Psychological problems like procrastination, perfectionism, low self-esteem, test anxiety and stress are common among college students. There are evidence-based interventions available for these... Show morePsychological problems like procrastination, perfectionism, low self-esteem, test anxiety and stress are common among college students. There are evidence-based interventions available for these problems that not only have direct effects on these problems, but also indirect effects on mental disorders such as depression and anxiety disorders. Targeting these psychological problems may offer new opportunities to prevent and treat mental disorders in a way that is less stigmatizing to students. In this study we examined the association of five psychological problems with five common mental disorders (panic, generalized anxiety, bipolar, major depressive, and substance use disorder) in a sample of 2,449 students from two Dutch universities. Psychological problems were measured with one item for each problem and mental disorders were measured with the Composite International Diagnostic Interview Screening Scales. Associations were examined with Poisson regression models as relative risks (RR) of the disorders as a function of the psychological problems. The population attributable fraction (PAF) indicates by what percentage the prevalence of the mental disorder would be reduced if the psychological problem was addressed successfully by an intervention. Especially generalized anxiety disorder was strongly associated with psychological problems (strong associations with stress and low self-esteem and moderately with test anxiety). The group with three or more psychological problems had a strongly increased risk for generalized anxiety (RR = 11.25; 95% CI: 7.51-16.85), and a moderately increase risk for major depression (RR = 3.22; 95% CI: 2.63-3.95), panic disorder (RR = 3.19; 95% CI: 1.96-5.20) and bipolar disorder (RR = 3.66; 95% CI: 2.40-5.58). The PAFs for having any of the psychological problems (one or more) were considerable, especially for generalized anxiety (60.8%), but also for panic disorder (35.1%), bipolar disorder (30.6%) and major depression (34.0%). We conclude that common psychological problems are associated with mental disorders and with each other. After adjustment, psychological problems are associated with different patterns of mental disorders. If the impact of the psychological problems could be taken away, the prevalence of several mental disorders would be reduced considerably. The psychological problems may provide a promising target to indirectly prevent and intervene in psychopathology in hard to reach college students with mental disorders. Show less
Cuijpers, P.; Miguel, C.; Ciharova, M.; Aalten, P.; Batelaan, N.; Salemink, E.; ... ; Karyotaki, E. 2021
We conducted an umbrella review of 31 meta-analyses with 608 primary studies, examining the effects of psychological interventions for prevention and treatment of mental and psychological problems... Show moreWe conducted an umbrella review of 31 meta-analyses with 608 primary studies, examining the effects of psychological interventions for prevention and treatment of mental and psychological problems in college students. The proportion of unique primary studies included in the meta-analyses ranged from 6 to 100%. For problems like depression, anxiety, and stress, effective universal, indicated, and treatment interventions are available. For alcohol problems effects are small and it is not clear if these are clinically relevant. Effective interventions have been developed for smoking cessation, test-anxiety, internet addiction, procrastination, and bystander sexual assault prevention programs. The quality of most metaanalyses and almost all primary studies was suboptimal. Therefore, all findings have to be considered with caution. Show less
Bron, E.E.; Klein, S.; Papma, J.M.; Jiskoot, L.C.; Venkatraghavan, V.; Linders, J.; ... ; Lugt, A. van der 2021
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD... Show moreThis work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive preprocessing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924-0.955) and CNN (0.933; 95%CI: 0.918-0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p < 0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855-0.932) and CNN (0.876; 95%CI: 0.836-0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p = 0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice. Show less
