Publications by authors named "Ashish Raj"

73 Publications

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

Brain Commun 2021 15;3(3):fcab144. Epub 2021 Jul 15.

Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/braincomms/fcab144DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376686PMC
July 2021

Emergence of directional bias in tau deposition from axonal transport dynamics.

PLoS Comput Biol 2021 07 27;17(7):e1009258. Epub 2021 Jul 27.

Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, United States of America.

Defects in axonal transport may partly underpin the differences between the observed pathophysiology of Alzheimer's disease (AD) and that of other non-amyloidogenic tauopathies. Particularly, pathological tau variants may have molecular properties that dysregulate motor proteins responsible for the anterograde-directed transport of tau in a disease-specific fashion. Here we develop the first computational model of tau-modified axonal transport that produces directional biases in the spread of tau pathology. We simulated the spatiotemporal profiles of soluble and insoluble tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Changes in the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between compartments in the system. Aggregation and fragmentation parameters can also perturb this balance, suggesting a complex interplay between these distinct molecular processes. Critically, we show that the model faithfully recreates the characteristic network spread biases in both AD-like and non-AD-like mouse tauopathy models. Tau transport feedback may therefore help link microscopic differences in tau conformational states and the resulting variety in clinical presentations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1009258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345857PMC
July 2021

Emergence of canonical functional networks from the structural connectome.

Neuroimage 2021 08 19;237:118190. Epub 2021 May 19.

Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United States. Electronic address:

How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its "structural connectome". By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2021.118190DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451304PMC
August 2021

Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography.

Brain Connect 2021 Oct 16;11(8):624-638. Epub 2021 Jul 16.

Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy.

Alzheimer's disease involves widespread and progressive deposition of misfolded protein tau (), first appearing in the entorhinal cortex, coagulating in longer polymers and insoluble fibrils. There is mounting evidence for "prion-like" trans-neuronal transmission, whereby misfolded proteins cascade along neuronal pathways, giving rise to networked spread. However, the cause-effect mechanisms by which various oligomeric species are produced, aggregate, and disseminate are unknown. The question of how protein aggregation and subsequent spread lead to stereotyped progression in the Alzheimer brain remains unresolved. We address these questions by using mathematically precise parsimonious modeling of these pathophysiological processes, extrapolated to the whole brain. We model three key processes: monomer production; aggregation into oligomers and then into tangles; and the spatiotemporal progression of misfolded as it ramifies into neural circuits via the brain connectome. We model monomer seeding and production at the entorhinal cortex, aggregation using Smoluchowski equations; and networked spread using our prior Network-Diffusion model. This combined aggregation-network-diffusion model exhibits all hallmarks of progression seen in human patients. Unlike previous theoretical studies of protein aggregation, we present here an empirical validation on imaging and fluid measurements from large datasets. The model accurately captures not just the spatial distribution of empirical regional and atrophy but also patients' cerebrospinal fluid phosphorylated profiles as a function of disease progression. This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing . Impact statement The presented aggregation-network-diffusion model exhibits all hallmarks of tau progression in human patients; it accurately captures not just the spatial distribution of empirical regional tau and atrophy but also patients' cerebrospinal fluid phosphorylated tau profiles. Thus, it serves to fill a theoretical gap between microscopic biophysical processes and empirical macroscopic measurements of pathological patterns in Alzheimer's disease. This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/brain.2020.0841DOI Listing
October 2021

Population-based input function for TSPO quantification and kinetic modeling with [C]-DPA-713.

EJNMMI Phys 2021 Apr 29;8(1):39. Epub 2021 Apr 29.

Department of Radiology, Weill Cornell Medical College, New York, NY, 10021, USA.

Introduction: Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF.

Materials And Methods: Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (V) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weight×Dose, (b) area under AIF curve (AUC), and (c) Weight×AUC. The variability in the V measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed.

Results: Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based V estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is -10% and ±45% for Weight×Dose; +8% and ±50% for AUC; and +2% and ± 38% for Weight×AUC. In all cases, normalizing by Weight×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average V values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%).

Conclusions: PSAIF kinetic results are reproducible with PBIF, with variability in V within that obtained for the test-retest studies. Therefore, V assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40658-021-00381-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085191PMC
April 2021

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

Authors:
Ashish Raj

Brain Connect 2021 May 25. Epub 2021 May 25.

Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA.

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. 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. 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 . 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 test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/brain.2020.0905DOI Listing
May 2021

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

Neuroimage 2021 07 28;235:118008. Epub 2021 Mar 28.

Department of Radiology, Weill Cornell Medical College of Cornell University, 407 E. 61 Street, RR106, New York, NY 10065, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2021.118008DOI Listing
July 2021

Computational Models in Electroencephalography.

Brain Topogr 2021 Mar 29. Epub 2021 Mar 29.

Department of Ophthalmology, Hopital Ophthalmic Jules Gonin, FAA, Lausanne, Switzerland.

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by "computational model" is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10548-021-00828-2DOI Listing
March 2021

Algebraic relationship between the structural network's Laplacian and functional network's adjacency matrix is preserved in temporal lobe epilepsy subjects.

Neuroimage 2021 03 30;228:117705. Epub 2020 Dec 30.

Radiology and Biomedical Imaging Graduate Program in BioEngineering UCSF, San Francisco, CA, USA.

The relationship between anatomic and resting state functional connectivity of large-scale brain networks is a major focus of current research. In previous work, we introduced a model based on eigen decomposition of the Laplacian which predicts the functional network from the structural network in healthy brains. In this work, we apply the eigen decomposition model to two types of epilepsy; temporal lobe epilepsy associated with mesial temporal sclerosis, and MRI-normal temporal lobe epilepsy. Our findings show that the eigen relationship between function and structure holds for patients with temporal lobe epilepsy as well as normal individuals. These results suggest that the brain under TLE conditions reconfigures and rewires the fine-scale connectivity (a process which the model parameters are putatively sensitive to), in order to achieve the necessary structure-function relationship.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2020.117705DOI Listing
March 2021

Molecular Imaging of Striatal Dopaminergic Neuronal Loss and the Neurovascular Unit in Parkinson Disease.

Front Neurosci 2020 18;14:528809. Epub 2020 Sep 18.

Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States.

Parkinson disease (PD) is the second most common neurodegenerative disorder, characterized by loss of nigrostriatal dopaminergic neurons. Impairment of the neurovascular unit (NVU) has been hypothesized to play a critical role in early PD pathophysiology, and to precede neurodegenerative mechanisms. [C-11]-PE2I (-(3-iodoprop-2E-enyl)-2b-carbomethoxy-3b-(4-methyl-phenyl)nortropane) (PE2I) is a PET radiotracer targeting neuronal dopamine transporters (DaT) with high specificity, allowing for highly accurate and specific DaT quantification. We investigated NVU integrity using arterial spin labeling (ASL) MRI in a prospective cohort of 26 patients with PD, and correlated our findings with analysis of striatal DaT density using PE2I PET in a subcohort of 17 patients. Analysis was performed in FreeSurfer to obtain rCBF and mean standardized regional PET avidity. Pearson correlations and Mann-Whitney tests were performed. Significantly lower mean normalized striatal PE2I SUV values were seen in multiple regions in patients with greater disease duration ( < 0.05). PET uptake in the putamen correlated with disease duration independent of patient age. Stratifying patients based on Montreal Cognitive Assessment (MoCA) scores (stratified into ≥ 27 vs. < 27), there was statistically significantly lower PE2I PET avidity in the higher MoCA score group in both more and less affected sides of the caudate, putamen and pallidum ( < 0.05). A moderate negative correlation between MDS-UPDRS part 3 (motor) "off" and rCBF values was also seen in the L and R cerebellum WM ( = -0.43 and -0.47, < 0.05). A statistically significant negative correlation was found between dominant hand pegboard test results and rCBF in the less affected pallidum ( = -0.41; = 0.046). A statistically significant negative correlation of ASL MRI with [11C]-PE2I PET was also found ( = -0.53 to -0.58; -value 0.017-0.033) between left cerebral WM rCBF and more and less affected striatal PET regions. Our ROI-based analyses suggest that longer disease duration is associated with lower rCBF and lower PE2I mean SUV, implying greater NVU dysfunction and dopaminergic neuronal loss, respectively. Combined ASL MRI and PE2I PET imaging could inform future prospective clinical trials providing an improved mechanistic understanding of the disease, laying the foundation for the development of early disease biomarkers and potential therapeutic targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fnins.2020.528809DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530280PMC
September 2020

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

Brain Commun 2020 11;2(2):fcaa065. Epub 2020 Jun 11.

Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, USA.

There is enormous clinical value in inferring the brain regions initially atrophied in Parkinson disease for individual patients and understanding its relationship with clinical and genetic risk factors. The aim of this study is to leverage a new seed-inference algorithm demonstrated for Alzheimer's disease to the Parkinsonian context and to cluster patients in meaningful subgroups based on these incipient atrophy patterns. Instead of testing brain regions separately as the likely initiation site for each patient, we solve an L1-penalized optimization problem that can return a more predictive heterogeneous, multi-locus seed patterns. A cluster analysis of the individual seed patterns reveals two distinct subgroups (S1 versus S2). The S1 subgroup is characterized by the involvement of the brainstem and ventral nuclei, and S2 by cortex and striatum. analysis in features not included in the clustering shows significant differences between subgroups regarding age of onset and local transcriptional patterns of Parkinson-related genes. Top genes associated with regional microglial abundance are strongly associated with subgroup S1 but not with S2. Our results suggest two distinct aetiological mechanisms operative in Parkinson disease. The interplay between immune-related genes, lysosomal genes, microglial abundance and atrophy initiation sites may explain why the age of onset for patients in S1 is on average 4.5 years later than for those in S2. We highlight and compare the most prominently affected brain regions for both subgroups. Altogether, our findings may improve current screening strategies for early Parkinson onsetters.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/braincomms/fcaa065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472895PMC
June 2020

Network mediation of pathology pattern in sporadic Creutzfeldt-Jakob disease.

Brain Commun 2020 15;2(1):fcaa060. Epub 2020 May 15.

Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY 10065, USA.

Sporadic Creutzfeldt-Jakob disease is a rare fatal rapidly progressive dementia caused by the accumulation and spread of pathologically misfolded prions. Evidence from animal models and experiments suggests that prion pathology propagates along neural connectivity pathways, with the transmission of misfolded prions initiating a corruptive templating process in newly encountered brain regions. Although particular regional patterns of disease have been recognized in humans, the underlying mechanistic basis of these patterns remains poorly understood. Here, we demonstrate that the spatial pattern of disease derived from publicly available human diffusion-weighted MRI data demonstrates stereotypical features across patient cohorts and can be largely explained by intrinsic connectivity properties of the human structural brain network. Regional diffusion-weighted MRI signal abnormalities are predicted by graph theoretical measures of centrality, with highly affected regions such as cingulate gyrus demonstrating strong structural connectivity to other brain regions. We employ network diffusion modelling to demonstrate that the spatial pattern of disease can be predicted by a diffusion process originating from a single regional pathology seed and operating on the structural connectome. The most likely seeds correspond to the most highly affected brain regions, suggesting that pathological prions could originate in a single brain region and spread throughout the brain to produce the regional distribution of pathology observed on MRI. Further investigation of top seed regions and associated connectivity pathways may be a useful strategy for developing therapeutic approaches.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/braincomms/fcaa060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425363PMC
May 2020

A dictionary-based graph-cut algorithm for MRI reconstruction.

NMR Biomed 2020 12 2;33(12):e4344. Epub 2020 Jul 2.

Department of Radiology, University of California, San Francisco, California.

Purpose: Compressive sensing (CS)-based image reconstruction methods have proposed random undersampling schemes that produce incoherent, noise-like aliasing artifacts, which are easier to remove. The denoising process is critically assisted by imposing sparsity-enforcing priors. Sparsity is known to be induced if the prior is in the form of the L (0 ≤ p ≤ 1) norm. CS methods generally use a convex relaxation of these priors such as the L norm, which may not exploit the full power of CS. An efficient, discrete optimization formulation is proposed, which works not only on arbitrary L -norm priors as some non-convex CS methods do, but also on highly non-convex truncated penalty functions, resulting in a specific type of edge-preserving prior. These advanced features make the minimization problem highly non-convex, and thus call for more sophisticated minimization routines.

Theory And Methods: The work combines edge-preserving priors with random undersampling, and solves the resulting optimization using a set of discrete optimization methods called graph cuts. The resulting optimization problem is solved by applying graph cuts iteratively within a dictionary, defined here as an appropriately constructed set of vectors relevant to brain MRI data used here.

Results: Experimental results with in vivo data are presented.

