| 1. |
- Bjerkeli, Pernilla J., et al.
(author)
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Overuse of methylphenidate : an analysis of Swedish pharmacy dispensing data
- 2018
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In: Clinical Epidemiology. - : Dove Press. - 1179-1349 .- 1179-1349. ; 10, s. 1657-1665
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Journal article (peer-reviewed)abstract
- Purpose: To identify overuse of methylphenidate and to investigate patterns of overuse in relation to sociodemographic and clinical characteristics. Patients and methods: Swedish national, pharmacy dispensing data were analyzed for all 56,922 individuals aged 6-79 years, who filled a methylphenidate prescription between 2010 and 2011. Overuse was defined as having above 150% days covered by the dispensed amount during 365 days from the first prescription fill, assuming use at the maximum recommended daily dose. Results: In total, 4,304 individuals (7.6% of the methylphenidate users) were categorized as overusers. The risk of overuse increased with age (OR for 46-65 years vs 6-12 years 17.5, 95% CI 14.3-21.3), and was higher in men (OR 1.4, 95% CI 1.3-1.5) and individuals with low income (OR 1.1, 95% CI 1.0-1.2), as well as in individuals with an attention deficit hyperactivity disorder (ADHD) diagnosis (OR 1.4, 95% CI 1.3-1.6), health care visits (OR 1.3, 95% CI 1.2-1.4), previous ADHD medication use (OR 2.6, 95% CI 2.4-2.8), and previous diagnosis of mental and behavioral disorders due to psychoactive substance use (OR 2.1 95% CI 2.0-2.3). Conclusion: Among individuals using methylphenidate in Sweden, 7.6% receive amounts that are larger than what they should have a medical need for, assuming that they were using the maximum recommended daily dose 365 days per year. Notably, the prevalence of overuse was associated with previous diagnosis of alcohol and drug misuse. The prevalence was also positively associated with higher age and previous use of ADHD medication. These findings may point toward a link between exposure time and overuse. However, future studies with long-term data are needed to investigate this.
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| 2. |
- Javeed, Ashir, 1989-, et al.
(author)
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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks
- 2023
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In: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 75:2, s. 2491-2508
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Journal article (peer-reviewed)abstract
- Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.
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| 3. |
- Javeed, Ashir, 1989-, et al.
(author)
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Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
- 2023
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In: Biomedicines. - : MDPI. - 2227-9059. ; 11:2
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Journal article (peer-reviewed)abstract
- Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.
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| 4. |
- Kivimäki, Mika, et al.
(author)
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Cognitive stimulation in the workplace, plasma proteins, and risk of dementia : three analyses of population cohort studies
- 2021
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In: The BMJ. - : BMJ Publishing Group Ltd. - 1756-1833. ; 374
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Journal article (peer-reviewed)abstract
- OBJECTIVES: To examine the association between cognitively stimulating work and subsequent risk of dementia and to identify protein pathways for this association.DESIGN: Multicohort study with three sets of analyses.SETTING: United Kingdom, Europe, and the United States.PARTICIPANTS: Three associations were examined: cognitive stimulation and dementia risk in 107 896 participants from seven population based prospective cohort studies from the IPD-Work consortium (individual participant data meta-analysis in working populations); cognitive stimulation and proteins in a random sample of 2261 participants from one cohort study; and proteins and dementia risk in 13 656 participants from two cohort studies.MAIN OUTCOME MEASURES: Cognitive stimulation was measured at baseline using standard questionnaire instruments on active versus passive jobs and at baseline and over time using a job exposure matrix indicator. 