Online cognitive monitoring technology for people with Parkinson's disease and REM sleep behavioural disorder.

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson's Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.

Remote digital cognitive assessment reveals cognitive deficits related to hippocampal atrophy in autoimmune limbic encephalitis: a cross-sectional validation study.

Background: Autoimmune limbic encephalitis (ALE) is a neurological disease characterised by inflammation of the limbic regions of the brain, mediated by pathogenic autoantibodies. Because cognitive deficits persist following acute treatment of ALE, the accurate assessment of long-term cognitive outcomes is important for clinical assessments and trials. However, evaluating cognition is costly and an unmet need exists for validated digital methods. Methods: In this cross-sectional validation study, we investigated whether a remote digital platform could identify previously characterised cognitive impairments in patients with chronic ALE and whether digital metrics would correlate with standard neuropsychological assessment and hippocampal volume. Patients with ALE who had a chronic and stable presentation and received a clinical diagnosis of ALE were recruited for this study. The cognitive performance of 21 patients with ALE and 54 age-matched healthy controls - enrolled via the University of Oxford (UK) Cognitive Neurology Lab testing programme - was assessed with a battery of 12 cognitive tasks from the Cognitron online platform. The platform was optimised with National Institute for Health and Care Research (NIHR) support to be deliverable remotely to elderly and patient groups. The primary outcome measure was behavioural performance and corresponding neuroimaging and neuropsychological assessment metrics. Findings: Between February 15, 2021, and April 21, 2022, 21 patients with ALE (mean age 63.01 years, 14 males) and 54 healthy controls (mean age 65.56 years, 23 males) completed the digital cognitive assessment. Patients with ALE performed significantly worse in memory, visuospatial abilities, executive function, and language. No impairments in digit & spatial span, target detection (attention) and emotion discrimination were observed. The global score on the online cognitive tasks correlated significantly with the established Addenbrooke's Cognitive Examination III (ACE) pen-and-paper test. Deficits in visuospatial processing and language were identified in ALE compared to controls using remote digital testing but not using the ACE, highlighting higher sensitivity of computerised testing to residual cognitive impairment. Finally, the hippocampal volumes of patients with ALE and healthy controls correlated with online cognitive scores. Interpretation: These findings demonstrate that subtle cognitive deficits in patients with chronic ALE, who often show full recovery in measures of disability and dependence on daily activities, are detectable using a remote online platform, which also relates to hippocampal atrophy. Such methods may facilitate the characterisation of cognitive profiles in complex neurological diseases. Future longitudinal studies designed to assess the utility of such digital methods for further clinical characterisation are needed. Funding: The Wellcome Trust, Medical Research Council, National Institute for Health Research, Rhodes Scholarship, and the Berrow Foundation Scholarship.

Cognition and Memory after Covid-19 in a Large Community Sample.

BACKGROUND: Cognitive symptoms after coronavirus disease 2019 (Covid-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are well-recognized. Whether objectively measurable cognitive deficits exist and how long they persist are unclear. METHODS: We invited 800,000 adults in a study in England to complete an online assessment of cognitive function. We estimated a global cognitive score across eight tasks. We hypothesized that participants with persistent symptoms (lasting ≥12 weeks) after infection onset would have objectively measurable global cognitive deficits and that impairments in executive functioning and memory would be observed in such participants, especially in those who reported recent poor memory or difficulty thinking or concentrating ("brain fog"). RESULTS: Of the 141,583 participants who started the online cognitive assessment, 112,964 completed it. In a multiple regression analysis, participants who had recovered from Covid-19 in whom symptoms had resolved in less than 4 weeks or at least 12 weeks had similar small deficits in global cognition as compared with those in the no-Covid-19 group, who had not been infected with SARS-CoV-2 or had unconfirmed infection (-0.23 SD [95% confidence interval {CI}, -0.33 to -0.13] and -0.24 SD [95% CI, -0.36 to -0.12], respectively); larger deficits as compared with the no-Covid-19 group were seen in participants with unresolved persistent symptoms (-0.42 SD; 95% CI, -0.53 to -0.31). Larger deficits were seen in participants who had SARS-CoV-2 infection during periods in which the original virus or the B.1.1.7 variant was predominant than in those infected with later variants (e.g., -0.17 SD for the B.1.1.7 variant vs. the B.1.1.529 variant; 95% CI, -0.20 to -0.13) and in participants who had been hospitalized than in those who had not been hospitalized (e.g., intensive care unit admission, -0.35 SD; 95% CI, -0.49 to -0.20). Results of the analyses were similar to those of propensity-score-matching analyses. In a comparison of the group that had unresolved persistent symptoms with the no-Covid-19 group, memory, reasoning, and executive function tasks were associated with the largest deficits (-0.33 to -0.20 SD); these tasks correlated weakly with recent symptoms, including poor memory and brain fog. No adverse events were reported. CONCLUSIONS: Participants with resolved persistent symptoms after Covid-19 had objectively measured cognitive function similar to that in participants with shorter-duration symptoms, although short-duration Covid-19 was still associated with small cognitive deficits after recovery. Longer-term persistence of cognitive deficits and any clinical implications remain uncertain. (Funded by the National Institute for Health and Care Research and others.).

