Tweeting on dementia: A snapshot of the content and sentiment of tweets associated with dementia

First Monday ◽  
2021 ◽  
Author(s):  
David Robertshaw ◽  
Ivana Babicova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lewy Body Dementia ◽  
Future Research ◽  
Search Term ◽  
Word Count ◽  
Emotional Tone ◽  
Search Terms ◽  
Linguistic Inquiry ◽  
World List

This study aimed to record and characterise tweets related to dementia, to investigate their content and sentiment. Data were extracted from Twitter over a period of six weeks during February and March 2019 and then analysed using Linguistic Inquiry and Word Count (LIWC) and AntWordProfiler. Using five search terms related to dementia, this study collected 860,383 tweets (more than 27 million words). Results have shown that out of all the collected tweets, 48.63 percent of tweets related to the search term ‘dementia’, 49.95 percent to ‘Alzheimer’s disease’ and the remainder related to frontotemporal dementia, Lewy Body dementia and vascular dementia. People wrote more positively and personally about the term ‘dementia’ than the other terms, and more technically regarding the term ‘Alzheimer’s disease’. All search terms had a negative emotional tone overall. Dementia and related terms are commonly discussed on Twitter. The overall negative emotional tone associated with all dementia related search terms suggests that dementia is still largely stigmatised and talked about negatively. Recommendations for future research include the development of a health world list or a dementia world list, and to consider how the results of this research inform social change interventions going forwards.

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.

scholarly journals Repetitive Transcranial Magnetic Stimulation in the Treatment of Alzheimer’s Disease and Other Dementias

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 949
Author(s):  
Athina-Maria Aloizou ◽  
Georgia Pateraki ◽  
Konstantinos Anargyros ◽  
Vasileios Siokas ◽  
Christos Bakirtzis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Lewy Body Dementia ◽  
Standard Of Care ◽  
Central Position ◽  
Beneficial Effects ◽  
Dorsolateral Prefrontal

Dementia is a debilitating impairment of cognitive functions that affects millions of people worldwide. There are several diseases belonging to the dementia spectrum, most prominently Alzheimer’s disease (AD), vascular dementia (VD), Lewy body dementia (LBD) and frontotemporal dementia (FTD). Repetitive transcranial magnetic stimulation (rTMS) is a safe, non-invasive form of brain stimulation that utilizes a magnetic coil to generate an electrical field and induce numerous changes in the brain. It is considered efficacious for the treatment of various neuropsychiatric disorders. In this paper, we review the available studies involving rTMS in the treatment of these dementia types. The majority of studies have involved AD and shown beneficial effects, either as a standalone, or as an add-on to standard-of-care pharmacological treatment and cognitive training. The dorsolateral prefrontal cortex seems to hold a central position in the applied protocols, but several parameters still need to be defined. In addition, rTMS has shown potential in mild cognitive impairment as well. Regarding the remaining dementias, research is still at preliminary phases, and large, randomized studies are currently lacking.

scholarly journals Functional connectivity of the nucleus basalis of Meynert in Lewy body dementia and Alzheimer’s disease

2021 ◽  
pp. 1-6
Author(s):  
Julia Schumacher ◽  
Alan J. Thomas ◽  
Luis R. Peraza ◽  
Michael Firbank ◽  
John T. O’Brien ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Lewy Body ◽  
Cholinergic System ◽  
Structural Changes ◽  
Lewy Body Dementia ◽  
Occipital Cortex ◽  
Nucleus Basalis ◽  
Nucleus Basalis Of Meynert

ABSTRACT Cholinergic deficits are a hallmark of Alzheimer’s disease (AD) and Lewy body dementia (LBD). The nucleus basalis of Meynert (NBM) provides the major source of cortical cholinergic input; studying its functional connectivity might, therefore, provide a tool for probing the cholinergic system and its degeneration in neurodegenerative diseases. Forty-six LBD patients, 29 AD patients, and 31 healthy age-matched controls underwent resting-state functional magnetic resonance imaging (fMRI). A seed-based analysis was applied with seeds in the left and right NBM to assess functional connectivity between the NBM and the rest of the brain. We found a shift from anticorrelation in controls to positive correlations in LBD between the right/left NBM and clusters in right/left occipital cortex. Our results indicate that there is an imbalance in functional connectivity between the NBM and primary visual areas in LBD, which provides new insights into alterations within a part of the corticopetal cholinergic system that go beyond structural changes.

