scholarly journals Cognitive Theory of Mind Influences Destination Memory: Evidence from Normal Aging and Alzheimer’s Disease

2019 ◽  
Vol 34 (8) ◽  
pp. 1409-1417 ◽  
Author(s):  
Mohamad El Haj ◽  
Philippe Allain ◽  
Ahmed A Moustafa
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Theory Of Mind ◽  
Beneficial Effect ◽  
Cognitive Theory ◽  
Normal Aging ◽  
Future Research ◽  
Destination Memory ◽  
Social Abilities ◽  
Individual Profiles

AbstractObjectiveTheory of mind and destination memory are social abilities that require processing the attributes of interlocutors. Empirical research has demonstrated a relationship between performance on both abilities in normal aging and Alzheimer’s disease (AD). We therefore investigated whether processing attributes of interlocutors would result in better destination memory in AD.MethodsTwenty-six mild AD participants and 28 controls were tested on two occasions. On the first one, participants had to tell proverbs to celebrities’ faces. Following that, they decided whether they previously told that proverb to that celebrity or not. The same procedures were repeated on the second occasion; however, after telling the proverbs, participants had to introspect about what the celebrities might think about the proverbs (e.g., “what do you think that the celebrities would think about the proverbs?”).ResultsGroup comparisons showed a beneficial effect of introspection on destination memory in controls (Z = −2.57, p < .05) but not in AD participants (Z = −1.05, p = .29). However, analyzes of individual profiles demonstrated that 15 AD participants demonstrated better destination memory after introspection.ConclusionsOur findings show a beneficial effect of introspection on destination memory in normal aging, and at least in some mild AD cases. Future research should investigate the influence of social cognition on memory in AD and how introspection may provide a potential treatment for AD.

Destination Memory and Cognitive Theory of Mind in Alzheimer’s Disease

2015 ◽  
Vol 48 (2) ◽  
pp. 529-536 ◽  
Author(s):  
Mohamad El Haj ◽  
Marie-Christine Gély-Nargeot ◽  
Stéphane Raffard
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Theory Of Mind ◽  
Cognitive Theory ◽  
Destination Memory

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.

Are Deficits in Social Cognition Linked to Autobiographical Memory in Patients with Alzheimer’s Disease?

2016 ◽  
Vol 27 (4) ◽  
pp. 257-264 ◽  
Author(s):  
Johannes H. Scheidemann ◽  
Franz Petermann ◽  
Marc Schipper
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Social Cognition ◽  
Theory Of Mind ◽  
Autobiographical Memory ◽  
Positive Relationship ◽  
Neuropsychological Test ◽  
Clinical Group ◽  
Neuropsychological Test Battery ◽  
Fluency Task

Abstract. We investigated theory of mind (ToM) deficits in Alzheimer‘s disease (AD) and its possible connection to autobiographical memory (ABM). Patients and matched controls were evaluated and compared using a video-based ToM test, an autobiographical fluency task, and a neuropsychological test battery. We found that ToM deficits were positively associated with semantic ABM in the clinical group, whereas a positive relationship appeared between ToM and episodic ABM in controls. We hypothesize that this reflects the course of the disease as well as that semantic ABM is used for ToM processing, being still accessible in AD. Furthermore, we assume that it is also less efficient, which in turn leads to a specific deficit profile of social cognition.

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.

Destination memory in Alzheimer’s Disease: When I imagine telling Ronald Reagan about Paris

Cortex ◽  
2013 ◽  
Vol 49 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Mohamad El Haj ◽  
Virginie Postal ◽  
Philippe Allain
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ronald Reagan ◽  
Destination Memory

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.

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