4953 proteins in plasma samples were scanned. Follow-up of incident dementia varied between 13.7 to 30.1 years depending on the cohort. People with dementia were identified through linked electronic health records and repeated clinical examinations.RESULTS: During 1.8 million person years at risk, 1143 people with dementia were recorded. The risk of dementia was found to be lower for participants with high compared with low cognitive stimulation at work (crude incidence of dementia per 10 000 person years 4.8 in the high stimulation group and 7.3 in the low stimulation group, age and sex adjusted hazard ratio 0.77, 95% confidence interval 0.65 to 0.92, heterogeneity in cohort specific estimates I2=0%, P=0.99). This association was robust to additional adjustment for education, risk factors for dementia in adulthood (smoking, heavy alcohol consumption, physical inactivity, job strain, obesity, hypertension, and prevalent diabetes at baseline), and cardiometabolic diseases (diabetes, coronary heart disease, stroke) before dementia diagnosis (fully adjusted hazard ratio 0.82, 95% confidence interval 0.68 to 0.98). The risk of dementia was also observed during the first 10 years of follow-up (hazard ratio 0.60, 95% confidence interval 0.37 to 0.95) and from year 10 onwards (0.79, 0.66 to 0.95) and replicated using a repeated job exposure matrix indicator of cognitive stimulation (hazard ratio per 1 standard deviation increase 0.77, 95% confidence interval 0.69 to 0.86). In analysis controlling for multiple testing, higher cognitive stimulation at work was associated with lower levels of proteins that inhibit central nervous system axonogenesis and synaptogenesis: slit homologue 2 (SLIT2, fully adjusted β -0.34, P<0.001), carbohydrate sulfotransferase 12 (CHSTC, fully adjusted β -0.33, P<0.001), and peptidyl-glycine α-amidating monooxygenase (AMD, fully adjusted β -0.32, P<0.001). These proteins were associated with increased dementia risk, with the fully adjusted hazard ratio per 1 SD being 1.16 (95% confidence interval 1.05 to 1.28) for SLIT2, 1.13 (1.00 to 1.27) for CHSTC, and 1.04 (0.97 to 1.13) for AMD.CONCLUSIONS: The risk of dementia in old age was found to be lower in people with cognitively stimulating jobs than in those with non-stimulating jobs. The findings that cognitive stimulation is associated with lower levels of plasma proteins that potentially inhibit axonogenesis and synaptogenesis and increase the risk of dementia might provide clues to underlying biological mechanisms.
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| 5. |
- Nyberg, Solja T., et al.
(author)
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Association of alcohol use with years lived without major chronic diseases : A multicohort study from the IPD-Work consortium and UK Biobank
- 2022
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In: The Lancet Regional Health - Europe. - : Elsevier. - 2666-7762. ; 19
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Journal article (peer-reviewed)abstract
- Background Heavy alcohol consumption increases the risk of several chronic diseases. In this multicohort study, we estimated the number of life-years without major chronic diseases according to different characteristics of alcohol use.Methods In primary analysis, we pooled individual-level data from up to 129,942 adults across 12 cohort studies with baseline data collection on alcohol consumption, drinking patterns, and history between 1986 and 2005 (the IPD-Work Consortium). Self-reported alcohol consumption was categorised according to UK guidelines - non-drinking (never or former drinkers); moderate consumption (1-14 units); heavy consumption (>14 units per week). We further subdivided moderate and heavy drinkers by binge drinking pattern (alcohol-induced loss of consciousness). In addition, we assessed problem drinking using linked data on hospitalisations due to alcohol abuse or poisoning. Follow-up for chronic diseases for all participants included incident type 2 diabetes, coronary heart disease, stroke, cancer, and respiratory disease (asthma and chronic obstructive pulmonary disease) as ascertained via linkage to national morbidity and mortality registries, repeated medical examinations, and/or self-report. We estimated years lived without any of these diseases between 40 and 75 years of age according to sex and characteristics of alcohol use. We repeated the main analyses using data from 427,621 participants in the UK Biobank cohort study.Findings During 1.73 million person-years at risk, 22,676 participants in IPD-Work cohorts developed at least one chronic condition. From age 40 to 75 years, never-drinkers [men: 29.3 (95%CI 27.9-30.8) years, women 29.8 (29.2 - 30.4) years)] and moderate drinkers with no binge drinking habit [men 28.7 (28.4-29.0) years, women 29.6 (29.4-29.7) years] had the longest disease-free life span. A much shorter disease-free life span was apparent in participants who experienced alcohol poisoning [men 23.4 (20.9-26.0) years, women 24.0 (21.4-26.5) years] and those with self-reported heavy overall consumption and binge drinking [men: 26.0 (25.3-26.8), women 27.5 (26.4 - 28.5) years]. The pattern of results for alcohol poisoning and self-reported alcohol consumption was similar in UK Biobank. In IPD-Work and UK Biobank, differences in disease-free years between self-reported moderate drinkers and heavy drinkers were 1.5 years or less.Interpretation Individuals with alcohol poisonings or heavy self-reported overall consumption combined with a binge drinking habit have a marked 3- to 6-year loss in healthy longevity. Differences in disease-free life between categories of self-reported weekly alcohol consumption were smaller.