Conclusion: The proposed algorithm produces better results than regularized SENSE or standard CS for reconstruction of in vivo data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/nbm.4344DOI Listing
December 2020

Spectral graph theory of brain oscillations.

Hum Brain Mapp 2020 08 23;41(11):2980-2998. Epub 2020 Mar 23.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California.

The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/hbm.24991DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336150PMC
August 2020

Stereotaxic Diffusion Tensor Imaging White Matter Atlas for the Domestic Feline Brain.

Front Neuroanat 2020 11;14. Epub 2020 Feb 11.

Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States.

The cat brain is a useful model for neuroscientific research and with the increasing use of advanced neuroimaging techniques there is a need for an open-source stereotaxic white matter brain atlas to accompany the cortical gray matter atlas, currently available. A stereotaxic white matter atlas would facilitate anatomic registration and segmentation of the white matter to aid in lesion localization or standardized regional analysis of specific regions of the white matter. In this article, we document the creation of a stereotaxic feline white matter atlas from diffusion tensor imaging (DTI) data obtained from a population of eight mesaticephalic felines. Deterministic tractography reconstructions were performed to create tract priors for the major white matter projections of Corpus callosum (CC), fornix, cingulum, uncinate, Corona Radiata (CR), Corticospinal tract (CST), inferior longitudinal fasciculus (ILF), Superior Longitudinal Fasciculus (SLF), and the cerebellar tracts. T1-weighted, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) population maps were generated. The volume, mean tract length and mean FA, MD, AD and RD values for each tract prior were documented. A structural connectome was then created using previously published cortical priors and the connectivity metrics for all cortical regions documented. The provided white matter atlas, diffusivity maps, tract priors and connectome will be a valuable resource for anatomical, pathological and translational neuroimaging research in the feline model. Multi-atlas population maps and segmentation priors are available at Cornell's digital repository: https://ecommons.cornell.edu/handle/1813/58775.2.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fnana.2020.00001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7026623PMC
February 2020

Dynamical Role of Pivotal Brain Regions in Parkinson Symptomatology Uncovered with Deep Learning.

Brain Sci 2020 Jan 30;10(2). Epub 2020 Jan 30.

Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA.

Background: The release of a broad, longitudinal anatomical dataset by the Parkinson's Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to the Parkinsonian symptomatology.

Objectives: The aim of this study is two-fold: (i) to predict future motor and cognitive impairments up to four years from brain features acquired at baseline; and (ii) to interpret the role of pivotal brain regions responsible for different symptoms from a neurological viewpoint.

Methods: We test several deep-learning neural network configurations, and report our best results obtained with an autoencoder deep-learning model, run on a 5-fold cross-validation set. Comparison with Existing Methods: Our approach improves upon results from standard regression and others. It also includes neuroimaging biomarkers as features.

Results: The relative contributions of pivotal brain regions to each impairment change over time, suggesting a dynamical reordering of culprits as the disease progresses. Specifically, the Putamen is initially the most critical region accounting for the overall cognitive state, only being surpassed by the Substantia Nigra in later years. The Pallidum is the first region to influence motor scores, followed by the parahippocampal and ambient gyri, and the anterior orbital gyrus.

Conclusions: While the causal link between regional brain atrophy and Parkinson symptomatology is poorly understood, our methods demonstrate that the contributions of pivotal regions to cognitive and motor impairments are more dynamical than generally appreciated.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/brainsci10020073DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071401PMC
January 2020

Neural connectivity predicts spreading of alpha-synuclein pathology in fibril-injected mouse models: Involvement of retrograde and anterograde axonal propagation.

Neurobiol Dis 2020 02 16;134:104623. Epub 2019 Oct 16.

Weill Cornell Medicine of Cornell University, Department of Radiology, New York, NY 10065, USA; University of California-San Francisco, Department of Biomedical Imaging, USA.

