67 research outputs found
Towards exercise as a transdiagnostic mood boosting intervention: Links to daily life emotional memory and neural plasticity.
No full textDepression, anxiety, burnout, and other stress-related illnesses are becoming a major cause of disability globally. However, current treatments, including pharmacology and psychotherapy, show high resistance and relapse rates. A transdiagnostic approach that targets underlying mechanisms may provide better results than current treatments. One potential avenue that fits a transdiagnostic approach is exercise as a mood boosting treatment, which can serve as a low-cost and easily accessible tool in treating and preventing mental illness. Exercise has been shown to boost mood, and alleviate symptoms of depression and anxiety, though the mechanisms of how this happens are not well understood. One theory is that exercise acts to modify emotional memory processing, which is often disrupted in psychiatric illness. Laboratory results have shown some support for this argument, yet results from the lab remain mixed, higlhighitng a need to expand research into real-life settings. In addition to understanding the cognitive mechanisms, the neural pathways that are involved in this process are also not well understood. The relationship between exercise and mood and memory may be due to increased neural plasticity, as increased levels of brain-derived neurotrophic factors (BDNF) are often reported following exercise, which are linked to plasticity in the hippocampus. The hippocampus is a central episodic memory hubs, with connections to the default mode network (DMN). The DMN is involved in autobiographical memory and disruptions in this network are reported in a wide range of psychiatric illnesses. By stimulating neural plasticity in the hippocampus through exercise, it may lead to changes in DMN connectivity and alter emotional memory, thereby elevating mood. However, this overarching model encompassing the lab and real-life, as well as examining these processes involved, have yet to be investigated thoroughly
Manifestation of memory bias in daily life in clinically depressed and remitted individuals
No full textDepression is characterized by a cognitive bias toward negative memory recall (Beck, 1969; Kircanski et al., 2012; LeMoult & Gotlib, 2019). This cognitive bias may last beyond depressive episodes and indicates a risk of relapse in those who are in remission (Everaert et al., 2022; Ingram, 1984; Ronold et al., 2020). While earlier studies have primarily concentrated on negative memory bias (MB) in depression (Beck, 1969; Duyser et al., 2020; Hertel, 2004; Kircanski et al., 2012), less attention has been given to positive MB formation. Recent studies suggest that the absence of positive MB may be a crucial factor distinguishing depressed and never-depressed individuals (Askelund et al., 2019; Everaert et al., 2022; Gaddy & Ingram, 2014), making it a potential target for novel treatment approaches. Worth noting, however, is that research on the prevalence of MB in those remitted from depression has produced inconsistent results (Everaert et al., 2022; Hallford et al., 2022; Romero et al., 2014).This may occur because previous research on MB has primarily been conducted in laboratory settings (Lin et al., 2021; Ridout et al., 2003; Zupan et al., 2017), neglecting the impact of daily-life stressors and context on MB processes. Indeed, contextual factors have been shown to play an important role in this (Vrijsen et al., 2021). Incorporating daily-life context such as stressors in the research design may clarify the presence or absence of MB in this population, as daily-life processes may serve to activate latent depressogenic schemas (Everaert et al., 2022; Fritzsche et al., 2010; Kircanski et al., 2012; Rude et al., 2001; Timbremont & Braet, 2004). Daily life fluctuations in MB under changing contexts can be investigated using ecological momentary assessments (EMA). EMA involves conducting multiple assessments per day during the participant's daily life, providing a more ecologically valid approach to examining MB (Csikszentmihalyi & Larson, 1987). Assessing memory bias and depressotypic negative affect through EMA also allows for the examination of temporaneous and contemporaneous effects, including emotional inertia, which may affect cognitive emotional processing (Brose et al., 2015; Kuppens et al., 2010) . The aim for this pre-registration is to investigate memory bias (MB) in depression in a contextually dependent manner using data from the MEDAL study (Vrijsen et al., 2021). In the MEDAL study, never depressed, depressed, and remitted participants were followed in daily life using EMA questions probing affect, memory bias, and stress. Data will be analyzed to investigate how these dynamics play out in real-life settings
Classification of coastal profile development in the Hoogheemraadschap Hollands Noorderkwartier area: Using advanced data analysis techniques
