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Artificial Intelligence models to enhance cognitive intervention in older adults with Subjective Cognitive Decline: pilot study
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Introduction: Subjective cognitive decline (SCD) in older adults are an early risk indicator for Alzheimer's disease or other forms of dementia, making older adults with SCD a target population for proactive interventions. The aim of this study was to determine if perceptual-cognitive training (PCT) can serve as a proactive intervention and enhance cognition in older adults with SCD.
Objectives: The objective of this pilot study was to determine if a 3-dimensional multiple object tracking training (3D-MOT) intervention could improve performance on measures of attention, psychomotor speed, and cognitive flexibility in healthy older adults. Methods: Forty-six individuals aged 63-87 years old participated in the study. Twenty-five participants in the intervention group completed the Stroop task before and after intervention that consisted of seven training sessions with the Neurotracker, a 3D-MOT software program. Stroop test scores were examined for changes in selective attention, cognitive flexibility (CF), as well as psychomotor speed pre-and post-intervention. The 21 individuals in the control group completed the Stroop test at the pre-post interval, without completing the Neurotracker intervention. Results: The Neurotracker training intervention group showed significant improvements in both cognitive flexibility (M = 5.01, SE = 1.44, p = 0.002), and psychomotor speed and selective attention (M = 4.90, SE = 1.44, p = 0.002). Significant changes were also detected in a condition that measured psychomotor speed and cognitive flexibility together (M = 9.39, SE = 1.74, p < 0.001). No significant changes were detected in the control group. Conclusion: The current results suggest that the Neurotracker may be an effective tool for improving selective attention, cognitive flexibility, and psychomotor speed in healthy older individuals.
Introduction
Computerised cognitive training (CCT) has been shown to enhance cognitive function in elderly individuals with cognitive deterioration, but evidence is controversial. Additionally, whether specific CCT is most effective and which stages of cognitive impairment benefit most is unclear.
Methods
We systematically searched nine medical and technological databases to collect randomized controlled trials related to CCT primarily conducted in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).
Results
We identified 12 studies in patients with SCD and MCI. Pooled analysis showed that CCT could significantly improve cognitive function (g = 0.518, p = 0.000), especially related to memory. In terms of different types of cognitive training, specific CCT was more efficacious than non-specific CCT (g = 0.381, p = 0.007) or placebo (g = 0.734, p = 0.000) but not traditional CT (p = 0.628). In terms of stages of cognitive deterioration, the effect of CCT on SCD (g = 0.926, p = 0.002) was almost double that of its effect on MCI (g = 0.502, p = 0.000).
Conclusion
CCT was most effective in cognitive rehabilitation, particularly in the subdomain of memory. Early intervention in SCD is better.
Evidence that bilingualism protects against age-related neurocognitive decline is mixed. One relatively consistent finding is that bilingual seniors have greater grey matter volume (GMV) in regions implicated in executive control (EC) and language processing. Here, we compare the neuroplastic effects of bilingual experience on the EC network of young and aging populations directly, and for the first time we evaluate the extent to which such effects may predict executive control performance across age. We used GMV as an index of neural reserve and response time (RT) performance on the Flanker task for measuring EC efficiency. In the presence of age-related widespread GM deterioration, bilinguals had greater GMV than monolinguals in key regions of interest across age. Moreover, whereas EC performance in monolingual seniors was strictly related to GMV, this was not observed for bilingual seniors or younger participants in either group. Interactions between expected effects-of-age and language group on the relationships between GMV and RT suggested that bilingualism affords differential benefits across the lifespan. In younger participants, greater GMV offered no behavioral benefit on EC performance , whilst it did for seniors. It thus appears that age-related cognitive decline following GMV loss in the EC network is delayed in bilinguals.
Background:
The increasing prevalence of Alzheimer disease (AD) emphasizes the need for effective treatments. Both pharmacological therapies such as nutrition therapy (NT) and nonpharmacologic therapies including traditional treatment or personalized treatment (e.g., physical exercise, music therapy, computerized cognitive training) have been approved for the treatment of AD or mild cognitive impairment (MCI) in numerous areas.