In Parkinson's disease, some of the first alpha-synuclein aggregates appear in the olfactory system and the dorsal motor nucleus of the vagus nerve before spreading to connected brain regions. We previously demonstrated that injection of alpha-synuclein fibrils unilaterally into the olfactory bulb of wild type mice leads to widespread synucleinopathy in brain regions directly and indirectly connected to the injection site, consistently, over the course of periods longer than 6 months. Our previously reported observations support the idea that alpha-synuclein inclusions propagates between brain region through neuronal networks. In the present study, we further defined the pattern of propagation of alpha-synuclein inclusions and developed a mathematical model based on known mouse brain connectivity. Using this model, we first predicted the pattern of alpha-synuclein inclusions propagation following an injection of fibrils into the olfactory bulb. We then analyzed the fitting of these predictions to our published histological data. Our results demonstrate that the pattern of propagation we observed in vivo is consistent with axonal transport of alpha-synuclein aggregate seeds, followed by transsynaptic transmission. By contrast, simple diffusion of alpha-synuclein fits very poorly our in vivo data. We also found that the spread of alpha-synuclein inclusions appeared to primarily follow neural connections retrogradely until 9 months after injection into the olfactory bulb. Thereafter, the pattern of spreading was consistent with anterograde propagation mathematical models. Finally, we applied our mathematical model to a different, previously published, dataset involving alpha-synuclein fibril injections into the striatum, instead of the olfactory bulb. We found that the mathematical model accurately predicts the reported progressive increase in alpha-synuclein neuropathology also in that paradigm. In conclusion, our findings support that the progressive spread of alpha-synuclein inclusions after injection of protein fibrils follows neural networks in the mouse connectome.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nbd.2019.104623DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138530PMC
February 2020

Regional transcriptional architecture of Parkinson's disease pathogenesis and network spread.

Brain 2019 10;142(10):3072-3085

Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, USA.

Although a significant genetic contribution to the risk of developing sporadic Parkinson's disease has been well described, the relationship between local genetic factors, pathogenesis, and subsequent spread of pathology throughout the brain has been largely unexplained in humans. To address this question, we use network diffusion modelling to infer probable pathology seed regions and patterns of disease spread from MRI atrophy maps derived from 232 de novo subjects in the Parkinson's Progression Markers Initiative study. Allen Brain Atlas regional transcriptional profiles of 67 Parkinson's disease risk factor genes were mapped to the inferred seed regions to determine the local influence of genetic risk factors. We used hierarchical clustering and L1 regularized regression analysis to show that transcriptional profiles of immune-related and lysosomal risk factor genes predict seed region location and the pattern of disease propagation from the most likely seed region, substantia nigra. By leveraging recent advances in transcriptomics, we show that regional microglial abundance quantified by high fidelity gene expression also predicts seed region location. These findings suggest that early disease sites are genetically susceptible to dysfunctional lysosomal α-synuclein processing and microglia-mediated neuroinflammation, which may initiate the disease process and contribute to spread of pathology along neural connectivity pathways.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/brain/awz223DOI Listing
October 2019

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI.

Hum Brain Mapp 2019 10 11;40(15):4441-4456. Epub 2019 Jul 11.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California.

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/hbm.24713DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865536PMC
October 2019

Systematic Differences Between Perceptually Relevant Image Statistics of Brain MRI and Natural Images.

Front Neuroinform 2019 25;13:46. Epub 2019 Jun 25.

Department of Neurology and Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY, United States.

It is well-known that the human visual system is adapted to the statistical structure of natural scenes. Yet there are important classes of images - for example, medical images - that are not natural scenes, and therefore, that are expected to have statistical properties that deviate from the class of images that shaped the evolution and development of human vision. Here, focusing on structural brain MRI images, we quantify and characterize these deviations in terms of a set of local image statistics to which human visual sensitivity has been well-characterized, and that has previously been used for natural image analysis. We analyzed MRI images in multiple databases including T1-weighted and FLAIR sequence types, and simulated MRI images based on a published image simulation procedure for T1 images, which we also modified to generate FLAIR images. We first computed the power spectra of MRI images; spectral slopes were in the range -2.6 to -3.1 for T1 sequences, and -2.2 to -2.7 for FLAIR sequences. Analysis of local image statistics was then carried out on whitened images. For all of the databases as well as for the simulated images, we found that the three-point correlations contributed substantially to the differences between the "texture" of randomly selected ROIs. The informative nature of three-point correlations for brain MRI was greater than for natural images, and also disproportionate to human visual sensitivity. As this finding was consistent across databases, it is likely to result from brain geometry at the scale of brain MRI resolution, rather than characteristics of specific imaging and reconstruction methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fninf.2019.00046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603243PMC
June 2019

Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries.

J Comput Neurosci 2019 08 4;47(1):1-16. Epub 2019 Jun 4.

