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It feels real: physiological responses to a stressful virtual reality environment and its impact on working memory.
BACKGROUND: Virtual reality (VR) is increasingly used to study and treat psychiatric disorders. Its fidelity depends in part on the extent to which the VR environment provides a convincing simulation, for example whether a putatively stressful VR situation actually produces a stress response. METHODS: We studied the stress response in 28 healthy men exposed either to a stressor VR elevator (which simulated travelling up the outside of a tall building and culminated in the participant being asked to step off the elevator platform), or to a control elevator. We measured psychological and physiological (salivary cortisol and alpha-amylase, blood pressure, pulse, skin conductance) stress indices. We also measured subsequent performance on the N-back task because acute stress has been reported to impact on working memory. RESULTS: Compared to participants in the control elevator, those in the external elevator had increases in skin conductance, pulse and subjective stress and anxiety ratings, altered heart rate variability, and a delayed rise in cortisol. N-back performance was unaffected. CONCLUSIONS: A putatively stressful VR elevator produces a physiological as well as a psychological stress response, supporting its use in the investigation and treatment of stress-related disorders, and its potential value as an experimental laboratory stressor.
Effects of ketamine treatment on suicidal ideation: a qualitative study of patients’ accounts following treatment for depression in a UK ketamine clinic
<jats:sec><jats:title>Objective</jats:title><jats:p>It is recognised that ketamine treatment can reduce suicidal ideation (SI) in people with depression, at least in the short term. However, information is lacking on patients’ perspectives on such effects. Studying these can contribute to greater understanding of the mechanisms underlying impact of ketamine treatment on SI. The aim of this study was to investigate patients’ reports of the impact of treatment on their SI, the duration of effects and possible mechanisms.</jats:p></jats:sec><jats:sec><jats:title>Design and setting</jats:title><jats:p>This qualitative study consisted of semi-structured interviews with patients who had received ketamine treatment for depression. Interview data were analysed thematically.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Fourteen patients (8 females, 6 males, aged 24–64 years) who had received treatment with ketamine for treatment-resistant depression, and had SI at the initiation of treatment. Two participants also had a diagnosis of bipolar type 1 and two of emotionally unstable personality disorder. Eight had a history of self-harm.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>SI reduced following ketamine treatment in 12 out of 14 participants for periods of a few hours following a single treatment to up to three years with ongoing treatment. Reduction of SI was variable in terms of extent and duration, and re-emergence of suicidal thoughts often occurred when treatment ceased. Participants’ accounts indicated that reduced SI was associated with improved mood and reduced anxiety, as were clarity of thought, focus and concentration, and ability to function. Participants reported experiencing some or all of these effects in various orders of occurrence.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Generally, ketamine treatment was experienced as effective in reducing SI, although duration of effects varied considerably. Patients’ perspectives indicated similarities in the mechanisms of reduction in SI, but some differences in their manifestation, particularly in relation to chronology. Experiences of this cohort suggest that reduced anxiety and improvement in ability to think and function were important mechanisms alongside, or in some cases independently of, improvement in mood. Further studies of patients’ experiences are required to gain enhanced understanding of the variability of effects of ketamine on SI and functionality.</jats:p></jats:sec>
The default-mode network (DMN) and its principal core hubs in the posterior midline cortices (PMC), i.e., the precuneus and the posterior cingulate cortex, play a critical role in the human brain structural and functional architecture. Because of their centrality, they are affected by a wide spectrum of brain disorders, e.g., Alzheimer's disease. Non-invasive electrophysiological techniques such as magnetoencephalography (MEG) are crucial to the investigation of the neurophysiology of the DMN and its alteration by brain disorders. However, MEG studies relying on band-limited power envelope correlation diverge in their ability to identify the PMC as a part of the DMN in healthy subjects at rest. Since these works were based on different MEG recording systems and different source reconstruction pipelines, we compared DMN functional connectivity estimated with two distinct MEG systems (Elekta, now MEGIN, and CTF) and two widely used reconstruction algorithms (Minimum Norm Estimation and linearly constrained minimum variance Beamformer). Our results identified the reconstruction method as the critical factor influencing PMC functional connectivity, which was significantly dampened by the Beamformer. On this basis, we recommend that future electrophysiological studies on the DMN should rely on Minimum Norm Estimation (or close variants) rather than on the classical Beamformer. Crucially, based on analytic knowledge about these two reconstruction algorithms, we demonstrated with simulations that this empirical observation could be explained by the existence of a spontaneous linear, approximately zero-lag synchronization structure between areas of the DMN or among multiple sources within the PMC. This finding highlights a novel property of the neural dynamics and functional architecture of a core human brain network at rest.
