Apathy in Alzheimer’s Disease

2021 ◽  
pp. 39-54
Author(s):  
Danielle Vieira ◽  
Celina Liu ◽  
Krista L. Lanctôt
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Social Interactions ◽  
Treatment Response ◽  
Diagnostic Criteria ◽  
Neuropsychiatric Symptom ◽  
Future Research ◽  
Activity Loss ◽  
Predict Treatment Response ◽  
Neuroimaging Techniques

Apathy is characterized by diminished goal-directed activity, loss of emotion, and reduced engagement in social interactions. It is a common neuropsychiatric symptom in Alzheimer’s disease (AD) and is present throughout every stage of the disease. This chapter comprehensively discusses the prevalence of apathy, its impact on patient and caregiver outcomes, the diagnostic criteria and validated scales used in the assessment of apathy, the neurobiology of this neuropsychiatric symptom, and findings from studies investigating the treatment of apathy in AD. Recommendations for future research include validating apathy scales to identify and measure apathy in AD, and consideration of neuroimaging techniques and biomarker analyses to better predict treatment response.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

The Modulatory Effect of Sex on the Association between APOE and Neuropsychiatric Symptom Burden in Older Adults at Risk for Alzheimer's Disease.

2021 ◽  
Vol 29 (4) ◽  
pp. S51
Author(s):  
Andrew Dissanayake ◽  
Cristopher R. Bowie ◽  
Meryl A. Butters ◽  
Alastair Flint ◽  
Damien Gallagher ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Symptom Burden ◽  
Neuropsychiatric Symptom

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

2016 ◽  
Vol 113 (42) ◽  
pp. E6535-E6544 ◽  
Author(s):  
Xiuming Zhang ◽  
Elizabeth C. Mormino ◽  
Nanbo Sun ◽  
Reisa A. Sperling ◽  
Mert R. Sabuncu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Executive Function ◽  
Bayesian Model ◽  
Late Onset ◽  
Temporal Cortex ◽  
Cortical Atrophy ◽  
Future Research ◽  
Dementia Patients ◽  
With Memory

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

scholarly journals Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer’s Disease: A Review

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jessica Beltrán ◽  
Mireya S. García-Vázquez ◽  
Jenny Benois-Pineau ◽  
Luis Miguel Gutierrez-Robledo ◽  
Jean-François Dartigues
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Movements ◽  
Early Diagnosis ◽  
Future Research ◽  
Computational Techniques ◽  
Research Directions ◽  
Future Research Directions ◽  
Tools And Techniques

An opportune early diagnosis of Alzheimer’s disease (AD) would help to overcome symptoms and improve the quality of life for AD patients. Research studies have identified early manifestations of AD that occur years before the diagnosis. For instance, eye movements of people with AD in different tasks differ from eye movements of control subjects. In this review, we present a summary and evolution of research approaches that use eye tracking technology and computational analysis to measure and compare eye movements under different tasks and experiments. Furthermore, this review is targeted to the feasibility of pioneer work on developing computational tools and techniques to analyze eye movements under naturalistic scenarios. We describe the progress in technology that can enhance the analysis of eye movements everywhere while subjects perform their daily activities and give future research directions to develop tools to support early AD diagnosis through analysis of eye movements.

scholarly journals Specific pattern of melanin-concentrating hormone (MCH) neuron degeneration in Alzheimer’s disease and possible clinical implications

2021 ◽  
Author(s):  
Mihovil Mladinov ◽  
Jun Yeop Oh ◽  
Cathrine Petersen ◽  
Rana Eser ◽  
Song Hua Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Progressive Supranuclear Palsy ◽  
Functional Relation ◽  
Lateral Hypothalamic Area ◽  
Future Research ◽  
Hypothalamic Area ◽  
Lateral Hypothalamic ◽  
New Findings ◽  
Melanin Concentrating Hormone

