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Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning
2022
Brain Communications
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Supervised machine learning models can derive multi-variate patterns of brain change related to the two processes, including the SPARE-AD (Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease ...
Neuroimaging biomarkers that distinguish between changes due to typical brain aging and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. ...
Thus, a disentangled understanding of an aging brain using these measures would aid clinicians in understanding the severity of Alzheimer's disease-specific neurodegeneration and in potentially detecting ...
doi:10.1093/braincomms/fcac117
fatcat:625iw4imzzhpva636i4vqiizuu
Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
[article]
2021
arXiv
pre-print
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Neuroimaging biomarkers that distinguish between typical brain aging and Alzheimer's disease (AD) are valuable for determining how much each contributes to cognitive decline. ...
Machine learning models can derive multi-variate brain change patterns related to the two processes, including the SPARE-AD (Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease) and SPARE-BA ...
Wolk has received grant support from Merck, Biogen, and Eli Lilly/Avid and consultation fees from Neuronix and GE Healthcare and is on the Data and Safety Monitoring Board for a clinical trial run by Functional ...
arXiv:2109.03723v1
fatcat:q3yaojhwizenraacuju455nulu
Reply to Biskup et al. and Tu et al.: Sex differences in metabolic brain aging
2019
Proceedings of the National Academy of Sciences of the United States of America
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machine learning model. ...
with higher AG in females are also those that lose the most AG with age, Pearson's r = 0.72), suggesting that more typical statistical analysis for AG alone reflects the results from our multiparametric ...
machine learning model. ...
doi:10.1073/pnas.1904673116
pmid:31138715
pmcid:PMC6561185
fatcat:hcwoec5pnbde3obarfzd2gsdiy
Understanding neurodegeneration after traumatic brain injury: from mechanisms to clinical trials in dementia
2019
Journal of Neurology, Neurosurgery and Psychiatry
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Traumatic brain injury (TBI) leads to increased rates of dementia, including Alzheimer's disease. The mechanisms by which trauma can trigger neurodegeneration are increasingly understood. ...
Brain atrophy is a key measure of disease progression and can be used to accurately quantify neuronal loss. ...
In disease states, the discrepancy between a 'brain age' estimated using machine learning approaches and a patient's chronological age can be informative. ...
doi:10.1136/jnnp-2017-317557
pmid:31542723
pmcid:PMC6860906
fatcat:rtcpnhjsfjcd5fovxxveavd42i
Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes
[article]
2018
bioRxiv
pre-print
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Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping-morphometry and connectomics-and machine learning ...
Accurate, reliable prediction of risk for Alzheimers disease (AD) is essential for early, disease-modifying therapeutics. ...
Acknowledgments This work used the Extreme Science and Engineering Discovery Environment Stampede 2 at the Texas Advanced Computing Center (TG-IBN170015: Cha) and Argonne National Laboratory Leadership ...
doi:10.1101/407601
fatcat:bskcwpjihzg67nvdr4422vg6ve
Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes
[article]
2018
bioRxiv
pre-print
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Accurate, reliable prediction of risk for Alzheimer's disease (AD) is essential for early, disease-modifying therapeutics. ...
tractography), and machine learning analytics for classification. ...
Acknowledgments This work used the Extreme Science and Engineering Discovery Environment Stampede 2 at the Texas Advanced Computing Center (TG-IBN170015: Cha) and Argonne National Laboratory Leadership ...
doi:10.1101/255141
fatcat:cfvsbvqmxnf27keulvxyt5d6pm
Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease
2020
Frontiers in Neurology
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Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. ...
Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau ...
Finally, PLS has also been used as a feature extractor in a larger machine learning analysis pipeline (20) . ...
doi:10.3389/fneur.2020.00648
pmid:32849173
pmcid:PMC7399334
fatcat:pa57t7k2bjevhjectz74qttt2e
Beyond the average patient: how neuroimaging models can address heterogeneity in dementia
2021
Brain
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Dementia is a highly heterogeneous condition, with pronounced individual differences in onset age, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic ...
However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on the comparison of group average differences (e.g., patient ...
Age is also a key risk factor for pathophysiological changes; disentangling disease-related variation from the ageing process is challenging, for example when differentiating between normal cognitive decline ...
doi:10.1093/brain/awab165
pmid:33892488
pmcid:PMC8634113
fatcat:ncxbd6bbrzdv5ocoklmko6l3sm
Cervical spinal cord atrophy and Alzheimer's disease
[article]
2019
bioRxiv
pre-print
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Methods: 3DT1 images of 31 Alzheimer's disease (AD) and 35 healthy control (HC) subjects were processed to calculate volumes of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical ...
Objective: Brain atrophy is an established biomarker for dementia. We hypothesise that spinal cord atrophy is an important in vivo imaging biomarker for neurodegeneration associated with dementia. ...
Machine learning analysis Classification between AD and HC was performed using a machine learning approach implemented with Orange(https://orange.biolab.si/). ...
doi:10.1101/673350
fatcat:xqgx2kijunhmxhg4wxukbhphpe
Harmonization with Flow-based Causal Inference
[article]
2021
arXiv
pre-print
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A causal model is used to model observed effects (brain magnetic resonance imaging data) that result from known confounders (site, gender and age) and exogenous noise variables. ...
., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize ...
Pratik Chaudhari would like to acknowledge the support of the Amazon Web Services Machine Learning Research Award.
References ...
arXiv:2106.06845v2
fatcat:hildildhc5go3fojq3rjtcjfuy
Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods
2018
Neurobiology of Aging
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Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative crosssectional ...
tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. ...
Acknowledgements This study was supported in part by NIH grant AG014971, the Intramural Research Program, National Institute on Aging, NIH, and NIA contract HHSN2712013000284P to the University of Pennsylvania ...
doi:10.1016/j.neurobiolaging.2018.06.013
pmid:30077821
pmcid:PMC6162110
fatcat:37ev2boqc5andl7mn76pkfy56i
Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes
2022
Journal of Alzheimer's Disease
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Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). ...
Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. ...
memory impairment, Alzheimer's disease (AD)) [1, [5] [6] [7] . ...
doi:10.3233/jad-215289
pmid:35570482
fatcat:lvutechoezedzdeyhdbdv7kry4
Self-Supervised Longitudinal Neighbourhood Embedding
[article]
2021
arXiv
pre-print
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Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. ...
Analyzing this data via machine learning generally requires a large number of ground-truth labels, which are often missing or expensive to obtain. ...
Note, learning a prediction model for normal aging is an emerging approach for understanding structural changes of the human brain and quantifying impact of neurological diseases (e.g. estimating brain ...
arXiv:2103.03840v3
fatcat:7qzi2ziigrcullgnjgkesmyqze
Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain
2021
Frontiers in Neuroinformatics
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Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale ...
Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. ...
INTRODUCTION Every second senior with age above 90 years suffers from Alzheimer's disease (AD) or another dementia (Robinson et al., 2018a) . ...
doi:10.3389/fninf.2021.630172
pmid:33867964
pmcid:PMC8047422
fatcat:khujbqtyxfhwvk66l4azu7hg54
Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration
[article]
2019
bioRxiv
pre-print
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Evaluated on 1969 subjects in the spectrum of late-onset Alzheimer's and Huntington's diseases (from ROSMAP, HBTRC and ADNI studies), this unsupervised machine learning algorithm strongly predicts neuropathological ...
This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. ...
Dataset-1 (ROSMAP) was provided by the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago. ...
doi:10.1101/548974
fatcat:iz7bg2ee7jbbzensoh7yct5mvy
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