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The Smart Aging Platform for Assessing Early Phases of Cognitive Impairment in Patients With Neurodegenerative Diseases.

ABSTRACT: Background: Smart Aging is a serious game (SG) platform that generates a 3D virtual reality environment in which users perform a set of screening tasks designed to allow evaluation of global cognition. Each task replicates activities of daily living performed in a familiar environment. The main goal of the present study was to ascertain whether Smart Aging could differentiate between different types and levels of cognitive impairment in patients with neurodegenerative disease. Methods: Ninety-one subjects (mean age = 70.29 ± 7.70 years)-healthy older adults (HCs, n = 23), patients with single-domain amnesic mild cognitive impairment (aMCI, n = 23), patients with single-domain executive Parkinson's disease MCI (PD-MCI, n = 20), and patients with mild Alzheimer's disease (mild AD, n = 25)-were enrolled in the study. All participants underwent cognitive evaluations performed using both traditional neuropsychological assessment tools, including the Mini-Mental State Examination (MMSE), Montreal Overall Cognitive Assessment (MoCA), and the Smart Aging platform. We analyzed global scores on Smart Aging indices (i.e., accuracy, time, distance) as well as the Smart Aging total score, looking for differences between the four groups. Results: The findings revealed significant between-group differences in all the Smart Aging indices: accuracy (p < 0.001), time (p < 0.001), distance (p < 0.001), and total Smart Aging score (p < 0.001). The HCs outperformed the mild AD, aMCI, and PD-MCI patients in terms of accuracy, time, distance, and Smart Aging total score. In addition, the mild AD group was outperformed both by the HCs and by the aMCI and PD-MCI patients on accuracy and distance. No significant differences were found between aMCI and PD-MCI patients. Finally, the Smart Aging scores significantly correlated with the results of the neuropsychological assessments used. Conclusion: These findings, although preliminary due to the small sample size, suggest the validity of Smart Aging as a screening tool for the detection of cognitive impairment in patients with neurodegenerative diseases.

SUBMITTER: Bottiroli S

PROVIDER: S-EPMC8005545 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer's disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults. Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night. Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05). Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.

Project description:Mitochondrial dysfunction is an important factor in the pathogenesis of age-related diseases, including neurodegenerative diseases like Alzheimer's and Parkinson's spectrum disorders. A polymorphism in Translocase of the Outer Mitochondrial Membrane - 40 kD (TOMM40) is associated with risk and age-of onset of late-onset AD, and is the only nuclear- encoded gene identified in genetic studies to date that presumably contributes to LOAD-related mitochondria dysfunction. In this review, we describe the TOM40-mediated mitochondrial protein import mechanism, and discuss the evidence linking TOM40 with Alzheimer's (AD) and Parkinson's (PD) diseases. All but 36 of the >~1,500 mitochondrial proteins are encoded by the nucleus and are synthesized on cytoplasmic ribosomes, and most of these are imported into mitochondria through the TOM complex, of which TOM40 is the central pore, mediating communication between the cytoplasm and the mitochondrial interior. APP enters and obstructs the TOM40 pore, inhibiting import of OXPHOS-related proteins and disrupting the mitochondrial redox balance. Other pathogenic proteins, such as Aβ and alpha-synuclein, readily pass through the pore and cause toxic effects by directly inhibiting mitochondrial enzymes. Healthy mitochondria normally import and degrade the PD-related protein Pink1, but Pink1 exits mitochondria if the membrane potential collapses and initiates Parkin-mediated mitophagy. Under normal circumstances, this process helps clear dysfunctional mitochondria and contributes to cellular health, but PINK1 mutations associated with PD exit mitochondria with intact membrane potentials, disrupting mitochondrial dynamics, leading to pathology. Thus, TOM40 plays a central role in the mitochondrial dysfunction that underlies age-related neurodegenerative diseases. Learning about the factors that control TOM40 levels and activity, and how TOM40, specifically, and the TOM complex, generally, interacts with potentially pathogenic proteins, will provide deeper insights to AD and PD pathogenesis, and possibly new targets for preventative and/or therapeutic treatments.

Project description:The two most generally diagnosed Neurodegenerative diseases are the Alzheimer and Parkinson diseases. So this paper presents a fully automated early screening system based on the Capsule network for the classification of these two Neurodegenerative diseases. In this study, we hypothesized that the Neurodegenerative diseases-Caps system based on the Capsule network architecture accurately performs the multiclass i.e. three class classification into either the Alzheimer class or Parkinson class or Healthy control and delivers better results in comparison other deep transfer learning models. The real motivation behind choosing the capsule network architecture is its more resilient nature towards the affine transformations as well as rotational & translational invariance, which commonly persists in the medical image datasets. Apart from this, the capsule networks overcomes the pooling layers related deficiencies from which conventional CNNs are mostly affected and unable to delivers accurate results especially in the tasks related to image classification. The various Computer aided systems based on machine learning for the classification of brain tumors and other types of cancers are already available. Whereas for the classification of Neurodegenerative diseases, the amount of research done is very limited and the number of persons suffering from this type of diseases are increasing especially in developing countries like India, China etc. So there is a need to develop an early screening system for the correct multiclass classification into Alzheimer's, Parkinson's and Normal or Healthy control cases. The Alzheimer disease and Parkinson progression (ADPP) dataset is used in this research study for the training of the proposed Neurodegenerative diseases-Caps system. This ADPP dataset is developed with the aid of both the Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's disease Neuroimaging Initiative (ADNI) databases. There is no such early screening system exist yet, which can perform the accurate classification of these two Neurodegenerative diseases. For the sake of genuine comparison, other popular deep transfer learning models like VGG19, VGG16, ResNet50 and InceptionV3 are implemented and also trained over the same ADPP dataset. The proposed Neurodegenerative diseases-Caps system deliver accuracies of 97.81, 98, 96.81% for the Alzheimer, Parkinson and Healthy control or Normal cases with 70/30 (training/validation split) and performs way better as compare to the other popular Deep transfer learning models.Supplementary informationThe online version contains supplementary material available at 10.1007/s11571-022-09787-1.

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The Smart Aging Platform for Assessing Early Phases of Cognitive Impairment in Patients With Neurodegenerative Diseases.

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