scholarly journals A Narrative Review of Alzheimer’s Disease Stigma

2020 ◽  
Vol 78 (2) ◽  
pp. 515-528
Author(s):  
Eric R. Rosin ◽  
Drew Blasco ◽  
Alexander R. Pilozzi ◽  
Lawrence H. Yang ◽  
Xudong Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Senile Dementia ◽  
Narrative Review ◽  
Future Research ◽  
Review Of The Literature ◽  
Definition Of ◽  
New Framework ◽  
The Way

As the most common form of senile dementia, Alzheimer’s disease (AD) is accompanied by a great deal of uncertainty which can lead to fear and stigma for those identified with this devastating disease. As the AD definition evolves from a syndromal to a biological construct, and early diagnoses becomes more commonplace, more confusion and stigma may result. We conducted a narrative review of the literature on AD stigma to consolidate information on this body of research. From the perspective of several stigma theories, we identified relevant studies to inform our understanding of the way in which implementation of the new framework for a biological based AD diagnosis may have resulted in new and emerging stigma. Herein, we discuss the emergence of new AD stigma as our understanding of the definition of the disease changes. We further propose recommendations for future research to reduce the stigma associated with AD.

scholarly journals A Systematic Review of Single Chinese Herbs for Alzheimer's Disease Treatment

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Li-Min Fu ◽  
Ju-Tzu Li
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quality Assessment ◽  
Senile Dementia ◽  
Mechanisms Of Action ◽  
Huperzine A ◽  
Chinese Herbs ◽  
Current Evidence ◽  
Future Research

The objectives here are to provide a systematic review of the current evidence concerning the use of Chinese herbs in the treatment of Alzheimer's disease (AD) and to understand their mechanisms of action with respect to the pathophysiology of the disease. AD, characterized microscopically by deposition of amyloid plaques and formation of neurofibrillary tangles in the brain, has become the most common cause of senile dementia. The limitations of western medications have led us to explore herbal medicine. In particular, many Chinese herbs have demonstrated some interesting therapeutic properties. The following databases were searched from their inception: MEDLINE (PUBMED), ALT HEALTH WATCH (EBSCO), CINAH and Cochrane Central. Only single Chinese herbs are included. Two reviewers independently extracted the data and performed quality assessment. The quality assessment of a clinical trial is based on theJadadcriteria. Seven Chinese herbs and six randomized controlled clinical trials were identified under the predefined criteria.Ginkgo biloba, Huperzine A (Lycopodium serratum) and Ginseng have been assessed for their clinical efficacy with limited favorable evidence. No serious adverse events were reported. Chinese herbs show promise in the treatment of AD in terms of their cognitive benefits and more importantly, their mechanisms of action that deal with the fundamental pathophysiology of the disease. However, the current evidence in support of their use is inconclusive or inadequate. Future research should place emphasis on herbs that can treat the root of the disease.

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.

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.

A narrative review on the potential of tomato and lycopene for the prevention of Alzheimer’s disease and other dementias

2021 ◽  
pp. 1-12
Author(s):  
Federica Ratto ◽  
Flaminia Franchini ◽  
Massimo Musicco ◽  
Giulia Caruso ◽  
Simona Gabriella Di Santo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Narrative Review

A Detailed Phenomenological Comparison of Complex Visual Hallucinations in Dementia With Lewy Bodies and Alzheimer's Disease

1997 ◽  
Vol 9 (4) ◽  
pp. 381-388 ◽  
Author(s):  
Clive Ballard ◽  
Ian McKeith ◽  
Richard Harrison ◽  
John O'Brien ◽  
Peter Thompson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Time Of Day ◽  
Visual Hallucinations ◽  
Dementia Patients ◽  
Core Feature ◽  
Definition Of

Visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), but little is known about their phenomenology. A total of 73 dementia patients (42 DLB, 30 Alzheimer's disease [AD], 1 undiagnosed) in contact with clinical services were assessed with a detailed standardized inventory. DLB was diagnosed according to the criteria of McKeith and colleagues, AD was diagnosed using the NINCDS-ADRDA criteria. Autopsy confirmation has been obtained when possible. VH were defined using the definition of Burns and colleagues. Detailed descriptions of hallucinatory experiences were recorded. Annual follow-up interviews were undertaken. The clinical diagnosis has been confirmed in 18 of the 19 cases that have come to autopsy. A total of 93% of DLB patients and 27% of AD patients experienced VH. DLB patients were significantly more likely to experience multiple VH that persisted over follow-up. They were significantly more likely to hear their VH speak but there were no significant differences in the other phenomenological characteristics including whether the hallucinations moved, the time of day that they were experienced, their size, the degree of insight, and whether they were complete. VH may be more likely to be multiple, to speak, and to be persistent in DLB patients. These characteristics could potentially aid accurate diagnosis.

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.

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