One-year changes in brain microstructure differentiate preclinical Huntington's disease stages.

OBJECTIVE: To determine whether brain imaging markers of tissue microstructure can detect the effect of disease progression across the preclinical stages of Huntington's disease. METHODS: Longitudinal microstructural changes in diffusion imaging metrics (mean diffusivity and fractional anisotropy) were investigated in participants with presymptomatic Huntington's disease (N = 35) stratified into three preclinical subgroups according to their estimated time until onset of symptoms, compared with age- and gender-matched healthy controls (N = 19) over a 1y period. RESULTS: Significant differences were found over the four groups in change of mean diffusivity in the posterior basal ganglia and the splenium of the corpus callosum. This overall effect was driven by significant differences between the group far-from-onset (FAR) of symptoms and the groups midway- (MID) and near-the-onset (NEAR) of symptoms. In particular, an initial decrease of mean diffusivity in the FAR group was followed by a subsequent increase in groups closer to onset of symptoms. The seemingly counter-intuitive decrease of mean diffusivity in the group furthest from onset of symptoms might be an early indicator of neuroinflammatory process preceding the neurodegenerative phase. In contrast, the only clinical measure that was able to capture a difference in 1y changes between the preclinical stages was the UHDRS confidence in motor score. CONCLUSIONS: With sensitivity to longitudinal changes in brain microstructure within and between preclinical stages, and potential differential response to distinct pathophysiological mechanisms, diffusion imaging is a promising state marker for monitoring treatment response and identifying the optimal therapeutic window of opportunity in preclinical Huntington's disease

Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks

In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+) and healthy trauma-exposed controls (only mtDMN+). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function

Resilience, posttraumatic stress and recovery: insights from brain network dynamics

In our lifetimes we will encounter varying degrees of stress and traumatic experiences, and how we respond to them will strongly affect our well-being and mental health. While there is considerable literature on the neural correlates of stress-related mental disorders, there are only very few models including a perspective on the neural factors that render us vulnerable to stress and how therapeutic interventions can modulate brain activity to help us recover. This is in part due to the scarcity of longitudinal studies necessary to investigate both the trajectories of maladaptive changes that make us vulnerable as well as the positive changes that allow us to rebalance our brain back to healthy functioning. Moreover, only very few human neuroimaging studies have been able to incorporate a perspective on the temporal dimension of brain activation patterns. This is specially important given the highly dynamic nature of the stress response, and the current perspectives that propose fast shifts in large-scale brain networks as key in the production of an adaptive response to stress. In this thesis, we started by investigating brain function in relation to stress and stress vulnerability using a standard method of static functional connectivity on a longitudinal dataset of healthy soldiers exposed to stress (Chapter 2). This allowed us to define a starting point comparable to most of the available fMRI studies on stress and revealed a broad network of differences between stress conditions. We then used a recently developed method (i.e., Leading Eigenvector Dynamic Analysis) to investigate the stability and temporal dominance of discrete and recurrent functional connectivity patterns (or states) showing that increased temporal stability of a frontoparietal functional connectivity state was associated with stress vulnerability (Chapter 3). Exploration of transitions between the detected functional connectivity states showed that the increase in temporal stability was furthermore accompanied by decreased transitions from the frontoparietal to the default mode state in vulnerable participants (Chapter 4). We then applied a second state-of-the-art method (i.e., Hidden Markov Models) to detect recurrent activation patterns in a dataset of participants with PTSD before and after cognitive therapy. We showed that PTSD is associated with a decrease in the temporal dominance of activation patterns related to the default mode network. Moreover, cognitive therapy was related to normalization of these default mode activation patterns back to a level similar to that of healthy participants (Chapter 5). In the final chapter, we integrate our findings into a brain dynamic perspective of stress vulnerability, PTSD and recovery.</p

Resilience, posttraumatic stress and recovery: insights from brain network dynamics