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| 6. |
- Samrani, George, et al.
(author)
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Behavioral facilitation and increased brain responses from a high interference working memory context
- 2018
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In: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 8:1
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Journal article (peer-reviewed)abstract
- Many real-life situations require flexible behavior in changing environments. Evidence suggests that anticipation of conflict or task difficulty results in behavioral and neural allocation of task-relevant resources. Here we used a high- and low-interference version of an item-recognition task to examine the neurobehavioral underpinnings of context-sensitive adjustment in working memory (WM). We hypothesized that task environments that included high-interference trials would require participants to allocate neurocognitive resources to adjust to the more demanding task context. The results of two independent behavioral experiments showed enhanced WM performance in the high-interference context, which indicated that a high-interference context improves performance on non-interference trials. A third behavioral experiment showed that when WM load was increased, this effect was no longer significant. Neuroimaging results further showed greater engagement of inferior frontal gyrus, striatum, parietal cortex, hippocampus, and midbrain in participants performing the task in the high- than in the low-interference context. This effect could arise from an active or dormant mode of anticipation that seems to engage fronto-striatal and midbrain regions to flexibly adjust resources to task demands. Our results extend the model of conflict adaptation beyond trial-to-trial adjustments by showing that a high interference context affects both behavioral and biological aspects of cognition.
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| 7. |
- Wiens, Stefan, et al.
(author)
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Neural electrophysiological correlates of detection and identification awareness
- 2023
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In: Cognitive, Affective, & Behavioral Neuroscience. - : Springer Nature. - 1530-7026 .- 1531-135X. ; 23:5, s. 1303-1321
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Journal article (peer-reviewed)abstract
- Humans have conscious experiences of the events in their environment. Previous research from electroencephalography (EEG) has shown visual awareness negativity (VAN) at about 200 ms to be a neural correlate of consciousness (NCC). However, when considering VAN as an NCC, it is important to explore which particular experiences are associated with VAN. Recent research proposes that VAN is an NCC of lower-level experiences (detection) rather than higher-level experiences (identification). However, previous results are mixed and have several limitations. In the present study, the stimulus was a ring with a Gabor patch tilting either left or right. On each trial, subjects rated their awareness on a three-level perceptual awareness scale that captured both detection (something vs. nothing) and identification (identification vs. something). Separate staircases were used to adjust stimulus opacity to the detection threshold and the identification threshold. Bayesian linear mixed models provided extreme evidence (BF10 = 131) that VAN was stronger at the detection threshold than at the identification threshold. Mean VAN decreased from - 2.12 microV [- 2.86, - 1.42] at detection to - 0.46 microV [- 0.79, - 0.11] at identification. These results strongly support the claim that VAN is an NCC of lower-level experiences of seeing something rather than of higher-level experiences of specific properties of the stimuli. Thus, results are consistent with recurrent processing theory in that phenomenal visual consciousness is reflected by VAN. Further, results emphasize that it is important to consider the level of experience when searching for NCC.
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