Conceptualising compulsivity through network analysis: A two-sample study.

Compulsivity is a transdiagnostic construct crucial to understanding multiple psychiatric conditions and problematic repetitive behaviours. Despite being identified as a clinical- and research-relevant construct, there are limited insights into the internal conceptual structure of compulsivity. To provide a more nuanced understanding of compulsivity, the current study estimated the structure of compulsivity (indexed using the previously validated Cambridge-Chicago Compulsivity Trait Scale, CHI-T) among two large-scale and geographically distinct samples using the network estimation method. The samples consisted of a United Kingdom cohort (n = 122,346, 51.4% female, Mean age = 43.7, SD = 16.5, range = 9-86 years) and a South Africa cohort (n = 2674, 65.6% female, Mean age = 24.6, SD = 8.6, range = 18-65 years). Network community analysis demonstrated that compulsivity was constituted of three interrelated dimensions, namely: perfectionism, cognitive rigidity and reward drive. Further, 'Completion leads to soothing' and 'Difficulty moving from task to task' were identified as core (central nodes) to compulsivity. The dimensional structure and central nodes of compulsivity networks were consistent across the two samples. These findings facilitate the conceptualisation and measurement of compulsivity and may contribute to the early detection and treatment of compulsivity-related disorders.

The effects of COVID-19 on cognitive performance in a community-based cohort: a COVID symptom study biobank prospective cohort study.

Background: Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Methods: Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. Findings: 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, ß = -0.14 standard deviations, SDs, 95% confidence intervals, CI: -0.21, -0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, ß = -0.22 SDs, 95% CI: -0.35, -0.09). Effects were comparable to hospital presentation during illness (N = 281, ß = -0.31 SDs, 95% CI: -0.44, -0.18), and 10 years age difference (60-70 years vs. 50-60 years, ß = -0.21 SDs, 95% CI: -0.30, -0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. Interpretation: Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. Funding: Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer's Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency.

Mapping the sociodemographic distribution and self-reported justifications for non-compliance with COVID-19 guidelines in the United Kingdom.

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples' underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly.

Associations between the use of psychedelics and other recreational drugs with mental health and resilience during the COVID-19 pandemic.

The large-scale disruption to peoples' daily lives during the COVID-19 pandemic provides a context for examining whether use of substances such as psychedelics in a naturalistic (outside of a controlled environment) setting, is associated with better mental wellbeing and resilience relative to those who use other drugs, or who do not use drugs at all. We interrogate data from the Great British Intelligence Test and identify that 7.8% out of N = 30,598 unique respondents used recreational drugs inclusive of psychedelics, cannabis, cocaine, and MDMA during the COVID-19 pandemic. Recruitment materials did not mention drug use would be surveyed, thereby enabling us to model the relationship with mood and resilience in people who had not specifically self-selected themselves for a 'drug' study. We report that people form clusters, characterized by different real-world patterns of drug use, and the majority of psychedelics users also use cannabis. However, a subset of cannabis users do not use psychedelics, enabling a subtractive comparison. Those who primarily used psychedelics and cannabis during the COVID-19 pandemic had worse mood self-assessment and resilience scores compared to those who never used drugs or primarily used cannabis. This pattern was also evident for other recreational drug use clusters, except for those who primarily used MDMA and cannabis, who had better mood but were of too low incidence to have confidence in this estimate. These findings cast light on the significant differences in mental wellbeing between users of different drugs and the non-user population during a global-crisis and call for future research to explore the pharmacological, contextual and cultural variables associated with these differences, their generalisability and causal links with greater precision.

