Apraxia and Alzheimer’s Disease: Review and Perspectives

Apraxia is one of the cognitive deficits that characterizes Alzheimer\u27s disease. Despite its prevalence and relevance to diagnosing Alzheimer\u27s disease, this topic has received little attention and is without comprehensive review. The review herein is aimed to fill this gap by first presenting an overview of the impairment caused in different clinical situations: pantomime of tool use, single tool use, real tool use, mechanical problem solving, function and manipulation knowledge tasks, and symbolic/meaningless gestures. On the basis of these results, we then propose alternative interpretations regarding the nature of the underlying mechanisms impaired by the disease. Also presented are principal methodological issues precluding firm conclusions from being drawn

Apraxie et maladie d’Alzheimer : revue et perspectives

Alzheimer’s disease is characterized by the progressive impairment of cognitive functions. Whereas the study of amnesia, aphasia, agnosia and dysexecutive impairments to a lesser extent has been well documented, apraxia has received little attention [1]. The aim of this review is to fill this gap by presenting an overview of the praxis impairment, which typically appears in the course of the disease. This review focuses on transitive gestures (i.e., tool use tasks) and intransitive gestures (i.e., symbolic and meaningless). On the basis of these results, we propose interpretations as to the nature of the underlying mechanisms impaired by the disease. Finally, we provide some answers to help clinicians to better understand and assess the apraxic disorders in Alzheimer’s disease

Tool use in left brain damage and Alzheimer's disease: What about function and manipulation knowledge?

Tool use disorders are usually associated with difficulties in retrieving function and manipulation knowledge. Here, we investigate tool use (Real Tool Use, RTU), function (Functional Association, FA) and manipulation knowledge (Gesture Recognition, GR) in 17 left-brain-damaged (LBD) patients and 14 AD patients (Alzheimer disease). LBD group exhibited predicted deficit on RTU but not on FA and GR while AD patients showed deficits on GR and FA with preserved tool use skills. These findings question the role played by function and manipulation knowledge in actual tool use

Rethinking the Cognitive Mechanisms Underlying Pantomime of Tool Use: Evidence from Alzheimer's Disease and Semantic Dementia

OBJECTIVES: Pantomiming the use of familiar tools is a central test in the assessment of apraxia. However, surprisingly, the nature of the underlying cognitive mechanisms remains an unresolved issue. The aim of this study is to shed a new light on this issue by exploring the role of functional, mechanical, and manipulation knowledge in patients with Alzheimer\u27s disease and semantic dementia and apraxia of tool use. METHODS: We performed multiple regression analyses with the global performance and the nature of errors (i.e., production and conception) made during a pantomime of tool use task in patients and control participants as dependent variables and tasks investigating functional, mechanical, and manipulation knowledge as predictors. RESULTS: We found that mechanical problem solving, assessing mechanical knowledge, was a good predictor of the global performance of pantomime of tool use. We also found that occurrence of conception errors was robustly predicted by the task assessing functional knowledge whereas that of production errors was not explained by only one predictor. CONCLUSIONS: Our results suggest that both functional and mechanical knowledge are important to pantomime the use of tools. To our knowledge, this is the first demonstration that mechanical knowledge plays a role in pantomime of tool use. Although impairment in pantomime of tool use tasks (i.e., apraxia) is widely explained by the disruption of manipulation knowledge, we propose that pantomime of tool use is a complex problem-solving task. (JINS, 2017, 23, 128-138)

Posterior cortical atrophy and Alzheimer’s disease : a meta-analytic review of neuropsychological and brain morphometry studies

This paper presents the first systematic review and meta-analysis of neuropsychological and brain morphometry studies comparing posterior cortical atrophy (PCA) to typical Alzheimer's disease (tAD). Literature searches were conducted for brain morphometry and neuropsychological studies including a PCA and a tAD group. Compared to healthy controls (HC), PCA patients exhibited significant decreases in temporal, occipital and parietal gray matter (GM) volumes, whereas tAD patients showed extensive left temporal atrophy. Compared to tAD patients, participants with PCA showed greater GM volume reduction in the right occipital gyrus extending to the posterior lobule. In addition, PCA patients showed less GM volume loss in the left parahippocampal gyrus and left hippocampus than tAD patients. PCA patients exhibit significantly greater impairment in Immediate Visuospatial Memory as well as Visuoperceptual and Visuospatial Abilities than patients with tAD. However, tAD patients showed greater impairment in Delayed Auditory/Verbal Memory than patients with PCA. PCA is characterized by significant atrophy of the occipital and parietal regions and severe impairments in visuospatial functioning.JA is funded by a doctoral grant from the Foundation for Science and Technology, FCT (SFRH/BD/64457/2009, co-funded by FSE/POPH). JA and AS are funded by project PIC/IC/83290/2007, which is supported by FEDER (POFC-COMPETE) and FCT. JMS is supported by a fellowship of the project SwitchBox-FP7-HEALTH-2010-grant 259772-2. These organizations had no role in the study design, data collection, analysis, interpretation, or in the decision to submit the paper for publication

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD