Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases.

A substantial portion of dementia risk can be attributed to modifiable risk factors that can be affected by lifestyle changes. Identifying the contributors to dementia risk could prove valuable. Recently, machine learning methods have been increasingly applied to healthcare data. Several studies have attempted to predict dementia progression by using such techniques. This study aimed to compare the performance of different machine-learning methods in modeling associations between known cognitive risk factors and future dementia cases. A subset of the AGES-Reykjavik Study dataset was analyzed using three machine-learning methods: logistic regression, random forest, and neural networks. Data were collected twice, approximately five years apart. The dataset included information from 1,491 older adults who underwent a cognitive screening process and were considered to have healthy cognition at baseline. Cognitive risk factors included in the models were based on demographics, MRI data, and other health-related data. At follow-up, participants were re-evaluated for dementia using the same cognitive screening process. Various performance metrics for all three machine learning algorithms were assessed. The study results indicate that a random forest algorithm performed better than neural networks and logistic regression in predicting the association between cognitive risk factors and dementia. Compared to more traditional statistical analyses, machine-learning methods have the potential to provide more accurate predictions about which individuals are more likely to develop dementia than others.

Blunted Cardiovascular Reactivity Predicts Worse Performance in Working Memory Tasks.

When we experience psychological challenges in the environment, our heart rate usually rises to make us more able to solve the task, but there is an individual difference in cardiovascular reactivity (CVR). Extreme CVR to environmental demands has been associated with worse health outcomes, with blunted CVR (little or no rise in heart rate) related to maladaptive behavior, including depression. The blunted CVR has been explained by motivational disengagement, which involves giving up on a task when facing obstacles. Disengagement is thought to be a habitual response that people might not be aware of, and, therefore, objective measures such as test performance might serve as a good measure of engagement. In this study, 66 participants solved different cognitive tasks while their CVR was measured. The aim was to test the association between test performance and reactivity, measured with the difference in heart rate at baseline and the mean heart rate while solving the tasks. Our results show a significant association between reactivity scores and performance in all tests, of various difficulty, indicating that blunted cardiovascular reactivity predicts poorer cognitive performance. Furthermore, we find an association between reactivity in one test and the performance in the other tests, suggesting that disengagement from environmental demands can be more general and not depend on the task at hand. The results, therefore, support earlier research suggesting that blunted CVR is associated with worse cognitive performance, and extends the literature by indicating that disengagement could be a more general maladaptive response to the environment.

Exposure factors associated with dementia among older adults in Iceland: the AGES-Reykjavik study.

The study aimed to assess whether factors related to cognitive performance were associated with the development of dementia. Additionally, the study aimed to establish whether cognitive performance at baseline or change in cognition between baseline and follow-up (five-year period) had a stronger association with whether an individual would fulfill a dementia criterion at follow-up. The data was collected from 2002 to 2011. Logistic regression was applied to the AGES-Reykjavik Study epidemiological data. The analysis, which builds upon previous data analyses of the same dataset, included 1,491 participants between the ages of 66 and 90. All those included were considered to have normal cognition at baseline; 8.2% (n = 123) of them fulfilled a dementia criterion at follow-up five years later. The study's results indicated that being high on cognitive reserve factors reduced the risk of developing dementia. Compared to other known dementia risk factors, cognitive reserve factors (education level, participation in leisure activities, and self-reported health) were more likely than others to have an association with dementia. Additionally, the study's findings showed that cognitive performance at baseline, rather than change in cognition between baseline and follow-up five years later, had a stronger association with dementia at the follow-up assessment. Together, these findings support the notion that promoting high cognitive reserve throughout the lifespan and reaching high cognitive performance is important in reducing dementia risk.

Cognition and brain health among older adults in Iceland: the AGES-Reykjavik study.

The paper aimed to compare how factors previously identified as predictive factors for cognitive decline and dementia related to cognitive performance on the one hand and brain health on the other. To that aim, multiple linear regression was applied to the AGES-Reykjavik study epidemiological data. Additionally, a regression analysis was performed for change in cognition over 5 years, using the same exposure factors. The study ran from 2002 to 2011, and the sample analyzed included 1707 participants between the ages of 66 and 90. The data contains MR imaging, cognitive testing, background data, and physiological measurements. Overall, we conclude that risk factors linked to dementia relate differently to cognition and brain health. Mobility, physical strength, alcohol consumption, coronary artery disease, and hypertension were associated with cognition and brain volume. Smoking, depression, diabetes, and body fat percentage were only associated with brain volume, not cognitive performance. Modifiable factors previously linked to cognitive reserve, such as educational attainment, participation in leisure activities, multilingualism and good self-reported health, were associated with cognitive function but did not relate to brain volume. These findings show that, within the same participant pool, cognitive reserve proxy variables have a relationship with cognitive performance but have no association with relative brain volume measured simultaneously.

P300 Analysis Using High-Density EEG to Decipher Neural Response to rTMS in Patients With Schizophrenia and Auditory Verbal Hallucinations.

Schizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment.

Schizophrenia, cognition, and aging: cognitive deficits and the relationship between test performance and aging.

Most measures of cognitive function decline with age during adulthood. Research indicates that people with schizophrenia experience considerable cognitive deficits. These deficits appear to become more troublesome with increasing age, but this has been debated. The aim of this research was to better understand the age related cognitive deficits of Icelandic subjects with schizophrenia in comparison to healthy individuals. Cognition of individuals 18 to 64 years of age was evaluated with 10 neuropsychological tests. People with schizophrenia performed significantly worse on all tests, as expected, indicating widespread cognitive deficits compared to healthy individuals, independent of age. Furthermore, the results suggest that people with schizophrenia follow a similar age-related trajectory of cognitive decline as healthy individuals. Overall, we conclude that the cognitive difficulties often experienced by older people with schizophrenia are better explained by lower cognitive function at the time of diagnosis than by faster cognitive decline with increasing age.