ABSTRACTStudy ObjectivesThe lateral hypothalamic area (LHA) is one of the key regions orchestrating sleep and wake control. It is the site of wake-promoting orexinergic and sleep-promoting melanin-concentrating hormone (MCH) neurons, which share a close anatomical and functional relation. The aim of the study was to investigate the degeneration of MCH neurons in Alzheimer’s disease (AD) and progressive supranuclear palsy (PSP), and relate the new findings to our previously reported pattern of degeneration of wake-promoting orexinergic neuronsMethodsPost-mortem human brain tissue of subjects with AD, PSP and controls was examined using unbiased stereology. Double immunohistochemistry with MCH- and tau-antibodies on formalin-fixed, celloidin embedded tissue was performed.ResultsThere was no difference in the total number of MCH neurons between AD, PSP and controls, but a significant loss of non-MCH neurons in AD patients (p=0.019). The proportion of MCH neurons was significantly higher in AD (p=0.0047). No such a difference was found in PSP. In PSP, but not AD, the proportion of tau+ MCH neurons was lower than the proportion of tau+ non-MCH neurons (p=0.002). When comparing AD to PSP, the proportion of tau+MCH neurons was higher in AD (p<0.001).ConclusionsMCH neurons are more vulnerable to AD than PSP pathology. High burden of tau-inclusions, but comparably milder loss of MCH neurons in AD, together with previously reported orexinergic neuronal loss may lead to a hyperexcitability of the MCH system in AD, contributing to wake-sleep disorders in AD. Further experimental research is needed to understand why MCH neurons are more resistant to tau-toxicity compared to orexinergic neurons.STATEMENT OF SIGNIFICANCEThis is the first study to investigate the involvement of melanin-concentrating hormone (MCH) neurons in patients with Alzheimer’s disease and progressive supranuclear palsy. MCH neurons are key regulators of sleep and metabolic functions, and one of the major neuronal populations of the lateral hypothalamic area (LHA), but still underexplored in humans. Uncovering the pathology of this neuronal population in neurodegenerative disorders will improve our understanding of the complex neurobiology of the LHA and the interaction between MCH and orexinergic neurons. This new knowledge may open new strategies for treatment interventions. Further, this study represents a fundament for future research on MCH neurons and the LHA in tauopathies.

scholarly journals Working Memory and Executive Function Decline across Normal Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Anna-Mariya Kirova ◽  
Rebecca B. Bays ◽  
Sarita Lagalwar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Working Memory ◽  
Cognitive Impairment ◽  
Executive Function ◽  
Mild Cognitive Impairment ◽  
Executive Dysfunction ◽  
Normal Aging ◽  
Future Research ◽  
Neurological Differences

Alzheimer’s disease (AD) is a progressive neurodegenerative disease marked by deficits in episodic memory, working memory (WM), and executive function. Examples of executive dysfunction in AD include poor selective and divided attention, failed inhibition of interfering stimuli, and poor manipulation skills. Although episodic deficits during disease progression have been widely studied and are the benchmark of a probable AD diagnosis, more recent research has investigated WM and executive function decline during mild cognitive impairment (MCI), also referred to as the preclinical stage of AD. MCI is a critical period during which cognitive restructuring and neuroplasticity such as compensation still occur; therefore, cognitive therapies could have a beneficial effect on decreasing the likelihood of AD progression during MCI. Monitoring performance on working memory and executive function tasks to track cognitive function may signal progression from normal cognition to MCI to AD. The present review tracks WM decline through normal aging, MCI, and AD to highlight the behavioral and neurological differences that distinguish these three stages in an effort to guide future research on MCI diagnosis, cognitive therapy, and AD prevention.

Preclinical Alzheimer's disease

2014 ◽  
Vol 24 (2) ◽  
pp. 117-121
Author(s):  
P Gil-Gregorio ◽  
R Yubero-Pancorbo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Diagnostic Criteria ◽  
Stage I ◽  
Disease Stage ◽  
Synaptic Dysfunction ◽  
Preclinical Alzheimer’S Disease ◽  
Cerebral Amyloidosis ◽  
Three Stages

SummaryRecently, diagnostic criteria for preclinical Alzheimer's disease have been proposed. These describe and define three stages of disease. Stage I is focused on asymptomatic cerebral amyloidosis. Stage II includes evidence of synaptic dysfunction and/or early degeneration. Finally, stage III of the disease is characterized by the beginning of cognitive decline.

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