Computerised cognitive assessment in patients with traumatic brain injury: an observational study of feasibility and sensitivity relative to established clinical scales.

Background: Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. Methods: This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary's Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George's Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients' performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary's Hospital TBI clinic in London (UK). Findings: The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.99; p < 0.001). Interpretation: Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. Funding: This work was funded by a National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).

Measuring Compulsivity as a Self-Reported Multidimensional Transdiagnostic Construct: Large-Scale (N = 182,000) Validation of the Cambridge-Chicago Compulsivity Trait Scale.

Compulsivity has potential transdiagnostic relevance to a range of psychiatric disorders, but it has not been well-characterized and there are few existing measures available for measuring the construct across clinical and nonclinical samples that have been validated at large population scale. We aimed to characterize the multidimensional latent structure of self-reported compulsivity in a population-based sample of British children and adults (N = 182,145) using the Cambridge-Chicago Compulsivity Trait Scale (CHI-T). Exploratory structural equation modeling provided evidence for a correlated two-factor model consisting of (a) Perfectionism and (b) Reward Drive dimensions. Evidence was obtained for discriminant validity in relation to the big five personality dimensions and acceptable test-retest reliability. The CHI-T, here validated at extremely large scale, is suitable for use in studies seeking to understand the correlates and basis of compulsivity in clinical and nonclinical participants. We provide extensive normative data to facilitate interpretation in future studies.

Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations.

Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS.

A guided multiverse study of neuroimaging analyses.

For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.

Multivariate profile and acute-phase correlates of cognitive deficits in a COVID-19 hospitalised cohort.

Background: Preliminary evidence has highlighted a possible association between severe COVID-19 and persistent cognitive deficits. Further research is required to confirm this association, determine whether cognitive deficits relate to clinical features from the acute phase or to mental health status at the point of assessment, and quantify rate of recovery. Methods: 46 individuals who received critical care for COVID-19 at Addenbrooke's hospital between 10th March 2020 and 31st July 2020 (16 mechanically ventilated) underwent detailed computerised cognitive assessment alongside scales measuring anxiety, depression and post-traumatic stress disorder under supervised conditions at a mean follow up of 6.0 (± 2.1) months following acute illness. Patient and matched control (N = 460) performances were transformed into standard deviation from expected scores, accounting for age and demographic factors using N = 66,008 normative datasets. Global accuracy and response time composites were calculated (G_SScore & G_RT). Linear modelling predicted composite score deficits from acute severity, mental-health status at assessment, and time from hospital admission. The pattern of deficits across tasks was qualitatively compared with normal age-related decline, and early-stage dementia. Findings: COVID-19 survivors were less accurate (G_SScore=-0.53SDs) and slower (G_RT=+0.89SDs) in their responses than expected compared to their matched controls. Acute illness, but not chronic mental health, significantly predicted cognitive deviation from expected scores (G_SScore (p=​​0.0037) and G_RT (p = 0.0366)). The most prominent task associations with COVID-19 were for higher cognition and processing speed, which was qualitatively distinct from the profiles of normal ageing and dementia and similar in magnitude to the effects of ageing between 50 and 70 years of age. A trend towards reduced deficits with time from illness (r∼=0.15) did not reach statistical significance. Interpretation: Cognitive deficits after severe COVID-19 relate most strongly to acute illness severity, persist long into the chronic phase, and recover slowly if at all, with a characteristic profile highlighting higher cognitive functions and processing speed. Funding: This work was funded by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC), NIHR Cambridge Clinical Research Facility (BRC-1215-20014), the Addenbrooke's Charities Trust and NIHR COVID-19 BioResource RG9402. AH is funded by the UK Dementia Research Institute Care Research and Technology Centre and Imperial College London Biomedical Research Centre. ETB and DKM are supported by NIHR Senior Investigator awards. JBR is supported by the Wellcome Trust (220258) and Medical Research Council (SUAG/051 G101400). VFJN is funded by an Academy of Medical Sciences/ The Health Foundation Clinician Scientist Fellowship. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Rapid vigilance and episodic memory decrements in COVID-19 survivors.