Author Correction: Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Multiple sclerosis (MS) is a demyelinating, neuroinflammatory, and -degenerative disease that affects the brain's neurophysiological functioning through brain atrophy, a reduced conduction velocity and decreased connectivity. Currently, little is known on how MS affects the fast temporal dynamics of activation and deactivation of the different large-scale, ongoing brain networks. In this study, we investigated whether these temporal dynamics are affected in MS patients and whether these changes are induced by the pathology or by the use of benzodiazepines (BZDs), an important symptomatic treatment that aims at reducing insomnia, spasticity and anxiety and reinforces the inhibitory effect of GABA. To this aim, we employed a novel method capable of detecting these fast dynamics in 90 MS patients and 46 healthy controls. We demonstrated a less dynamic frontal default mode network in male MS patients and a reduced activation of the same network in female MS patients, regardless of BZD usage. Additionally, BZDs strongly altered the brain's dynamics by increasing the time spent in the deactivating sensorimotor network and the activating occipital network. Furthermore, BZDs induced a decreased power in the theta band and an increased power in the beta band. The latter was strongly expressed in those states without activation of the sensorimotor network. In summary, we demonstrate gender-dependent changes to the brain dynamics in the frontal DMN and strong effects from BZDs. This study is the first to characterise the effect of multiple sclerosis and BZDs in vivo in a spatially, temporally and spectrally defined way.
Fast analysis method for non-invasive imaging of blood flow using vessel-encoded arterial spin labelling
Arterial spin labelling (ASL) MRI offers a non-invasive means to create blood-borne contrast in vivo for dynamic angiographic imaging. By spatial modulation of the ASL process it is possible to uniquely label individual arteries over a series of measurements, allowing each to be separately identified in the resulting images. This separation requires appropriate analysis for which a general framework has previously been proposed. Here the general framework is modified for fast analysis of non-invasive imaging of blood flow using vessel encoded arterial spin labelling (VE-ASL). This specifically addresses the issues of computational speed of the analysis and the robustness required to deal with real patient data. The modification applies various approaches for estimation of one or more parameters that change the way a vessel, for example an artery, is encoded to provide the fast analysis.
Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity.
Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence, and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology.
Longitudinal Brain Atrophy Rates in Transient Ischemic Attack and Minor Ischemic Stroke Patients and Cognitive Profiles.
Introduction: Patients with transient ischemic attack (TIA) and minor stroke demonstrate cognitive impairment, and a four-fold risk of late-life dementia. Aim: To study the extent to which the rates of brain volume loss in TIA patients differ from healthy controls and how they are correlated with cognitive impairment. Methods: TIA or minor stroke patients were tested with a neuropsychological battery and underwent T1 weighted volumetric magnetic resonance imaging scans at fixed intervals over a 3 years period. Linear mixed effects regression models were used to compare brain atrophy rates between groups, and to determine the relationship between atrophy rates and cognitive function in TIA and minor stroke patients. Results: Whole brain atrophy rates were calculated for the TIA and minor stroke patients; n = 38 between 24 h and 18 months, and n = 68 participants between 18 and 36 months, and were compared to healthy controls. TIA and minor stroke patients demonstrated a significantly higher whole brain atrophy rate than healthy controls over a 3 years interval (p = 0.043). Diabetes (p = 0.012) independently predicted higher atrophy rate across groups. There was a relationship between higher rates of brain atrophy and processing speed (composite P = 0.047 and digit symbol coding P = 0.02), but there was no relationship with brain atrophy rates and memory or executive composite scores or individual cognitive tests for language (Boston naming, memory recall, verbal fluency or Trails A or B score). Conclusion: TIA and minor stroke patients experience a significantly higher rate of whole brain atrophy. In this cohort of TIA and minor stroke patients changes in brain volume over time precede cognitive decline.
Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation. 72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data. The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p < 0.001), lower CBF and ATT variance (p < 0.001), increased SNR (p < 0.001), and improved repeatability (p < 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data. The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.
BACKGROUND: Treatment approaches for patients with psychosis need major improvement. Our approach to improvement is twofold: target putative causal mechanisms for psychotic experiences that are treatable and also that patients wish treated. This leads to greater treatment engagement and clinical benefit. To inform mental health service provision we assessed the presence of treatable causal mechanisms and patient treatment preferences. METHODS: Patients with non-affective psychosis attending NHS mental health services completed assessments of paranoia, hallucinations, anxious avoidance, worry, self-esteem, insomnia, analytic reasoning, psychological well-being, and treatment preferences. RESULTS: 1809 patients participated. Severe paranoia was present in 53.4% and frequent voices in 48.2%. Of the causal mechanisms, severe worry was present in 67.7%, avoidance at agoraphobic levels in 64.5%, analytic reasoning difficulties in 55.9%, insomnia in 50.1%, poor psychological well-being in 44.3%, strongly negative self-beliefs in 36.6%, and weak positive self-beliefs in 19.2%. Treatment target preferences were: feeling happier (63.2%), worrying less (63.1%), increasing self-confidence (62.1%), increasing activities (59.6%), improving decision-making (56.5%), feeling safer (53.0%), sleeping better (52.3%), and coping with voices (45.3%). Patients with current paranoia and/or hallucinations had higher levels of the causal factors and of wanting these difficulties treated. CONCLUSIONS: Patients with non-affective psychosis have high levels of treatable problems such as agoraphobic avoidance, worry, low self-esteem, and insomnia and they would like these difficulties treated. Successful treatment of these difficulties is also likely to decrease psychotic experiences such as paranoia.