No full textThis thesis describes the development and the characteristics of the coastline along the Dutch coastal zone, managed by Hoogheemraadschap Hollands Noorderkwartier. The coastal zone is a rich environment with multiple stakeholders. These stakeholders use the coastal zone for a range of functions such as safety, nature, business, and recreation. Coastal dunes have been the first line of defense against the sea for many years. On decadal and intercentennial time scale, climate change (e.g. sea level rise) and human intervention (e.g. nourishment), affect the variability of the coastline in different ways. Reports have shown an increase of the mean sea level, which is expected to increase even after the year 2100. Bruun was one of the first researchers, who found a relation between sea level rise and shoreline recession. While Bruuns findings contain the fundamental adaption of the coastline due to sea level rise, the coastline remains a highly complex system. Morphological data has been collected yearly of the Dutch coast as part of the JarKus program. Meanwhile, the data collection and computational power have increased exponentially, while the computational cost has gone down. Combining the newly computational power with the extensive JarKus data set, provides new insight into the complex coastal system. The derived variables from the JarKus data set range from widths, gradients, volumes to heights. These derived variables are combined with nourishment into a high-dimensional data set. Clusters of comparable coastal profiles in the Hoogheemraadschap Hollands Noorderkwartier area, have been made using machine learning techniques. Machine learning techniques such as K-means and the Self-Organizing Map (SOM) contain an intelligence, with the capability of clustering similar high-dimensional objects or data, without knowing the desired output. While both methods have the same goal, K-means moves its centroids inside the data and the SOM moves the data to its centroids (BMU). The advantage of combining both methods, is to keep the topological preservation while having the 'hard' clustering advantage of the K-means, which provides easy interpretation and therefore computations. The coastline of the Noorderkwartier can be broken up into nine clusters. Five of these clusters are classified as main-clusters, having a large number of transects. Four clusters are classified as sub-clusters, having just a few transects. Each of the clusters contains its own characteristic variables. Each of these characteristics originates from its own long-term natural and human-induced causes. The variable dominating the general clustering, is the active profile of the coastline. The lesser dominating variables are the foreshore nourishment, depth of closure and increase in foreshore volume. Meaning that the high-dimensional data set, find their similarities due to these dominant variables. Upon further investigation, the clusters containing the highest active profiles, were also the clusters containing historical larger nourishments. Comparing the yearly change of the standard deviation, shows that the clusters containing larger historical nourishment, have a shifting depth of closure. With respect to the dunes, correlations are found between the dune foot, y-coordinate of the boundary between marine and aeolian transport, dune volume and active width of the dune. While the exact reason for this correlation is still unknown, it shows potential for further research. The results of this research contribute to another step in understanding the complex coastal zone. Hoogheemraadschap Hollands Noorderkwartier can now adjust its policy and approach for each cluster based on the results of this research. For further research, it is now possible to focus on specific clusters with their unique characteristics. Lastly, results highlight the importance of the effect of nourishment on the active profile and with this the future dynamic equilibrium of the coastline.Civil Engineering | Hydraulic Engineerin
Pre-and post-therapy assessment of clinical outcomes and white matter integrity in autism spectrum disorder: Pilot study
No full textObjective: This pilot study aims to identify white matter (WM) tract abnormalities in Autism Spectrum Disorders (ASD) toddlers and pre-schoolers by Diffusion Tensor Imaging (DTI), and to correlate imaging findings with clinical improvement after early interventional and Applied Behavior Analysis (ABA) therapies by Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP). Methods: DTI scans were performed on 17 ASD toddlers/pre-schoolers and seven age-matched controls. Nine ASD patients had follow-up MRI 12 months following early intervention and ABA therapy. VB-MAPP was assessed and compared at diagnosis, 6 and 12 months after therapies. Tract-Based Spatial Statistics (TBSS) was used to measure fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial (RD) diffusivity. Results: VB-MAPP scores improved at 6 and 12 months after early intervention and ABA therapy compared to scores at baseline. TBSS analysis showed significant FA decrease and/or RD increase in ASD patients before therapy vs. controls in inferior fronto-occipital fasciculi, uncinate fasciculi, left superior fronto-occipital fasciculus, forceps minor, left superior fronto-occipital fasciculus, right superior longitudinal fasciculus, corona radiate bilaterally, and left external capsule. A significantly FA increase in 21 tracts and ROIs is reported in post-vs. pre-therapy DTI analysis. Conclusion: DTI findings highlighted ASD patient WM abnormalities at diagnosis and confirmed the benefits of 12 months of early intervention and ABA therapy on clinical and neuro imaging outcomes. © 2019 Saaybi, AlArab, Hannoun, Saade, Tutunji, Zeeni, Shbarou, Hourani and Boustany
Being Physically Active Is Associated with More Positive Memories and Better Mood States in Daily Life
No full textPhysical activity benefits mental health, with emotional memory and repetitive negative thinking (RNT) as potential mechanisms. This study expands upon prior laboratory studies by examining how physical activity is associated with emotional memory and RNT in daily life, utilizing the experience sampling method (ESM).