Recent studies indicate that COVID-19 infection can lead to serious neurological consequences in a small percentage of individuals. However, in the months following acute illness, many more suffer from fatigue, low motivation, disturbed mood, poor sleep and cognitive symptoms, colloquially referred to as 'brain fog'. But what about individuals who had asymptomatic to moderate COVID-19 and reported no concerns after recovering from COVID-19? Here, we examined a wide range of cognitive functions critical for daily life (including sustained attention, memory, motor control, planning, semantic reasoning, mental rotation and spatial-visual attention) in people who had previously suffered from COVID-19 but were not significantly different from a control group on self-reported fatigue, forgetfulness, sleep abnormality, motivation, depression, anxiety and personality profile. Reassuringly, COVID-19 survivors performed well in most abilities tested, including working memory, executive function, planning and mental rotation. However, they displayed significantly worse episodic memory (up to 6 months post-infection) and greater decline in vigilance with time on task (for up to 9 months). Overall, the results show that specific chronic cognitive changes following COVID-19 are evident on objective testing even amongst those who do not report a greater symptom burden. Importantly, in the sample tested here, these were not significantly different from normal after 6-9 months, demonstrating evidence of recovery over time.

Item-level analysis of mental health symptom trajectories during the COVID-19 pandemic in the UK: Associations with age, sex and pre-existing psychiatric conditions.

BACKGROUND: There is widespread concern regarding how the COVID-19 pandemic has affected mental health. Emerging meta-analyses suggest that the impact on anxiety/depression may have been transient, but much of the included literature has major methodological limitations. Addressing this topic rigorously requires longitudinal data of sufficient scope and scale, controlling for contextual variables, with baseline data immediately pre-pandemic. AIMS: To analyse self-report of symptom frequency from two largely UK-based longitudinal cohorts: Cohort 1 (N = 10,475, two time-points: winter pre-pandemic to UK first winter resurgence), and Cohort 2 (N = 10,391, two time-points, peak first wave to UK first winter resurgence). METHOD: Multinomial logistic regression applied at the item level identified sub-populations with greater probability of change in mental health symptoms. Permutation analyses characterised changes in symptom frequency distributions. Cross group differences in symptom stability were evaluated via entropy of response transitions. RESULTS: Anxiety was the most affected aspect of mental health. The profiles of change in mood symptoms was less favourable for females and older adults. Those with pre-existing psychiatric diagnoses showed substantially higher probability of very frequent symptoms pre-pandemic and elevated risk of transitioning to the highest levels of symptoms during the pandemic. Elevated mental health symptoms were evident across intra-COVID timepoints in Cohort 2. CONCLUSIONS: These findings suggest that mental health has been negatively affected by the pandemic, including in a sustained fashion beyond the first UK lockdown into the first winter resurgence. Women, and older adults, were more affected relative to their own baselines. Those with diagnoses of psychiatric conditions were more likely to experience transition to the highest levels of symptom frequency.

Insights into the impact on daily life of the COVID-19 pandemic and effective coping strategies from free-text analysis of people's collective experiences.

There has been considerable speculation regarding how people cope during the COVID-19 pandemic; however, surveys requiring selection from prespecified answers are limited by researcher views and may overlook the most effective measures. Here, we apply an unbiased approach that learns from people's collective lived experiences through the application of natural-language processing of their free-text reports. At the peak of the first lockdown in the United Kingdom, 51 113 individuals provided free-text responses regarding self-perceived positive and negative impact of the pandemic, as well as the practical measures they had found helpful during this period. Latent Dirichlet Allocation identified, in an unconstrained data-driven manner, the most common impact and advice topics. We report that six negative topics and seven positive topics are optimal for capturing the different ways people reported being affected by the pandemic. Forty-five topics were required to optimally summarize the practical coping strategies that they recommended. General linear modelling showed that the prevalence of these topics covaried substantially with age. We propose that a wealth of coping measures may be distilled from the lived experiences of the general population. These may inform feasible individually tailored digital interventions that have relevance during and beyond the pandemic.

Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom.

The COVID-19 pandemic (including lockdown) is likely to have had profound but diverse implications for mental health and well-being, yet little is known about individual experiences of the pandemic (positive and negative) and how this relates to mental health and well-being, as well as other important contextual variables. Here, we analyse data sampled in a large-scale manner from 379,875 people in the United Kingdom (UK) during 2020 to identify population variables associated with mood and mental health during the COVID-19 pandemic, and to investigate self-perceived pandemic impact in relation to those variables. We report that while there are relatively small population-level differences in mood assessment scores pre- to peak-UK lockdown, the size of the differences is larger for people from specific groups, e.g. older adults and people with lower incomes. Multiple dimensions underlie peoples' perceptions, both positive and negative, of the pandemic's impact on daily life. These dimensions explain variance in mental health and can be statistically predicted from age, demographics, home and work circumstances, pre-existing conditions, maladaptive technology use and personality traits (e.g., compulsivity). We conclude that a holistic view, incorporating the broad range of relevant population factors, can better characterise people whose mental health is most at risk during the COVID-19 pandemic.

