Dataset Information

Peripheral reaching in Alzheimer's disease and mild cognitive impairment.

ABSTRACT: Recent evidence has implicated areas within the posterior parietal cortex (PPC) as among the first to show pathophysiological changes in Alzheimer's disease (AD). Focal brain damage to the PPC can cause optic ataxia, a specific deficit in reaching to peripheral targets. The present study describes a novel investigation of peripheral reaching ability in AD and mild cognitive impairment (MCI), to assess whether this deficit is common among these patient groups. Individuals with a diagnosis of mild-to-moderate AD, or MCI, and healthy older adult controls were required to reach to targets presented in central vision or in peripheral vision using two reaching tasks; one in the lateral plane and another presented in radial depth. Pre-registered case-control comparisons identified 1/10 MCI and 3/17 AD patients with significant peripheral reaching deficits at the individual level, but group-level comparisons did not find significantly higher peripheral reaching error in either AD or MCI by comparison to controls. Exploratory analyses showed significantly increased reach duration in both AD and MCI groups relative to controls, accounted for by an extended Deceleration Time of the reach movement. These findings suggest that peripheral reaching deficits like those observed in optic ataxia are not a common feature of AD. However, we show that cognitive decline is associated with a generalised slowing of movement which may indicate a visuomotor deficit in reach planning or online guidance.

SUBMITTER: Mitchell AG

PROVIDER: S-EPMC9007170 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Peripheral reaching in Alzheimer's disease and mild cognitive impairment.

Mitchell Alexandra G AG Rossit Stephanie S Pal Suvankar S Hornberger Michael M Warman Annie A Kenning Elise E Williamson Laura L Shapland Rebecca R McIntosh Robert D RD

Cortex; a journal devoted to the study of the nervous system and behavior 20220131

Recent evidence has implicated areas within the posterior parietal cortex (PPC) as among the first to show pathophysiological changes in Alzheimer's disease (AD). Focal brain damage to the PPC can cause optic ataxia, a specific deficit in reaching to peripheral targets. The present study describes a novel investigation of peripheral reaching ability in AD and mild cognitive impairment (MCI), to assess whether this deficit is common among these patient groups. Individuals with a diagnosis of mild ...[more]

PMID: 35184013

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Project description:Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.

Dataset Information

Peripheral reaching in Alzheimer's disease and mild cognitive impairment.

Publications

Peripheral reaching in Alzheimer's disease and mild cognitive impairment.

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