The Use of a Virtual Reality Platform for the Assessment of the Memory Decline and the Hippocampal Neural Injury in Subjects with Mild Cognitive Impairment: The Validity of Smart Aging Serious Game (SASG)

Due to the lack of pharmacological treatment for dementia, timely detection of subjects at risk can be of seminal importance for preemptive rehabilitation interventions. The aim of the study was to determine the usability of the smart aging serious game (SASG), a virtual reality platform, in assessing the cognitive profile of an amnestic mild cognitive impairment (aMCI) population, its validity in discriminating aMCI from healthy controls (HC), and in detecting hippocampal degeneration, a biomarker of clinical progression towards dementia. Thirty-six aMCI and 107 HC subjects were recruited and administered the SASG together with a neuropsychological evaluation. All aMCI and 30 HC subjects performed also an MRI for hippocampal volume measurement. Results showed good usability of the SASG despite the low familiarity with technology in both groups. ROC curve analyses showed similar discriminating abilities for SASG and gold standard tests, and a greater discrimination ability compared to non-specific neuropsychological tests. Finally, linear regression analysis revealed that the SASG outperformed the Montreal cognitive assessment test (MoCA) in the ability to detect neuronal degeneration in the hippocampus on the right side. These data show that SASG is an ecological task, that can be considered a digital biomarker providing objective and clinically meaningful data about the cognitive profile of aMCI subjects.

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Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease

NeuroImage ◽

10.1016/j.neuroimage.2010.03.018 ◽

2010 ◽

Vol 51 (4) ◽

pp. 1345-1359 ◽

Cited By ~ 174

Author(s):

Kelvin K. Leung ◽

Josephine Barnes ◽

Gerard R. Ridgway ◽

Jonathan W. Bartlett ◽

Matthew J. Clarkson ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Hippocampal Volume ◽

Volume Measurement ◽

Cross Sectional

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P1-189: The differential diagnosis of amnestic mild cognitive impairment and Alzheimer's disease dementia by hippocampal volume measurement with MR in a chinese population

Alzheimer s & Dementia ◽

10.1016/j.jalz.2015.06.388 ◽

2015 ◽

Vol 11 (7S_Part_9) ◽

pp. P420-P421

Author(s):

Jing Shi ◽

Jinzhou Tian ◽

Liping Zhang ◽

Xuekai Zhang ◽

Mingqing Wei ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differential Diagnosis ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Chinese Population ◽

Hippocampal Volume ◽

Volume Measurement ◽

Amnestic Mild Cognitive Impairment ◽

Alzheimer’S Disease Dementia

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Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

Current Alzheimer Research ◽

10.2174/1567205014666171120144343 ◽

2018 ◽

Vol 15 (2) ◽

pp. 104-110 ◽

Cited By ~ 3

Author(s):

Shohei Kato ◽

Akira Homma ◽

Takuto Sakuma

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Characteristic Curve ◽

Linear Regression Analysis ◽

Speech Sound ◽

The Elderly ◽

Speech Sounds ◽

Mel Frequency Cepstral Coefficients

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

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