Biology subjects

Tosun, D.

Publications and source records attributed to Tosun, D..

5 recordsLinked to original sources

bioRxiv2026

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning

Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimers disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies ({beta}-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here we evaluated automated machine learning models trained on data curated by the AD Sequencing Project Phenotype Harmonization Consortium (N=7,894 individuals), to predict 26 autopsy-confirmed neuropathological outcomes. Predictors included in vivo clinical and cognitive composite scores, brain measures from 3D structural MRI and diffusion tensor imaging, image-derived measures of white matter hyperintensities (WMH), and CSF biomarkers. Predictive models were trained using ensemble learning with stratified cross-validation. We assessed performance using Spearmans rank correlation and Matthews correlation coefficient, to accommodate co-occurring pathologic changes. The added value of neuroimaging and CSF versus clinical features alone was quantified. Braak stage was among the most consistently predicted outcomes. CSF biomarkers best predicted {beta}-amyloid and tau pathology, but diffusion MRI metrics best captured vascular brain injury and white matter injury, and outperformed clinical and cognitive measures and anatomical MRI in predicting Lewy body disease. Anatomical measures from structural MRI outperformed standard clinical assessments in assessing neurodegeneration and hippocampal sclerosis, and WMH complemented cognitive measures in predicting TDP-43 pathology. These results establish a baseline for comparing modalities for inferring neuropathology.

neuroscience↗

bioRxiv2024

Sex, racial, and APOE-ϵ4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSThe effects of sex, race, and Apolipoprotein E (APOE) - Alzheimers disease (AD) risk factors - on white matter integrity are not well characterized. METHODSDiffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 {+/-} 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex, race, and APOE-{varepsilon}4 carrier status. RESULTSSex differences in FAFWcorr in association and projection tracts, racial differences in FAFWcorr in projection tracts, and APOE-{varepsilon}4 differences in FW limbic and occipital transcallosal tracts were most pronounced. DISCUSSIONThere are prominent differences in white matter microstructure by sex, race, and APOE- {varepsilon}4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted. HighlightsO_LISex, race, and APOE-{varepsilon}4 carrier status relate to white matter microstructural integrity C_LIO_LIFemales generally have lower FAFWcorr compared to males C_LIO_LINon-Hispanic Black adults generally have lower FAFWcorr than non-Hispanic White adults C_LIO_LIAPOE-{varepsilon}4 carriers tended to have higher FW than non-carriers C_LI Research in Context Systematic ReviewThe authors used PubMed and Google Scholar to review literature that used conventional and free-water (FW)-corrected microstructural metrics to evaluate sex, race, and APOE-{varepsilon}4 differences in white matter microstructure. While studies have previously explored differences by sex and APOE-{varepsilon}4 status, less is known about racial differences and no large-scale FW-corrected analysis has been performed. InterpretationSex and race were more associated with FAFWcorr while APOE-{varepsilon}4 status was associated with FW metrics. Association, projection, limbic, and occipital transcallosal tracts showed the greatest differences. Future DirectionFuture studies to determine the biological and social pathways that lead to sex, racial, and APOE-{varepsilon}4 differences are warranted. Consent StatementAll participants provided informed consent in their respective cohort studies.

neuroscience↗

bioRxiv2022

Genetic, clinical underpinnings of subtle early brain change along Alzheimer's dimensions

Alzheimers disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised clustering technique known as Surreal-GAN, through which we identified two dominant dimensions of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE {varepsilon}4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE {varepsilon}4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction - driven by genes different from APOE - which may collectively contribute to the early pathogenesis of AD.

bioengineering↗

bioRxiv2019

Deep Learning Based Large-scale Histological Tau Protein Mapping for Neuroimaging Biomarker Validation in Alzheimer’s Disease

Deposits of abnormal tau protein inclusions in the brain are a pathological hallmark of Alzheimers disease (AD), and are the best predictor of neuronal loss and clinical decline, but have been limited to postmortem assessment. Imaging-based biomarkers to detect tau deposits in vivo could leverage AD diagnosis and monitoring beginning in pre-symptomatic disease stages. Several PET tau tracers are available for research studies, but validation of such tracers against direct detection of tau deposits in brain tissue remains incomplete because of methodological limitations. Confirmation of the biological basis of PET binding requires large-scale voxel-to-voxel correlation has been challenging because of the dimensionality of the whole human brain histology data, deformation caused by tissue processing that precludes registration, and the need to process terabytes of information to cover the whole human brain volume at microscopic resolution. In this study, we created a computational pipeline for segmenting tau inclusions in billion-pixel digital pathology images of whole human brains, aiming at generating quantitative, tridimensional tau density maps that can be used to decipher the distribution of tau inclusions along AD progression and validate PET tau tracers. Our pipeline comprises several pre- and post-processing steps developed to handle the high complexity of these brain digital pathology images. SlideNet, a convolutional neural network designed to process our large datasets to locate and segment tau inclusions, is at the core of the pipeline. Using our novel method, we have successfully processed over 500 slides from two whole human brains, immunostained for two phospho-tau antibodies (AT100 and AT8) spanning several Gigabytes of images. Our artificial neural network estimated strong tau inclusion from image segmentation, which performs with ROC AUC of 0.89 and 0.85 for AT100 and AT8, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. Furthermore, our pipeline successfully created 3D tau inclusion density maps that were co-registered to the histology 3D maps.

neuroscience↗

bioRxiv2019

BOLD Decoding of Individual Pain Anticipation Biases During Uncertainty

A prominent model of pain as a predictive cue posits that anticipation shapes pain transmission and ultimately pain experience. Consistent with this model, the neural mechanisms underlying pain anticipation have the power to modulate pain experience thus understanding pain predictions, particularly during uncertainty, may allow us to ascertain measures indicative of intrinsic anticipation biases. Understanding such biases moves way to precision pain management, as it can guide the individualized treatment. To examine individual pain anticipation biases, we applied machine-learning-based neural decoding to functional magnetic resonance imaging (fMRI) data acquired during a pain-anticipation paradigm to identify individualized neural activation patterns differentiating two certain anticipatory conditions, which we then used to decode that individuals uncertain anticipatory condition. We showed that neural patterns representative of the individualized response during certain anticipatory conditions were differentiable with high accuracy and, across individuals, most commonly involved neural activation patterns within anterior short gyrus of the insula and the nucleus accumbens. Using unsupervised clustering of individualized decodings of anticipatory responses during uncertain conditions, we identified three distinct response profiles representing subjects who, in uncertain situations, consistently anticipated high-pain (i.e., negative bias), subjects who consistently anticipated low-pain (i.e., positive bias), and subjects whose decoded anticipation responses were depended on the intensity of the preceding pain stimulus. The individualized decoded pain anticipation biases during uncertainty were independent of existence or type of diagnosed psychopathology, were stable over one year timespan and were related to underlying insula anatomy. Our results suggest that anticipation behaviors may be intrinsic, stable, and specific to each individual. Understanding individual differences in the neurobiology of pain anticipation has the potential to greatly improve the clinical pain management.

neuroscience↗