Department of Applied Mathematics, University of Washington, Seattle, WA, 98195-2420, USA.

We introduce a computational model for the cellular level effects of firing rate filtering due to the major forms of neuronal injury, including demyelination and axonal swellings. Based upon experimental and computational observations, we posit simple phenomenological input/output rules describing spike train distortions and demonstrate that slow-gamma frequencies in the 38-41 Hz range emerge as the most robust to injury. Our signal-processing model allows us to derive firing rate filters at the cellular level for impaired neural activity with minimal assumptions. Specifically, we model eight experimentally observed spike train transformations by discrete-time filters, including those associated with increasing refractoriness and intermittent blockage. Continuous counterparts for the filters are also obtained by approximating neuronal firing rates from spike trains convolved with causal and Gaussian kernels. The proposed signal processing framework, which is robust to model parameter calibration, is an abstraction of the major cellular-level pathologies associated with neurodegenerative diseases and traumatic brain injuries that affect spike train propagation and impair neuronal network functionality. Our filters are well aligned with the spectrum of dynamic memory fields including working memory, visual consciousness, and other higher cognitive functions that operate in a frequency band that is - at a single cell level - optimally guarded against common types of pathological effects. In contrast, higher-frequency neural encoding, such as is observed with short-term memory, are susceptible to neurodegeneration and injury.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10827-019-00714-8DOI Listing
August 2019

Editorial: Network Spread Models of Neurodegenerative Diseases.

Front Neurol 2018 8;9:1159. Epub 2019 Jan 8.

Montreal Neurological Institute, McGill University, Montreal, QC, Canada.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fneur.2018.01159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331437PMC
January 2019

Normal diffusivity of the domestic feline brain.

J Comp Neurol 2019 04 21;527(5):1012-1023. Epub 2018 Dec 21.

Cornell College of Veterinary Medicine, Cornell University, Ithaca, New York.

Diffusion magnetic resonance imaging (MRI) provides useful information about neuroanatomy and improves detection of neuropathology. As yet, a comprehensive evaluation of the diffusivity parameters within the feline brain has not been documented. In this study, we anesthetized and performed in vivo MRI on the brain of eight neurologically normal felines. A T1-weighted structural sequence with a resolution of 0.5 mm and a parallel diffusion weighted sequence with 61 directions and a resolution of 1.5 mm was obtained. After correction and processing the diffusion brain data were parcellated into 151 regions of interest using previously published priors. These regions were grouped according to their lobar location within the brain (frontal, occipital, temporal, parietal, thalamus, midbrain, cerebellum, and white matter). The mean and standard deviation of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) for these 151 individual regions and lobar groups were calculated and averaged across participants, creating a comprehensive distribution range of diffusion tensor values. When regions were statistically evaluated, white matter had significantly higher FA and RD and lower AD and MD diffusivity parameters when compared to other regions. Additionally, thalamic regions had significantly higher FA values than parietal and occipital regions. This information will not only help inform feline neuroanatomy but also will serve as a reference standard for future feline neuroimaging studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cne.24553DOI Listing
April 2019

Models of Network Spread and Network Degeneration in Brain Disorders.

Biol Psychiatry Cogn Neurosci Neuroimaging 2018 09 3;3(9):788-797. Epub 2018 Aug 3.

Department of Radiology, Weill Cornell Medicine, New York, New York.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bpsc.2018.07.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219468PMC
September 2018

Regional expression of genes mediating trans-synaptic alpha-synuclein transfer predicts regional atrophy in Parkinson disease.

Neuroimage Clin 2018 31;18:456-466. Epub 2018 Jan 31.

Department of Radiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, United States; Department of Radiology, University of California, San Francisco, United States.

Multiple genes have been implicated in Parkinson disease pathogenesis, but the relationship between regional expression of these genes and regional dysfunction across the brain is unknown. We address this question by joint analysis of high resolution magnetic resonance imaging data from the Parkinson's Progression Markers Initiative and regional genetic microarray expression data from the Allen Brain Atlas. Regional brain atrophy and genetic expression was co-registered to a common 86 region brain atlas and robust multivariable regression analysis was performed to identify genetic predictors of regional brain atrophy. Top candidate genes from GWAS analysis, as well as genes implicated in trans-synaptic alpha-synuclein transfer and autosomal recessive PD were included in our analysis. We identify three genes with expression patterns that are highly significant predictors of regional brain atrophy. The two most significant predictors are LAG3 and RAB5A, genes implicated in trans-synaptic synuclein transfer. Other well-validated PD-related genes do not have expression patterns that predict regional atrophy, suggesting that they may serve other roles such as disease initiation factors.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.nicl.2018.01.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984599PMC
February 2019

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

J Alzheimers Dis 2018 ;65(3):747-764

Department of Radiology, Weill Cornell Medicine, New York, NY, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/JAD-170798DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152926PMC
August 2019

Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.