Methods. A community sample (N = 112) was prompted four times daily for seven consecutive days to report their recent physical activity duration and intensity, recalled emotional events, RNT, and current positive and negative mood states.
Results. Participants rated recalled events as more pleasant when they had been physically active for longer durations or at higher intensities in the preceding hours. No evidence was found for an association between physical activity and RNT. Confirming the mood-enhancing effect of physical activity, both activity duration and intensity were negatively associated with negative mood, and intensity positively with positive mood.
Conclusions. The results indicate that physical activity duration and intensity are positively associated with emotional memory and mood states. This confirms the mental health advantages of physical activity using daily life data. Future research should further investigate emotional memory as a mechanism underlying the protective effects of physical activity on mental health
MeReL: Modelling and Predicting Resilience in Real Life
No full textStress-related disorders represent severe mental illnesses that constitute a heavy burden for the individual and society. The ongoing transition in clinical strategy from treatment towards prevention and health maintenance has increased the focus on understanding and enhancing stress resilience. Prior research focused on identifying subjective measures that predict resilience following stress exposure (resilience factors). However, predictors from objective measures and real-life resilience markers are lacking. Additionally, most studies have been conducted on non-representative samples. We aim to reduce this gap using the dataset of the Healthy Brain Study (HBS; https://www.healthybrainstudy.nl). This large cohort study tracked for one year over 700 individuals from diverse backgrounds, conducting extensive assessments in both laboratory and real-life environments. In this project, we aim to (1) validate a measure of resilience using the HBS data, (2) replicate and extend earlier works on resilience factors and resilience mechanisms, and (3) investigate dynamic changes in resilience following stress exposure. Building on recent theories, we conceptualize resilience as the maintenance of mental health despite exposure to stressors. We operationalize it using normative modeling as the inverse residual of stress-related mental health symptoms regressed onto stress exposure. The calculation of the resilience scores was performed before the submission of this preregistration. We aim to test the association between resilience and resilience factors using (1) self-reported trait measures, resilience mechanisms using (2) cognitive regulation of emotion, operationalized with startle EMG in an emotion regulation task, and (3) affective reactivity to real-life acute stressors using ecological momentary assessment
Manifestation of memory bias in daily life in clinically depressed and remitted individuals
No full textManifestation of memory bias in daily life in currently and remitted depressed individuals: How accurate is recall of past mood states?
No full textDepression is characterized by a loss of positive and pronounced negative memory bias, which persists after remission. While theoretical accounts of depressive realism, emotional inertia, and mood-congruency substantiate the compelling evidence of weakpositive memory in depression, they cannotfully explainnegative memory bias in depression. We appliedan Ecologically Momentary Assessment (EMA) measure of memory bias to provide insight into the accuracy and depression state-dependency of recall of previous positive and negative mood states. Currently-(n=46), remitted-(n=90), and never-depressed individuals (n=55) provided positive and negative mood ratings (7x/day for six days), while also recalling their recent(i.e., previous prompt; 3x/day) or distal (i.e., one day lag; 1x/day) mood states. Currently depressed individuals displayed most accuracy and hence least bias in recall of both positive and negative mood; with accuracy in currently and remitted depressed individuals being independent of their current mood state. Conversely, mood at the time of recall significantly related to memory accuracy among never-depressed individuals with more negative mood,resulting in a depressotypic memory bias. Results are consistent with depressive realism and mood-congruency accounts,as well as with evidence for loss of positive memory bias (but not for negative memory bias) in depressio
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