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

Decreased sAβPPβ, Aβ38, and Aβ40 Cerebrospinal Fluid Levels in Frontotemporal Dementia.

International audienceTo improve the etiological diagnosis of neurodegenerative dementias like Alzheimer's disease (AD) or frontotemporal dementia (FTD), we evaluated the value of individual and combined measurements of the following relevant cerebrospinal fluid (CSF) biomarkers: Tau, 181p-Tau, Aβ38, Aβ40, Aβ42, sAβPPα, and sAβPPβ. This study conducted in two centers included patients with FTD (n = 34), AD (n = 52), as well as a control group of persons without dementia (CTRL, n = 42). Identical clinical criteria and pre-analytical conditions were used while CSF biomarkers were measured using commercial single and multiplex quantitative immunoassays. Thorough statistical analyses, including ROC curves, logistic regressions, and decision trees, were performed. We validated in AD the specific increase of p-Tau levels and the decrease of Aβ42 levels, two biological hallmarks of this disease. Tau concentrations were highest in AD and intermediate in FTD when compared to CTRL. The most interesting results were obtained by focusing on amyloid biomarkers as we found out in FTD a significant decrease of sAβPPβ, Aβ38, and Aβ40 levels. Aβ38 in particular was the most useful biomarker to differentiate FTD subjects from the CTRL population. Combining p-Tau and Aβ38 led us to correctly classifying FTD patients with sensitivity at 85% and specificity at 82%. Significant changes in amyloid biomarkers, particularly for Aβ38, are therefore seen in FTD. This could be quite useful for diagnosis purposes and it might provide additional evidence on the interrelationship between Tau and AβPP biology which understanding is essential to progress towards optimal therapeutic and diagnostic approaches of dementia

Correlations between soluble alpha/beta forms of amyloid precursor protein and Abeta38, 40 and 42 in human cerebrospinal fluid

International audienceCerebrospinal fluid (CSF) biomarkers are now widely used for diagnosis of Alzheimer disease (AD) in atypical clinical forms, for differential and early diagnosis, or for stratification of patients in clinical trials. Among these biomarkers, different forms of amyloid peptides (Aβ) produced by the cleavage of a transmembrane precursor protein called APP (amyloid precursor protein) have a major role. Aβ peptides exist in different length the most common ones having 40 (Aβ40), 42 (Aβ42), or 38 (Aβ38) amino acids in length. APP processing by gamma-secretase releases also an amino-terminal secreted fragment called sAβPP-beta while an alternative nonamyloidogenic cleavage of APP, through an alpha-secretase, liberates another fragment called sAβPP-alpha. To decipher the molecular and pathological mechanisms leading to the production and the detection of these entities is essential for the comprehension and the prevention of AD. In this report, we present the results of the Keywords: Biomarkers CSF Soluble amyloid precursor proteins Aβ fragment peptides Alzheimer disease Dementi

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)

Connected speech as a marker of disease progression in autopsy-proven Alzheimer's disease.

Although an insidious history of episodic memory difficulty is a typical presenting symptom of Alzheimer's disease, detailed neuropsychological profiling frequently demonstrates deficits in other cognitive domains, including language. Previous studies from our group have shown that language changes may be reflected in connected speech production in the earliest stages of typical Alzheimer's disease. The aim of the present study was to identify features of connected speech that could be used to examine longitudinal profiles of impairment in Alzheimer's disease. Samples of connected speech were obtained from 15 former participants in a longitudinal cohort study of ageing and dementia, in whom Alzheimer's disease was diagnosed during life and confirmed at post-mortem. All patients met clinical and neuropsychological criteria for mild cognitive impairment between 6 and 18 months before converting to a status of probable Alzheimer's disease. In a subset of these patients neuropsychological data were available, both at the point of conversion to Alzheimer's disease, and after disease severity had progressed from the mild to moderate stage. Connected speech samples from these patients were examined at later disease stages. Spoken language samples were obtained using the Cookie Theft picture description task. Samples were analysed using measures of syntactic complexity, lexical content, speech production, fluency and semantic content. Individual case analysis revealed that subtle changes in language were evident during the prodromal stages of Alzheimer's disease, with two-thirds of patients with mild cognitive impairment showing significant but heterogeneous changes in connected speech. However, impairments at the mild cognitive impairment stage did not necessarily entail deficits at mild or moderate stages of disease, suggesting non-language influences on some aspects of performance. Subsequent examination of these measures revealed significant linear trends over the three stages of disease in syntactic complexity, semantic and lexical content. The findings suggest, first, that there is a progressive disruption in language integrity, detectable from the prodromal stage in a subset of patients with Alzheimer's disease, and secondly that measures of semantic and lexical content and syntactic complexity best capture the global progression of linguistic impairment through the successive clinical stages of disease. The identification of disease-specific language impairment in prodromal Alzheimer's disease could enhance clinicians' ability to distinguish probable Alzheimer's disease from changes attributable to ageing, while longitudinal assessment could provide a simple approach to disease monitoring in therapeutic trials

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