Vestibular agnosia in traumatic brain injury and its link to imbalance.

Vestibular dysfunction, causing dizziness and imbalance, is a common yet poorly understood feature in patients with TBI. Damage to the inner ear, nerve, brainstem, cerebellum and cerebral hemispheres may all affect vestibular functioning, hence, a multi-level assessment-from reflex to perception-is required. In a previous report, postural instability was the commonest neurological feature in ambulating acute patients with TBI. During ward assessment, we also frequently observe a loss of vertigo sensation in patients with acute TBI, common inner ear conditions and a related vigorous vestibular-ocular reflex nystagmus, suggesting a 'vestibular agnosia'. Patients with vestibular agnosia were also more unbalanced; however, the link between vestibular agnosia and imbalance was confounded by the presence of inner ear conditions. We investigated the brain mechanisms of imbalance in acute TBI, its link with vestibular agnosia, and potential clinical impact, by prospective laboratory assessment of vestibular function, from reflex to perception, in patients with preserved peripheral vestibular function. Assessment included: vestibular reflex function, vestibular perception by participants' report of their passive yaw rotations in the dark, objective balance via posturography, subjective symptoms via questionnaires, and structural neuroimaging. We prospectively screened 918 acute admissions, assessed 146 and recruited 37. Compared to 37 matched controls, patients showed elevated vestibular-perceptual thresholds (patients 12.92°/s versus 3.87°/s) but normal vestibular-ocular reflex thresholds (patients 2.52°/s versus 1.78°/s). Patients with elevated vestibular-perceptual thresholds [3 standard deviations (SD) above controls' average], were designated as having vestibular agnosia, and displayed worse posturography than non-vestibular-agnosia patients, despite no difference in vestibular symptom scores. Only in patients with impaired postural control (3 SD above controls' mean), whole brain diffusion tensor voxel-wise analysis showed elevated mean diffusivity (and trend lower fractional anisotropy) in the inferior longitudinal fasciculus in the right temporal lobe that correlated with vestibular agnosia severity. Thus, impaired balance and vestibular agnosia are co-localized to the inferior longitudinal fasciculus in the right temporal lobe. Finally, a clinical audit showed a sevenfold reduction in clinician recognition of a common peripheral vestibular condition (benign paroxysmal positional vertigo) in acute patients with clinically apparent vestibular agnosia. That vestibular agnosia patients show worse balance, but without increased dizziness symptoms, explains why clinicians may miss treatable vestibular diagnoses in these patients. In conclusion, vestibular agnosia mediates imbalance in traumatic brain injury both directly via white matter tract damage in the right temporal lobe, and indirectly via reduced clinical recognition of common, treatable vestibular diagnoses.

An automated machine learning approach to predict brain age from cortical anatomical measures.

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.

Diffusion tensor imaging of lumbar spinal nerves reveals changes in microstructural integrity following decompression surgery associated with improvements in clinical symptoms: A case report.

The outcomes from spinal nerve decompression surgery are highly variable with a sizable proportion of elderly foraminal stenosis patients not regaining good pain relief. A better understanding of nerve root compression before and following decompression surgery and whether these changes are mirrored by improvements in symptoms may help to improve clinical decision-making processes. This case study used a combination of diffusion tensor imaging (DTI), clinical questionnaires and motor neurophysiology assessments before and up to 3 months following spinal decompression surgery. In this case report, a 70-year-old women with compression of the left L5 spinal nerve root in the L5-S1 exit foramina was recruited to the study. At 3 months following surgery, DTI revealed marked improvements in left L5 microstructural integrity to a similar level to that seen in the intact right L5 nerve root. This was accompanied by a gradual improvement in pain-related symptoms, mood and disability score by 3 months. Using this novel multimodal approach, it may be possible to track concurrent improvements in pain-related symptoms, function and microstructural integrity of compressed nerves in elderly foraminal stenosis patients undergoing decompression surgery.