Dataset Information

Distinctive features of microsaccades in Alzheimer's disease and in mild cognitive impairment.

ABSTRACT: During visual fixation, the eyes are never completely still, but produce small involuntary movements, called "fixational eye movements," including microsaccades, drift, and tremor. In certain neurological disorders, attempted fixation results in abnormal fixational eye movements with distinctive characteristics. Thus, determining how normal fixation differs from pathological fixation has the potential to aid early and differential noninvasive diagnosis of neurological disease as well as the quantification of its progression and response to treatment. Here, we recorded the eye movements produced by patients with Alzheimer's disease, patients with mild cognitive impairment, and healthy age-matched individuals during attempted fixation. We found that microsaccade magnitudes, velocities, durations, and intersaccadic intervals were comparable in the three subject groups, but microsaccade direction differed in patients versus healthy subjects. Our results indicate that microsaccades are more prevalently oblique in patients with Alzheimer's disease or mild cognitive impairment than in healthy subjects. These findings extended to those microsaccades paired in square-wave jerks, supporting the hypothesis that microsaccades and square-wave jerks form a continuum, both in healthy subjects and in neurological patients.

SUBMITTER: Kapoula Z

PROVIDER: S-EPMC4039256 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Distinctive features of microsaccades in Alzheimer's disease and in mild cognitive impairment.

Kapoula Zoi Z Yang Qing Q Otero-Millan Jorge J Xiao Shifu S Macknik Stephen L SL Lang Alexandre A Verny Marc M Martinez-Conde Susana S

Age (Dordrecht, Netherlands) 20130915 2

During visual fixation, the eyes are never completely still, but produce small involuntary movements, called "fixational eye movements," including microsaccades, drift, and tremor. In certain neurological disorders, attempted fixation results in abnormal fixational eye movements with distinctive characteristics. Thus, determining how normal fixation differs from pathological fixation has the potential to aid early and differential noninvasive diagnosis of neurological disease as well as the quan ...[more]

PMID: 24037325

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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

Distinctive features of microsaccades in Alzheimer's disease and in mild cognitive impairment.

Publications

Distinctive features of microsaccades in Alzheimer's disease and in mild cognitive impairment.

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