scholarly journals Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer’s Disease and Mild Cognitive Impairment

2021 ◽  
pp. 1-13
Author(s):  
Yasunori Yamada ◽  
Kaoru Shinkawa ◽  
Masatomo Kobayashi ◽  
Vittorio Caggiano ◽  
Miyuki Nemoto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Impairments ◽  
Classification Performance ◽  
Behavioral Data ◽  
Cognitively Normal ◽  
Clinical Measures

Background: Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD. Objective: We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI. Methods: Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants. Results: Combining all three behavioral modalities achieved 93.0%accuracy for classifying AD, MCI, and CN, and only 81.9%when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI. Conclusion: Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

scholarly journals Financial Capacity and Regional Cerebral Tau in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer’s Disease Dementia

2020 ◽  
pp. 1-10
Author(s):  
Christopher Gonzalez ◽  
Nicole S. Tommasi ◽  
Danielle Briggs ◽  
Michael J. Properzi ◽  
Rebecca E. Amariglio ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Stage ◽  
Cognitively Normal ◽  
Financial Capacity ◽  
Posterior Cingulate ◽  
Dorsolateral Prefrontal

Background: Financial capacity is often one of the first instrumental activities of daily living to be affected in cognitively normal (CN) older adults who later progress to amnestic mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. Objective: The objective of this study was to investigate the association between financial capacity and regional cerebral tau. Methods: Cross-sectional financial capacity was assessed using the Financial Capacity Instrument –Short Form (FCI-SF) in 410 CN, 199 MCI, and 61 AD dementia participants who underwent flortaucipir tau positron emission tomography from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Linear regression models with backward elimination were used with FCI-SF total score as the dependent variable and regional tau and tau-amyloid interaction as predictors of interest in separate analyses. Education, age sex, Rey Auditory Verbal Learning Test Total Learning, and Trail Making Test B were used as covariates. Results: Significant associations were found between FCI-SF and tau regions (entorhinal: p <  0.001; inferior temporal: p <  0.001; dorsolateral prefrontal: p = 0.01; posterior cingulate: p = 0.03; precuneus: p <  0.001; and supramarginal gyrus: p = 0.005) across all participants. For the tau-amyloid interaction, significant associations were found in four regions (amyloid and dorsolateral prefrontal tau interaction: p = 0.005; amyloid and posterior cingulate tau interaction: p = 0.005; amyloid and precuneus tau interaction: p <  0.001; and amyloid and supramarginal tau interaction: p = 0.002). Conclusion: Greater regional tau burden was modestly associated with financial capacity impairment in early-stage AD. Extending this work with longitudinal analyses will further illustrate the utility of such assessments in detecting clinically meaningful decline, which may aid clinical trials of early-stage AD.

scholarly journals 12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

2021 ◽  
Author(s):  
McKenna E Williams ◽  
Jeremy A Elman ◽  
Linda K McEvoy ◽  
Ole A Andreassen ◽  
Anders M Dale ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mean Diffusivity ◽  
Area Under The Curve ◽  
Polygenic Risk Score ◽  
Age Difference ◽  
Cognitively Normal ◽  
Brain Age

Abstract Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and 2) a novel gray matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply aging-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P=0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently-derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step toward improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

scholarly journals To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Justine Staal ◽  
Francesco Mattace-Raso ◽  
Hennie A. M. Daniels ◽  
Johannes van der Steen ◽  
Johan J. M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Support Vector ◽  
Potential Biomarker

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

scholarly journals Visual Object Discrimination Impairment as an Early Predictor of Mild Cognitive Impairment and Alzheimer’s Disease

2019 ◽  
Vol 25 (7) ◽  
pp. 688-698 ◽  
Author(s):  
Leslie S. Gaynor ◽  
Rosie E. Curiel Cid ◽  
Ailyn Penate ◽  
Mónica Rosselli ◽  
Sara N. Burke ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Discrimination Task ◽  
Inverse Function ◽  
Visual Object ◽  
Object Discrimination ◽  
Cognitively Normal ◽  
Novel Object

AbstractObjective:Detection of cognitive impairment suggestive of risk for Alzheimer’s disease (AD) progression is crucial to the prevention of incipient dementia. This study was performed to determine if performance on a novel object discrimination task improved identification of earlier deficits in older adults at risk for AD.Method:In total, 135 participants from the 1Florida Alzheimer’s Disease Research Center [cognitively normal (CN), Pre-mild cognitive impairment (PreMCI), amnestic mild cognitive impairment (aMCI), and dementia] completed a test of object discrimination and traditional memory measures in the context of a larger neuropsychological and clinical evaluation.Results:The Object Recognition and Discrimination Task (ORDT) revealed significant differences between the PreMCI, aMCI, and dementia groups versus CN individuals. Moreover, relative risk of being classified as PreMCI rather than CN increased as an inverse function of ORDT score.Discussion:Overall, the obtained results suggest that a novel object discrimination task improves the detection of very early AD-related cognitive impairment, increasing the window for therapeutic intervention. (JINS, 2019,25, 688–698)

Performance of Validated MicroRNA Biomarkers for Alzheimer’s Disease in Mild Cognitive Impairment

2020 ◽  
Vol 78 (1) ◽  
pp. 245-263
Author(s):  
Ursula S. Sandau ◽  
Jack T. Wiedrick ◽  
Sierra J. Smith ◽  
Trevor J. McFarland ◽  
Theresa A. Lusardi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Stage ◽  
Linear Trend ◽  
Area Under The Curve ◽  
Classification Performance ◽  
T Tau ◽  
Median Expression

Background: Cerebrospinal fluid (CSF) microRNA (miRNA) biomarkers of Alzheimer’s disease (AD) have been identified, but have not been evaluated in prodromal AD, including mild cognitive impairment (MCI). Objective: To assess whether a set of validated AD miRNA biomarkers in CSF are also sensitive to early-stage pathology as exemplified by MCI diagnosis. Methods: We measured the expression of 17 miRNA biomarkers for AD in CSF samples from AD, MCI, and cognitively normal controls (NC). We then examined classification performance of the miRNAs individually and in combination. For each miRNA, we assessed median expression in each diagnostic group and classified markers as trending linearly, nonlinearly, or lacking any trend across the three groups. For trending miRNAs, we assessed multimarker classification performance alone and in combination with apolipoprotein E ɛ4 allele (APOE ɛ4) genotype and amyloid-β42 to total tau ratio (Aβ42:T-Tau). We identified predicted targets of trending miRNAs using pathway analysis. Results: Five miRNAs showed a linear trend of decreasing median expression across the ordered diagnoses (control to MCI to AD). The trending miRNAs jointly predicted AD with area under the curve (AUC) of 0.770, and MCI with AUC of 0.705. Aβ42:T-Tau alone predicted MCI with AUC of 0.758 and the AUC improved to 0.813 (p = 0.051) after adding the trending miRNAs. Multivariate correlation of the five trending miRNAs with Aβ42:T-Tau was weak. Conclusion: Selected miRNAs combined with Aβ42:T-Tau improved classification performance (relative to protein biomarkers alone) for MCI, despite a weak correlation with Aβ42:T-Tau. Together these data suggest that that these miRNAs carry novel information relevant to AD, even at the MCI stage. Preliminary target prediction analysis suggests novel roles for these biomarkers.

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