Biology subjects

Toga, A. W.

Publications and source records attributed to Toga, A. W..

26 records · Page 2Linked to original sources

bioRxiv2020

Alteration of perivascular spaces in early cognitive decline

Vascular contributions to early cognitive decline are increasingly recognized, prompting further investigation into the nature of related changes in perivascular space. Using magnetic resonance imaging, we show that, compared to a cognitively normal sample, individuals with early cognitive dysfunction have altered perivascular space presence and distribution, irrespective of Amyloid-{beta}. Surprisingly, we noted decreased perivascular space presence in the anterosuperior medial temporal lobe, which was associated with neurofibrillary tau tangle deposition in the entorhinal cortex, one of the hallmarks of early Alzheimers disease pathology. Our results suggest that anatomically-specific alteration of the perivascular spaces may provide an early biomarker of cognitive impairment in aging adults.

neuroscience↗

bioRxiv2019

Cross-Sectional and Longitudinal Validation of Serum Neurofilament Light Chain (NfL) as a Biomarker of Parkinson’s Disease Progression

ObjectiveTo assess neurofilament light chain (NfL), as a biomarker for Parkinsons disease (PD). MethodsWe quantified NfL in (1) longitudinal CSF samples from PD, other cognate/neurodegenerative disorders (OND), and healthy controls (HC); (2) a cross-sectional cohort with paired CSF and serum samples from participants with PD, OND, and HC, and (3) a large longitudinal validation cohort with serum samples from PD, OND, HC, prodromal conditions, and mutation carriers. ResultsIn the longitudinal discovery cohort (1) NfL in CSF was highest in OND and higher in PD vs. HC across all visits (p<0.05) but did not change longitudinally. In the cross-sectional cohort (2) paired CSF and serum NfL samples were highly correlated (Spearmans rank [Formula]; p<10^-6). In the large validation cohort (3) mean baseline serum NfL was higher in PD (13{+/-}7.2pg/ml) vs. HC (12{+/-}6.7pg/ml; p=0.0336) and was highest in OND (18{+/-}7pg/ml; p=0.0351). Serum NfL increased longitudinally in PD vs. HC (p<0.01). Longitudinal motor scores were positively longitudinally associated with NfL, whereas some cognitive scores showed a negative longitudinal association with NfL. ConclusionsNfL levels in serum samples are increased in PD vs. HC, increase significantly over time, and correlate with clinical measures of PD severity. Although the specificity of NfL in PD is low and more specific biomarkers are needed, serum NfL is the first blood-based biomarker candidate that could support disease stratification (PD vs. OND), track clinical progression, and possibly assess responsiveness to neuroprotective treatments. NfL as a biomarker of response to neuroprotective interventions remains to be determined. Funding sources for studyPPMI is sponsored by the Michael J. Fox Foundation for Parkinsons Research (MJFF) and is co-funded by MJFF, Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Eli Lilly & Co., F. Hoffman-La Roche, Ltd., GE Healthcare, Genentech, GlaxoSmithKline, Lundbeck, Merck, MesoScale, Piramal, Pfizer and UCB. The funders had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, in the preparation, review, or approval of the manuscript or in the decision to submit the manuscript for publication. Financial Disclosure/Conflict of Interest concerning the research related to the manuscriptBrit Mollenhauer, Douglas Galasko, Tatiana Foroud, Lana M. Chahine, Christopher S. Coffey, Andrew B. Singleton, Tanya Simuni, Daniel Weintraub, John Seibyl, Arthur W. Toga, and Caroline M. Tanner received funding from The Michael J. Fox Foundation for Parkinsons Research. Mohammed Dakna, Tzu-Ying Liu, Henrik Zetterberg, Sebastian Schade, Roland G. Gera, Wenting Wang, Feng Gao, Niels Kruse, Mark Frasier, Jesse M. Cedarbaum, Samantha J. Hutten, Claudia Trenkwalder, and Danielle Graham report no disclosures.

neuroscience↗

bioRxiv2019

Image processing approaches to enhance perivascular space visibility and quantification using MRI

Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.

neuroscience↗

bioRxiv2018

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

PurposeFiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do not capture variabilities because of in vivo physiological factors.\n\nMethodsTo date, a large-scale reproducibility analysis has not been performed for the assessment of the newest generation of tractography algorithms with in vivo data. Reproducibility does not assess the validity of a brain connection however it is still of critical importance because it describes the variability for an algorithm in group studies. The ISMRM 2017 TraCED challenge was created to fulfill the gap. The TraCED dataset consists of a single healthy volunteer scanned on two different scanners of the same manufacturer. The multi-shell acquisition included b-values of 1000, 2000 and 3000 s/mm2 with 20, 45 and 64 diffusion gradient directions per shell, respectively.\n\nResultsNine international groups submitted 46 tractography algorithm entries. The top five submissions had high ICC > 0.88. Reproducibility is high within these top 5 submissions when assessed across sessions or across scanners. However, it can be directly attributed to containment of smaller volume tracts in larger volume tracts. This holds true for the top five submissions where they are contained in a specific order. While most algorithms are contained in an ordering there are some outliers.\n\nConclusionThe different methods clearly result in fundamentally different tract structures at the more conservative specificity choices (i.e., volumetrically smaller tractograms). The data and challenge infrastructure remain available for continued analysis and provide a platform for comparison.

neuroscience↗

bioRxiv2018

A systematic bias in DTI findings

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.\n\nHighlightsO_LIPerivascular space (PVS) fluid significantly contributes to diffusion tensor imaging metrics\nC_LIO_LIIncreased PVS fluid results in increased mean diffusivity and decreased fractional anisotropy\nC_LIO_LIPVS contribution to diffusion signal is overlooked and demands further investigation\nC_LI

neuroscience↗

bioRxiv2018

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractographys inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

neuroscience↗

bioRxiv2018

Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility--thus ensuring that big data are not hard-to-(re)use data. We compare and contrast our approach with other approaches to big data analysis and reproducibility.

bioinformatics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