Methods:
The aim of this study was to compare 4 types of interventions, physical exercise (PE), music therapy (MT), computerized cognitive training (CCT), and NT, in older adults with mild to moderate AD or MCI and identify the most effective intervention for their cognitive function. We used a system of search strategies to identify relevant studies and include randomized controlled trials (RCTs), placebo-controlled trials evaluating the efficacy and safety of 4 interventions in patients with AD or MCI. We updated the relevant studies which were published before March 2017 as a full-text article. Using Bayesian network meta-analysis (NMA), we ranked cognitive ability based objectively on Mini-Mental State Examination (MMSE), and assessed neuropsychiatric symptoms based on Neuropsychiatric Inventory (NPI). Pairwise and network meta-analyses were sequentially performed for efficacy and safety of intervention compared to control group through RCTs included.
Results:
We included 17 RCTs. Fifteen trials (n = 1747) were pooled for cognition and no obvious heterogeneity was found (I = 21.7%, P = .212) in NMA, the mean difference (MD) of PE (MD = 2.1, confidence interval [CI]: 0.44-3.8) revealed that PE was significantly efficacious in the treatment group in terms of MMSE. Five trials (n = 660) assessed neuropsychiatric symptoms with an obvious heterogeneity (I = 61.6%, P = .034), the MD of CCT (MD = -7.7, CI: -14 to -2.4), revealing that CCT was significantly efficacious in NPI.
Conclusions:
As the first NMA comparing different interventions for AD and MCI, our study suggests that PE and CCT might have a significant improvement in cognition and neuropsychiatric symptoms respectively. Moreover, nonpharmacological therapies might be better than pharmacological therapies.
Algorithmic results of Machine Learning applications in a commercial aviation airline.
Scores for Unstable Approaches (UA) for individual flight operations of an undisclosed european airline are presented. A selection of features (predictors) have been modelled by logistic functions to target the UA variable (prediction). All possible combinations of such features have been processed over several thousands of flight operations.
Some combinations (true risk pattern signatures) got statistically significant parameters (significant beta values/odd ratios with large effects) by various Machine Learning algorithms performed in IBM SPSS 25 software between 2017 and 2018. SPSS syntax coding was preferred.
The diagnosis of concussion remains challenging, particularly in cases where several months have passed between a head injury and clinical assessment. Tracking multiple moving objects in three-dimensional (3D) space engages many of the same cognitive processes that are affected by concussion, a form of mild traumatic brain injury (mTBI), suggesting that tests of 3D multiple object tracking (3D-MOT) may be sensitive to post-concussion syndrome after a brain injury has occurred. To test this, we evaluated 3D-MOT performance (using NeuroTrackerTM) against Sports Concussion Assessment Tool results for cognition, balance, and symptom severity in a large sample (N = 457) of male and female participants between the ages of 6 to 73. 3D-MOT performance in subjects under age 13 was not impaired by a history of concussion, but was positively associated with cognition and balance. 3D-MOT performance in those 13 and older was negatively associated with concussion symptom severity, and positively associated with cognition and balance. 3D-MOT was selectively impaired in subjects with probable post-concussion syndrome (pPCS), defined using the 95th percentile of symptom severity for subjects with no history of concussion. A decision tree predicted concussion status with 95.2% overall test accuracy (91.1% sensitivity, 97.8% specificity) using concussion history, age, and 3D-MOT score. Individuals with a history of concussion in the past 37 days were predicted to have pPCS if they were age 35 or older, or if they were under age 35 but achieved scores below 1.2 on the 3D-MOT. These results demonstrate the potential of 3D-MOT for pPCS diagnosis, and highlight the increased vulnerability to concussion symptoms that comes with age.
Objective:
This review evaluates the use of virtual reality (VR) tools in cognitive rehabilitation of stroke-affected individuals.
Methods:
Studies performed between 2010 and 2017 that fulfilled inclusion criteria were selected from PubMed, Scopus, Cochrane, and Web of Sciences databases. The search combined the terms "VR," "rehabilitation," and "stroke."
Results:
Stroke patients experienced significant improvement in many cognitive domains (such as executive and visual-spatial abilities and speech, attention, and memory skills) after the use of VR training.
Conclusions:
Rehabilitation using new VR tools could positively affect stroke patient cognitive outcomes by boosting motivation and participation.
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
