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Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning

Gyujoon Hwang, Ahmed Abdulkadir, Guray Erus, Mohamad Habes, Raymond Pomponio, Haochang Shou, Jimit Doshi, Elizabeth Mamourian, Tanweer Rashid, Murat Bilgel, Yong Fan, Aristeidis Sotiras (+9 others)
2022 Brain Communications  
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]

Gyujoon Hwang, Ahmed Abdulkadir, Guray Erus, Mohamad Habes, Raymond Pomponio, Haochang Shou, Jimit Doshi, Elizabeth Mamourian, Tanweer Rashid, Murat Bilgel, Yong Fan, Aristeidis Sotiras (+9 others)
2021 arXiv   pre-print
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

Manu S. Goyal, Andrei G. Vlassenko, Marcus E. Raichle
2019 Proceedings of the National Academy of Sciences of the United States of America  
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

Neil SN Graham, David J Sharp
2019 Journal of Neurology, Neurosurgery and Psychiatry  
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]

Yun Wang, Chenxiao Xu, Ji-Hwan Park, Seonjoo Lee, Yaakov Stern, Shinjae Yoo, Jong Hun Kim, Hyoung Seop Kim, Jiook Cha
2018 bioRxiv   pre-print
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]

Yun Wang, Chenxiao Xu, Ji-Hwan Park, Seonjoo Lee, Yaakov Stern, Jong Hun Kim, Shinjae Yoo, Hyoung Seop Kim, Jiook Cha
2018 bioRxiv   pre-print
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

Adrià Casamitjana, Paula Petrone, José Luis Molinuevo, Juan Domingo Gispert, Verónica Vilaplana
2020 Frontiers in Neurology  
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

Serena Verdi, Andre F Marquand, Jonathan M Schott, James H Cole
2021 Brain  
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]

Roberta Lorenzi, Fulvia Palesi, Gloria Castellazzi, Paolo Vitali, Nicoletta Anzalone, Sara Bernini, Elena Sinforiani, Giuseppe Micieli, Alfredo Costa, Egidio Ugo D'Angelo, Claudia A.M. Gandini Wheeler-Kingshott
2019 bioRxiv   pre-print
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]

Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
2021 arXiv   pre-print
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

Harini Eavani, Mohamad Habes, Theodore D. Satterthwaite, Yang An, Meng-Kang Hsieh, Nicolas Honnorat, Guray Erus, Jimit Doshi, Luigi Ferrucci, Lori L. Beason-Held, Susan M. Resnick, Christos Davatzikos
2018 Neurobiology of Aging  
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

Shannon M. Drouin, G. Peggy McFall, Olivier Potvin, Pierre Bellec, Mario Masellis, Simon Duchesne, Roger A. Dixon, for the Alzheimer's Disease Neuroimaging Initiative, Susan Resnick
2022 Journal of Alzheimer's Disease  
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]

Jiahong Ouyang and Qingyu Zhao and Ehsan Adeli and Edith V Sullivan and Adolf Pfefferbaum and Greg Zaharchuk and Kilian M Pohl
2021 arXiv   pre-print
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

Leon Stefanovski, Jil Mona Meier, Roopa Kalsank Pai, Paul Triebkorn, Tristram Lett, Leon Martin, Konstantin Bülau, Martin Hofmann-Apitius, Ana Solodkin, Anthony Randal McIntosh, Petra Ritter
2021 Frontiers in Neuroinformatics  
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]

Yasser Iturria-Medina, Ahmed F. Khan, Quadri Adewale
2019 bioRxiv   pre-print
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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