Medicine and Health Sciences Commons^™

An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma

MSU Graduate Theses

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …

Leukocyte Surface Biomarkers Implicate Deficits Of Innate Immunity In Sporadic Alzheimer's Disease, Xin Huang, Yihan Li, Christopher Fowler, James D. Doecke, Yen Ying Lim, Candace Drysdale, Vicky Zhang, Keunha Park, Brett Trounson, Kelly Pertile, Rebecca Rumble, John W. Pickering, Robert A. Rissman, Floyd Sarsoza, Sara Abdel-Latif, Yong Lin, Vincent Doré, Victor Villemagne, Christopher C. Rowe, Jurgen Fripp, Ralph Martins, James S. Wiley, Paul Maruff, Jacobo E. Mintzer, Colin L. Masters, Ben J. Gu

Research outputs 2022 to 2026

Introduction:

Blood-based diagnostics and prognostics in sporadic Alzheimer's disease (AD) are important for identifying at-risk individuals for therapeutic interventions.

Methods:

In three stages, a total of 34 leukocyte antigens were examined by flow cytometry immunophenotyping. Data were analyzed by logistic regression and receiver operating characteristic (ROC) analyses.

Results:

We identified leukocyte markers differentially expressed in the patients with AD. Pathway analysis revealed a complex network involving upregulation of complement inhibition and downregulation of cargo receptor activity and Aβ clearance. A proposed panel including four leukocyte markers – CD11c, CD59, CD91, and CD163 – predicts patients’ PET Aβ status with an …

Alzheimer’S And Patient Caregiver Burnout: A Comprehensive Review Of The Literature, Madeline J. Hekeler

Senior Honors Projects, 2020-current

The term ‘silent epidemic’ has become fitting for Alzheimer’s disease, as it is now the sixth leading cause of death in the US. Caring for AD patients at home in the US costs billions of dollars each year. The current comprehensive literature review discusses the background/history of AD, pathology and modes of transmission of AD, behavioral and natural risk factors, prevention and treatment options, and how the aforementioned factors contribute to caregiver burnout and subsequently affect the AD patient. The extensive examination of the literature determined several gaps to be addressed. More specifically, burnout among AD caregivers has become an …

Investigating Diffusion Tensor Imaging Correlates Of Cognitive Impairment In Idiopathic Normal Pressure Hydrocephalus And Alzheimer's Disease, Omar Hasan, Omar Hasan

Dissertations & Theses (Open Access)

Modest expansion of the human brain cerebrospinal fluid (CSF)-filled ventricles is normal with aging, and because of this, it can be difficult for physicians to accurately diagnose and treat enlarged ventricles (ventriculomegaly), called hydrocephalus¹ (fluid or water in the brain) Ventriculomegaly occurs due to an obstruction (such as a blood clot or tumor), or a change in CSF absorption². Primary hydrocephalus, also called idiopathic normal pressure hydrocephalus (iNPH), is non-obstructive and may be comorbid with other neurodegenerative diseases such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD). Clinically, it can be difficult to tell whether the pathophysiological …

Early Diagnosis Of Alzheimer’S Disease In The Primary Care Setting, Raymond R. Romano

Theses and Dissertations (ETD)

The burden of Alzheimer’s disease (AD) affects not just the individual but also families, providers, and society. Early recognition and diagnosis of AD may reduce cost by reducing interaction with the health care system, earlier initiation of treatment, and prolonging time to long- term care. Primary care providers, the first contact for diagnosis of patients with AD, are not fulfilling the potential of early diagnosis for a variety of reasons. Biomarkers of AD emerge on average 15 to 20 years before clinical diagnosis, yet currently established biomarkers are not easily available in the primary care setting. A growing body of …

Nmd-12: A New Machine-Learning Derived Screening Instrument To Detect Mild Cognitive Impairment And Dementia, Pai-Yi Chiu, Haipeng Tang, Cheng-Yu Wei, Chaoyang Zhang, Guang-Uei Hung, Weihua Zhou

Faculty Publications

Introduction

Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.

Methods

With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in …

Cascaded Multi-View Canonical Correlation (Camcco) For Early Diagnosis Of Alzheimer's Disease Via Fusion Of Clinical, Imaging And Omic Features, Asha Singanamalli, Haibo Wang, Anant Madabhushi, Michael Weiner, Paul Aisen, Ronald Petersen, Clifford Jack, William Jagust, John Trojanowki, Arthur Toga, Laurel Beckett, Robert Green, Andrew Saykin, John Morris, Leslie Shaw, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith Heidebrink, Charles Smith, Greg A. Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer's Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic …

Longitudinal Measurement And Hierarchical Classification Framework For The Prediction Of Alzheimer's Disease, Meiyan Huang, Wei Yang, Qianjin Feng, Wufan Chen, Michael Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack Jr., William Jagust, John Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew Saykin, John Morris, Leslie M. Shaw, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Charles D. Smith, Greg A. Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important …