scholarly journals The individual course of neuropsychiatric symptoms in people with Alzheimer's and Lewy body dementia: 12-year longitudinal cohort study

2019 ◽  
Vol 216 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Audun Osland Vik-Mo ◽  
Lasse Melvaer Giil ◽  
Miguel Germán Borda ◽  
Clive Ballard ◽  
Dag Aarsland
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neuropsychiatric Symptoms ◽  
Personalised Medicine ◽  
Lewy Bodies ◽  
Lewy Body Dementia ◽  
Psychotic Symptoms ◽  
Primary Inclusion ◽  
The Individual

IntroductionUnderstanding the natural course of neuropsychiatric symptoms (NPS) in dementia is important for planning patient care and trial design, but few studies have described the long-term course of NPS in individuals.MethodPrimary inclusion of 223 patients with suspected mild dementia from general practice were followed by annual assessment, including the Neuropsychiatric Inventory (NPI), for up to 12 years. Total and item NPI scores were classified as stable, relapsing, single episodic or not present based on 4.96 (s.d. 2.3) observations (98% completeness of longitudinal data) for 113 patients with Alzheimer's disease and 84 patients with LBD (68 dementia with Lewy bodies and 16 Parkinson's disease dementia).ResultsWe found that 80% had stable NPI total ≥1, 50% had stable modest NPI total ≥12 and 25% had stable NPI total ≥24 scores. Very severe NPS (≥48) were mostly single episodes, but 8% of patients with Alzheimer's disease had stable severe NPS. Patients with Alzheimer's disease and the highest 20% NPI total scores had a more stable or relapsing course of four key symptoms: aberrant motor behaviour, aggression/agitation, delusions and irritability (odds ratio 55, P < 0.001). This was not seen in LBD. Finally, 57% of patients with Alzheimer's disease and 84% of patients with LBD had reoccurring psychotic symptoms.ConclusionsWe observed a highly individual course of NPS, with most presenting as a single episode or relapsing; a stable course was less common, especially in LBD. These findings demonstrate the importance of an individualised approach (i.e. personalised medicine) in dementia care.

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 Alzheimer's Disease–Related Dementias Summit 2016: National research priorities

Neurology ◽  
2017 ◽  
Vol 89 (23) ◽  
pp. 2381-2391 ◽  
Author(s):  
Roderick A. Corriveau ◽  
Walter J. Koroshetz ◽  
Jordan T. Gladman ◽  
Sophia Jeon ◽  
Debra Babcock ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Alzheimer Disease ◽  
Scientific Progress ◽  
Research Priorities ◽  
Lewy Body Dementia ◽  
National Plan ◽  
Relevant Research ◽  
Challenges And Opportunities ◽  
Dementia Research

Goal 1 of the National Plan to Address Alzheimer’s Disease is to prevent and effectively treat Alzheimer disease and Alzheimer disease–related dementias by 2025. To help inform the research agenda toward achieving this goal, the NIH hosts periodic summits that set and refine relevant research priorities for the subsequent 5 to 10 years. This proceedings article summarizes the 2016 Alzheimer's Disease–Related Dementias Summit, including discussion of scientific progress, challenges, and opportunities in major areas of dementia research, including mixed-etiology dementias, Lewy body dementia, frontotemporal degeneration, vascular contributions to cognitive impairment and dementia, dementia disparities, and dementia nomenclature.

scholarly journals Benzodiazepines and antidepressants: effects on cognitive and functional decline in Alzheimer’s disease and Lewy body dementia

2020 ◽  
Author(s):  
Miguel Germán Borda ◽  
Alberto Jaramillo‐Jimenez ◽  
Ragnhild Oesterhus ◽  
Jose Manuel Santacruz ◽  
Diego Alejandro Tovar‐Rios ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lewy Body ◽  
Functional Decline ◽  
Lewy Body Dementia
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