and dynamics of networks, which constitutes a changing structural brain network. We focus on diffusion magnetic resonance imaging-based structural or anatomic connectivity graphs. We address psychiatric disorders like schizophrenia; developmental disorders like epilepsy; stroke; and Alzheimer's disease and other neurodegenerative diseases.

Preserved Structural Network Organization Mediates Pathology Spread in Alzheimer's Disease Spectrum Despite Loss of White Matter Tract Integrity.

Models of Alzheimer's disease (AD) hypothesize stereotyped progression via white matter (WM) fiber connections, most likely via trans-synaptic transmission of toxic proteins along neuronal pathways. An important question in the field is whether and how organization of fiber pathways is affected by disease. It remains unknown whether fibers act as conduits of degenerative pathologies, or if they also degenerate with the gray matter network. This work uses graph theoretic modeling in a longitudinal design to investigate the impact of WM network organization on AD pathology spread. We hypothesize if altered WM network organization mediates disease progression, then a previously published network diffusion model will yield higher prediction accuracy using subject-specific connectomes in place of a healthy template connectome. Neuroimaging data in 124 subjects from ADNI were assessed. Graph topology metrics show preserved network organization in patients compared to controls. Using a published diffusion model, we further probe the effect of network alterations on degeneration spread in AD. We show that choice of connectome does not significantly impact the model's predictive ability. These results suggest that, despite measurable changes in integrity of specific fiber tracts, WM network organization in AD is preserved. Further, there is no difference in the mediation of putative pathology spread between healthy and AD-impaired networks. This conclusion is somewhat at variance with previous results, which report global topological disturbances in AD. Our data indicates the combined effect of edge thresholding, binarization, and inclusion of subcortical regions to network graphs may be responsible for previously reported effects.

A method for inferring regional origins of neurodegeneration.

Alzheimer's disease, the most common form of dementia, is characterized by the emergence and spread of senile plaques and neurofibrillary tangles, causing widespread neurodegeneration. Though the progression of Alzheimer's disease is considered to be stereotyped, the significant variability within clinical populations obscures this interpretation on the individual level. Of particular clinical importance is understanding where exactly pathology, e.g. tau, emerges in each patient and how the incipient atrophy pattern relates to future spread of disease. Here we demonstrate a newly developed graph theoretical method of inferring prior disease states in patients with Alzheimer's disease and mild cognitive impairment using an established network diffusion model and an L1-penalized optimization algorithm. Although the 'seeds' of origin using our inference method successfully reproduce known trends in Alzheimer's disease staging on a population level, we observed that the high degree of heterogeneity between patients at baseline is also reflected in their seeds. Additionally, the individualized seeds are significantly more predictive of future atrophy than a single seed placed at the hippocampus. Our findings illustrate that understanding where disease originates in individuals is critical to determining how it progresses and that our method allows us to infer early stages of disease from atrophy patterns observed at diagnosis.

Age-Related Changes in Topological Degradation of White Matter Networks and Gene Expression in Chronic Schizophrenia.

Current hypotheses stipulate core symptoms of schizophrenia (SZ) result from the brain's incapacity to integrate neural processes. Converging diffusion magnetic resonance imaging and graph theory studies provide evidence of macrostructural alterations in SZ. However, age-related topological changes within and between white matter (WM) networks and its relationship to gene expression with disease progression remain incompletely understood. This cross-sectional study uses network modeling to investigate changes in WM network organization with disease progression in chronic SZ as well its relationship with gene expression in healthy brains. First, we replicate prior findings demonstrating altered global WM network topology in SZ. Novel results show significantly altered age-related network degradation patterns in patients compared with controls. Specifically, controls show stereotyped, linear global network decline with age. In contrast, patients show nonlinear network decline with age. Further analysis reveals lack of significant topological decline in younger adult patients, which is subsequently followed by stereotyped linear decline in older adult patients. Node-specific analyses show significant topological differences in frontal and limbic regions of younger adult patients compared with age-matched controls, which become less pronounced with age in older adult patients compared with age-matched controls. Lastly, we show several gene expression profiles, including DISC1, are associated with age-related changes in WM disconnectivity. Together, these findings provide novel WM topological and genetic evidence supporting neurodevelopmental models of SZ, suggesting that network remodeling continues throughout the third decade of life before stabilizing.

Functional connectivity of the left DLPFC to striatum predicts treatment response of depression to TMS.

BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is a non-invasive, safe, and efficacious treatment for depression. TMS has been shown to normalize abnormal functional connectivity of cortico-cortical circuits in depression and baseline functional connectivity of these circuits predicts treatment response. Less is known about the relationship between functional connectivity of frontostriatal circuits and treatment response. OBJECTIVE/HYPOTHESIS: We investigated whether baseline functional connectivity of distinct frontostriatal circuits predicted response to TMS. METHODS: Resting-state fMRI (rsfMRI) was acquired in 27 currently depressed subjects with treatment resistant depression and 27 healthy controls. Depressed subjects were treated with 5 weeks of daily TMS over the left dorsolateral prefrontal cortex (DLPFC). The functional connectivity between limbic, executive, rostral motor, and caudal motor regions of frontal cortex and their corresponding striatal targets were determined at baseline using an existing atlas based on diffusion tensor imaging. TMS treatment response was measured by percent reduction in the 24-item Hamilton Depression Rating Scale (HAMD24). In an exploratory analysis, correlations were determined between baseline functional connectivity and TMS treatment response. RESULTS: Seven cortical clusters belonging to the executive and rostral motor frontostriatal projections had reduced functional connectivity in depression compared to healthy controls. No frontostriatal projections showed increased functional connectivity in depression (voxel-wise p < 0.01, family-wise α < 0.01). Only baseline functional connectivity between the left DLPFC and the striatum predicted TMS response. Higher baseline functional connectivity correlated with greater reductions in HAMD24 (Pearson's R = 0.58, p = 0.002). CONCLUSION(S): In an exploratory analysis, higher functional connectivity between the left DLPFC and striatum predicted better treatment response. Our findings suggest that the antidepressant mechanism of action of TMS may require connectivity from cortex proximal to the stimulation site to the striatum.