Neuroimage 2018 05 14;172:728-739. Epub 2018 Feb 14.

Radiology, Weill Cornell Medical College, New York, NY, USA.

How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroimage.2018.02.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170160PMC
May 2018

A method for inferring regional origins of neurodegeneration.

Brain 2018 03;141(3):863-876

Department of Radiology, Weill Cornell Medical College of Cornell University, 407 E. 61 Street, RR106, New York, NY 10065, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/brain/awx371DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837438PMC
March 2018

Analysis of Amyloid-β Pathology Spread in Mouse Models Suggests Spread Is Driven by Spatial Proximity, Not Connectivity.

Front Neurol 2017 18;8:653. Epub 2017 Dec 18.

Department of Neuroscience, Weill Cornell Medicine of Cornell University, New York, NY, United States.

While the spread of some neurodegenerative disease-associated proteinopathies, such as tau and α-synuclein, is well studied and clearly implicates transsynaptic pathology transmission, research into the progressive spread of amyloid-β pathology has been less clear. In fact, prior analyses of transregional amyloid-β pathology spread have implicated both transsynaptic and other intracellular- as well as extracellular-based transmission mechanisms. We therefore conducted the current meta-analytic analysis to help assess whether spatiotemporal amyloid-β pathology development patterns in mouse models, where regional proteinopathy is more directly characterizable than in patients, better fit with transsynaptic- or extracellular-based theories of pathology spread. We find that, consistently across the datasets used in this study, spatiotemporal amyloid-β pathology patterns are more consistent with extracellular-based explanations of pathology spread. Furthermore, we find that regional levels of amyloid precursor protein in a mouse model are also better correlated with expected pathology patterns based on extracellular, rather than intracellular or transsynaptic spread.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fneur.2017.00653DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741600PMC
December 2017

Predictive Model of Spread of Progressive Supranuclear Palsy Using Directional Network Diffusion.

Front Neurol 2017 21;8:692. Epub 2017 Dec 21.

Department of Radiology, Weill Cornell Medicine, New York, NY, United States.

Several neurodegenerative disorders including Alzheimer's disease (AD), frontotemporal dementia (FTD), Parkinson's disease (PD), amyotrophic lateral sclerosis, and Huntington's disease report aggregation and transmission of pathogenic proteins between cells. The topography of these diseases in the human brain also, therefore, displays a well-characterized and stereotyped regional pattern, and a stereotyped progression over time. This is most commonly true for AD and other dementias characterized by hallmark misfolded tau or alpha-synuclein pathology. Both tau and synuclein appear to propagate within brain circuits using a shared mechanism. The most canonical synucleopathy is PD; however, much less studied is a rare disorder called progressive supranuclear palsy (PSP). The hallmark pathology and atrophy in PSP are, therefore, also highly stereotyped: initially appearing in the striatum, followed by its neighbors and connected cortical areas. In this study, we explore two mechanistic aspects hitherto unknown about the canonical network diffusion model (NDM) of spread: (a) whether the NDM can apply to other common degenerative pathologies, specifically PSP, and (b) whether the directionality of spread is important in explaining empirical data. Our results on PSP reveal two important findings: first, that PSP is amenable to the connectome-based ND modeling in the same way as previously applied to AD and FTD and, second, that the NDM fit with empirical data are significantly enhanced by using the directional rather than the non-directional form of the human connectome. Specifically, we show that both the anterograde model of transmission (some to axonal terminal) and retrograde mode explain PSP topography more accurately than non-directional transmission. Collectively, these data show that the intrinsic architecture of the structural network mediates disease spread in PSP, most likely a process of trans-neuronal transmission. These intriguing results have several ramifications for future studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fneur.2017.00692DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5742613PMC
December 2017
-->