Biology subjects

Toga, A.

Publications and source records attributed to Toga, A..

4 recordsLinked to original sources

bioRxiv2026

White Matter Slow-4 fALFF as a Complementary Marker in the Multimodal Alzheimer's Disease Biomarker Landscape

INTRODUCTION White matter (WM) functional activity, quantified as slow-4 fractional amplitude of low-frequency fluctuations (fALFF; 0.027-0.073 Hz), may reflect Alzheimer's disease (AD) pathology, but its utility relative to established structural imaging markers remains poorly characterized. METHODS We analyzed 369 ADNI-3 participants with baseline resting-state fMRI. Global WM slow-4 fALFF (JHU-20 atlas) was compared against hippocampal volume, entorhinal cortical thickness, FA, and MD across 11 outcomes: plasma biomarkers (pTau217, Abeta42/40, NfL, GFAP; n = 108), PET (amyloid Centiloid, tau SUVR, FDG; n = 66-207), and cognition (MEM, EF, mPACC, MMSE; n = 222-225) using head-to-head benchmarking and variance decomposition. RESULTS WM fALFF was significantly associated with GFAP (partial R2 = 5.35%), amyloid PET (partial R2 = 2.17%), tau PET (partial R2 = 3.52%), executive function (partial R2 = 2.61%), and mPACC (partial R2 = 3.53%). In trimmed models, fALFF explained unique variance in NfL and GFAP beyond FA and MD (unique delta R2: 3.65% and 4.45%), nearly 8-fold greater than DTI's unique contribution for NfL. Gray matter markers showed larger associations with cognition and PET pathology. The incremental value of fALFF was modality-specific: it persisted beyond DTI markers for NfL, GFAP, amyloid PET, executive function, and mPACC, and beyond gray matter markers for NfL and GFAP, but in every domain, including plasma, fALFF was no longer significant when all five imaging markers were entered simultaneously. DISCUSSION WM slow-4 fALFF captures neurodegeneration-related variance, particularly astroglial reactivity and axonal injury, incremental to DTI microstructure and partially independent of gray matter atrophy. These findings position WM functional activity as a complementary, non-invasive imaging marker in the multimodal AD biomarker landscape.

neuroscience↗

bioRxiv2020

Compressive Big Data Analytics: An Ensemble Meta-Algorithm for High-dimensional Multisource Datasets

Health advances are contingent on continuous development of new methods and approaches to foster data driven discovery in the biomedical and clinical health sciences. Open-science offers hope for tackling some of the challenges associated with Big Data and team-based scientific discovery. Domain-independent reproducibility, area-specific replicability, curation, analysis, organization, management and sharing of health-related digital objects are critical components. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA identifies salient features and key biomarkers for reliable and reproducible forecasting of binary or multinomial outcomes. The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions of observed univariate outcomes. In this manuscript, we extend the CBDA technique by (1) efficiently handling extremely large datasets, (2) generalizing the internal and external validation steps, (3) expanding the set of base-learners for joint ensemble prediction, (4) introduce an automated selection of CBDA specifications, and (5) provide mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency. We validated the CBDA 2.0 technique using synthetic datasets as well as a population-wide census-like study, which grounds the mathematical models and the computational algorithm into translational health research settings. Specifically, we empirically validated the CBDA technique on a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, feature heterogeneity, multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions that require novel health analytical approaches. Our results showcase the scalability, efficiency and potential of CBDA to compress complex data into structural information leading to derived knowledge and translational action. The results of the real case-study suggest new and exciting avenues of research in the context of identifying, tracking, and treating mental health and aging-related disorders. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.

bioinformatics↗

bioRxiv2018

Association of brain age with smoking, alcohol consumption, and genetic variants

The association of the degree of aging based on the whole-brain anatomical characteristics, or brain age, with smoking, alcohol consumption, and individual genetic variants is unclear. Here, we investigated these associations through analyzing data collected for UK Biobank subjects with an age range of 45 to 79 years old. We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subjects brain age relative to peers, based on a randomly selected training set subjects (n=2,679). We then applied this model to the evaluation set subjects (n=6,252) and further tested the association of RBA with tobacco smoking, alcohol consumption, and 529,098 genetic variants. We found that daily or almost daily consumption of smoking or alcohol was significantly associated with increased RBA (P<0.05). Interestingly, there was no significant difference of RBA among subjects who smoked occasionally, only tried once or twice, or abstained from smoking. Further, there was no significant difference of RBA among subjects who consumed alcohol 1 to 3 times a month, at special occasions only, or abstained from alcohol consumption. Among the subjects who smoked on most or all days and did not abstain from alcohol drinking, RBA increased by 0.021 years for each addition pack-year of smoking (P<0.05) and by 0.014 years for each additional gram of alcohol consumed (P<0.05). We did not identify individual genetic variation significantly associate with RBA. Further exploration of genetic variation-brain aging association is warranted, where our current genetic association statistics may serve as prior knowledge.

neuroscience↗

bioRxiv2018

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

bioinformatics↗