Objectiv(e): To investigate the relationship between Alzheimer's disease biomarkers and neuropsychiatric symptoms. Methods: Data from two large cohort studies, the Dutch Parelsnoer Institute -... Show moreObjectiv(e): To investigate the relationship between Alzheimer's disease biomarkers and neuropsychiatric symptoms. Methods: Data from two large cohort studies, the Dutch Parelsnoer Institute - Neurodegenerative Diseases and the Alzheimer's Disease Neuroimaging Initiative was used, including subjects with subjective cognitive decline (N= 650), mild cognitive impairment (N = 887), and Alzheimer's disease dementia (N = 626). Cerebrospinal fluid (CSF) levels of A beta(42), t-tau, p-tau, and hippocampal volume were associated with neuropsychiatric symptoms (measured with the Neuropsychiatric Inventory) using multiple logistic regression analyses. The effect of the Mini-Mental State Examination (as proxy for cognitive functioning) on these relationships was assessed with mediation analyses. Results: Alzheimer's disease biomarkers were not associated with depression, agitation, irritability, and sleep disturbances. Lower levels of CSF A beta(42), higher levels of t- and p-tau were associated with presence of anxiety. Lower levels of CSF A beta(42) and smaller hippocampal volumes were associated with presence of apathy. All associations were mediated by cognitive functioning. Conclusion: The association between Alzheimer's disease pathology and anxiety and apathy is partly due to impairment in cognitive functioning. Show less
Introduction: It has been suggested that the development of post-stroke apathy (PSA) and depression (PSD) may be more strongly associated with generalised brain pathology, rather than the stroke... Show moreIntroduction: It has been suggested that the development of post-stroke apathy (PSA) and depression (PSD) may be more strongly associated with generalised brain pathology, rather than the stroke lesion itself. The present study aimed to investigate associations between imaging markers of lesion-related and generalised brain pathology and the development of PSA and PSD during a one-year follow-up.Patients and methods: In a prospective cohort study, 188 stroke patients received 3-Tesla MRI at baseline (three months post-stroke) for evaluation of lesion-related, vascular, and degenerative brain pathology. Presence of lacunes, microbleeds, white matter hyperintensities, and enlarged perivascular spaces was summed to provide a measure of total cerebral small vessel disease (cSVD) burden (range 0-4). The Mini International Neuropsychiatric Interview and Apathy Evaluation Scale were administered at baseline and repeated at 6- and 12-month follow-up to define presence of PSD and PSA, respectively.Results: Population-averaged logistic regression models showed that global brain atrophy and severe cSVD burden (score 3-4) were significantly associated with the odds of having PSA (ORGEE 5.33, 95% CI 1.99-14.25 and 3.04, 95% CI 1.20-7.69, respectively), independent of stroke lesion volume and co-morbid PSD. Medium cSVD burden (score 2) was significantly associated with the odds of having PSD (ORGEE 2.92, 95% CI 1.09-7.78), independent of stroke lesion volume, co-morbid PSA, and pre-stroke depression. No associations were found with lesion-related markers.Conclusions: The results suggest that generalised degenerative and vascular brain pathology, rather than lesion-related pathology, is an important predictor for the development of PSA, and less strongly for PSD. Show less
Beek, M. van de; Steenoven, I. van; Ramakers, I.H.G.B.; Aalten, P.; Koek, H.L.; Rikkert, M.G.M.O.; ... ; Flier, W.M. van der 2019
Background: Quality of Life (QoL) is an important outcome measure in dementia, particularly in the context of interventions. Research investigating longitudinal QoL in dementia with Lewy bodies ... Show moreBackground: Quality of Life (QoL) is an important outcome measure in dementia, particularly in the context of interventions. Research investigating longitudinal QoL in dementia with Lewy bodies (DLB) is currently lacking.Objective: To investigate determinants and trajectories of QoL in DLB compared to Alzheimer's disease (AD) and controls.Methods: QoL was assessed annually in 138 individuals, using the EQ5D-utility-score (0-100) and the health-related Visual Analogue Scale (VAS, 0-100). Twenty-nine DLB patients (age 69 +/- 6), 68 AD patients (age 70 +/- 6), and 41 controls (age 70 +/- 5) were selected from the Dutch Parelsnoer Institute-Neurodegenerative diseases and Amsterdam Dementia Cohort. We examined clinical work-up over time as determinants of QoL, including cognitive tests, neuropsychiatric inventory, Geriatric Depression Scale (GDS), and disability assessment of dementia (DAD).Results: Mixed models showed lower baseline VAS-scores in DLB compared to AD and controls (AD: beta +/- SE = - 7.6 +/- 2.8, controls: beta +/- SE = -7.9 +/- 3.0, p < 0.05). An interaction between diagnosis and time since diagnosis indicated steeper decline on VAS-scores for AD patients compared to DLB patients (beta +/- SE = 2.9 +/- 1.5, p < 0.1). EQ5D-utility-scores over time did not differ between groups. Higher GDS and lower DAD-scores were independently associated with lower QoL in dementia patients (GDS: VAS beta +/- SE = -1.8 +/- 0.3, EQ5D-utility beta +/- SE = -3.7 +/- 0.4; DAD: VAS = 0.1 +/- 0.0, EQ5D-utility beta +/- SE = 0.1 +/- 0.1, p < 0.05). No associations between cognitive tests and QoL remained in the multivariate model.Conclusion: QoL is lower in DLB, while in AD QoL shows steeper decline as the disease advances. Our results indicate that non-cognitive symptoms, more than cognitive symptoms, are highly relevant as they impact QoL. Show less