In our lifetimes we will encounter varying degrees of stress and traumatic experiences, and how we respond to them will strongly affect our well-being and mental health. While there is considerable literature on the neural correlates of stress-related mental disorders, there are only very few models including a perspective on the neural factors that render us vulnerable to stress and how therapeutic interventions can modulate brain activity to help us recover. This is in part due to the scarcity of longitudinal studies necessary to investigate both the trajectories of maladaptive changes that make us vulnerable as well as the positive changes that allow us to rebalance our brain back to healthy functioning. Moreover, only very few human neuroimaging studies have been able to incorporate a perspective on the temporal dimension of brain activation patterns. This is specially important given the highly dynamic nature of the stress response, and the current perspectives that propose fast shifts in large-scale brain networks as key in the production of an adaptive response to stress. In this thesis, we started by investigating brain function in relation to stress and stress vulnerability using a standard method of static functional connectivity on a longitudinal dataset of healthy soldiers exposed to stress (Chapter 2). This allowed us to define a starting point comparable to most of the available fMRI studies on stress and revealed a broad network of differences between stress conditions. We then used a recently developed method (i.e., Leading Eigenvector Dynamic Analysis) to investigate the stability and temporal dominance of discrete and recurrent functional connectivity patterns (or states) showing that increased temporal stability of a frontoparietal functional connectivity state was associated with stress vulnerability (Chapter 3). Exploration of transitions between the detected functional connectivity states showed that the increase in temporal stability was furthermore accompanied by decreased transitions from the frontoparietal to the default mode state in vulnerable participants (Chapter 4). We then applied a second state-of-the-art method (i.e., Hidden Markov Models) to detect recurrent activation patterns in a dataset of participants with PTSD before and after cognitive therapy. We showed that PTSD is associated with a decrease in the temporal dominance of activation patterns related to the default mode network. Moreover, cognitive therapy was related to normalization of these default mode activation patterns back to a level similar to that of healthy participants (Chapter 5). In the final chapter, we integrate our findings into a brain dynamic perspective of stress vulnerability, PTSD and recovery.</p

Different types of COVID-19 misinformation have different emotional valence on Twitter

The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation (cures, virus, vaccine, politics, conspiracy theories, and other). Applying the 226 classifiers to the Twitter sample we identified 690,004 tweets. Instead of running the sentiment on all tweets we manually coded a random subset of 100 tweets for each classifier to increase the validity, reducing the dataset to 2,097 tweets. We found that only a minor part of the entire dataset was related to misinformation. Also, misinformation in general does not lean towards a certain emotional valence. However, looking at comparisons of emotional valence for different types of misinformation uncovered that misinformation related to “virus” and “conspiracy” had a more negative valence than “cures,” “vaccine,” “politics,” and “other.” Knowing from existing studies that negative misinformation spreads faster, this demonstrates that filtering for misinformation type is fruitful and indicates that a focus on “virus” and “conspiracy” could be one strategy in combating misinformation. As emotional contexts affect misinformation spreading, the knowledge about emotional valence for different types of misinformation will help to better understand the spreading and consequences of misinformation

Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks

In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+) and healthy trauma-exposed controls (only mtDMN+). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function

Correlations between expression of emotions and number of daily deaths due to Covid-19.

The expression of emotions on Twitter are not correlated with fluctuations in the number of daily deaths due to Covid-19 in any of the Nordic countries. (DOCX)</p

Definitions for key concepts framing the research questions.

Definitions for key concepts framing the research questions.</p

Distributions of emotional expression across the three different subsamples (Non-hashtagged, #Covid-19 and #misinformation).

The distributions of negative emotions saturate on the lower end in the non-hashtagged and #Covid-19 tweets, and the moderate to high ends in the #misinformation tweets. Positive emotions are more equally distributed in the non-hashtagged and #Covid-19 subsamples, but have a distribution clearly pronounced to the lower end in the #misinformation tweets. Fear does not appear to be a driving emotion in any of the conditions, but is concentrated especially in the lower end in the #Covid-19 condition. (JPEG)</p

Negative emotion interactions in the three subsamples.

a, b) In #Covid19 tweets, the correlation between Anger and Fear, as well as between Anger and Sadness, is weaker than in non-hashtagged tweets (p = .00001 for both). On the contrary, the correlation between Sadness and Fear is stronger in the #Covid than in the non-hashtagged tweets. b,c) The correlation between Anger and Fear, as well as between Anger and Sadness is stronger in the #misinformation than in the #Covid-19 subsample (p = .00001). No significant differences were found in the correlations between Sadness and Fear (p = .72462).</p