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Cohort profile: characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19 - providing the evidence base for health care services (CONVALESCENCE) in the UK.
PURPOSE: The pathogenesis of the long-lasting symptoms which can follow an infection with the SARS-CoV-2 virus ('long covid') is not fully understood. The 'COroNaVirus post-Acute Long-term EffectS: Constructing an evidENCE base' (CONVALESCENCE) study was established as part of the Longitudinal Health and Wellbeing COVID-19 UK National Core Study. We performed a deep phenotyping case-control study nested within two cohorts (the Avon Longitudinal Study of Parents and Children and TwinsUK) as part of CONVALESCENCE. PARTICIPANTS: From September 2021 to May 2023, 349 participants attended the CONVALESCENCE deep phenotyping clinic at University College London. Four categories of participants were recruited: cases of long covid (long covid(+)/SARS-CoV-2(+)), alongside three control groups: those with neither long covid symptoms nor evidence of prior COVID-19 (long covid(-)/SARS-CoV-2(-); control group 1), those who self-reported COVID-19 and had evidence of SARS-CoV-2 infection, but did not report long covid (long covid(-)/SARS-CoV-2(+); control group 2) and those who self-reported persistent symptoms attributable to COVID-19 but no evidence of SARS-CoV-2 infection (long covid(+)/SARS-CoV-2(-); control group 3). Remote wearable measurements were performed up until February 2024. FINDINGS TO DATE: This cohort profile describes the baseline characteristics of the CONVALESCENCE cohort. Of the 349 participants, 141 (53±15 years old; 21 (15%) men) were cases, 89 (55±16 years old; 11 (12%) men) were in control group 1, 75 (49±15 years old; 25 (33%) men) were in control group 2 and 44 (55±16 years old; 9 (21%) men) were in control group 3. FUTURE PLANS: The study aims to use a multiorgan score calculated as the cumulative total for each of nine domains (ie, lung, vascular, heart, kidney, brain, autonomic function, muscle strength, exercise capacity and physical performance). The availability of data preceding acute COVID-19 infection in cohorts may help identify the consequences of infection independent of pre-existing subclinical disease and also provide evidence of determinants that influence the development of long covid.
The developing Human Connectome Project fetal functional MRI release: Methods and data structures
Abstract Recent advances in fetal fMRI present a new opportunity for neuroscience to study functional human brain connectivity at the time of its emergence. Progress in the field, however, has been hampered by the lack of openly available datasets that can be exploited by researchers across disciplines to develop methods that would address the unique challenges associated with imaging and analysing functional brain in utero, such as unconstrained head motion, dynamically evolving geometric distortions, or inherently low signal-to-noise ratio. Here we describe the developing Human Connectome Project’s release of the largest open access fetal fMRI dataset to date, containing 275 scans from 255 foetuses and spanning the period of 20.86 to 38.29 post-menstrual weeks. We present a systematic approach to its pre-processing, implementing multi-band soft SENSE reconstruction, dynamic distortion corrections via phase unwrapping method, slice-to-volume reconstruction and a tailored temporal filtering model, with attention to the prominent sources of structured noise in the in utero fMRI. The dataset is accompanied with an advanced registration infrastructure, enabling group-level data fusion, and contains outputs from the main intermediate processing steps. This allows for various levels of data exploration by the imaging and neuroscientific community, starting from the development of robust pipelines for anatomical and temporal corrections to methods for elucidating the development of functional connectivity in utero. By providing a high-quality template for further method development and benchmarking, the release of the dataset will help to advance fetal fMRI to its deserved and timely place at the forefront of the efforts to build a life-long connectome of the human brain.
Statistical Analysis of fMRI Data
fMRI is a powerful tool used in the study of brain function. It can noninvasively detect signal changes in areas of the brain where neuronal activity is varying. This chapter is a comprehensive description of the various steps in the statistical analysis of fMRI data. This will cover topics such as the general linear model (including orthogonality, hemodynamic variability, noise modeling, and the use of contrasts), multi-subject statistics, and statistical thresholding (including random field theory and permutation methods, as well as a discussion of some recent controversies about correction for multiple comparisons of statistical models).
Individualised prediction of longitudinal change in multimodal brain imaging.
It remains largely unknown whether individualised longitudinal changes of brain imaging features can be predicted based only on the baseline brain images. This would be of great value, for example, for longitudinal data imputation, longitudinal brain-behaviour associations, and early prediction of brain-related diseases. We explore this possibility using longitudinal data of multiple modalities from UK Biobank brain imaging, with around 3,500 subjects. As baseline and follow-up images are generally similar in the case of short follow-up time intervals (e.g., 2 years), a simple copy of the baseline image may have a very good prediction performance. Therefore, for the first time, we propose a new mathematical framework for guiding the longitudinal prediction of brain images, providing answers to fundamental questions: (1) what is a suitable definition of longitudinal change; (2) how to detect the existence of changes; (3) what is the "null" prediction performance; and (4) can we distinguish longitudinal change prediction from simple data denoising. Building on these, we designed a deep U-Net based model for predicting longitudinal changes in multimodal brain images. Our results show that the proposed model can predict to a modest degree individualised longitudinal changes in almost all modalities, and outperforms other potential models. Furthermore, compared with the true longitudinal changes computed from real data, the predicted longitudinal changes have a similar or even improved accuracy in predicting subjects' non-imaging phenotypes, and have a high between-subject discriminability. Our study contributes a new theoretical framework for longitudinal brain imaging studies, and our results show the potential for longitudinal data imputation, along with highlighting several caveats when performing longitudinal data analysis.
MEAanalysis: An Open-Source R package for Downstream Visualisation of AxIS Navigator Multi-Electrode Array Burst Data at the Single-Electrode Level
Abstract Summary Multi-electrode array (MEA) generate electrophysiological data that can be used to functionally characterise excitable cells. MEA data can be complex to analyse in a reproducible manner, with current data analysis tools often calculating parameters at the whole-well level. Here we present MEAanalysis, an open-source R package (GPL ( > = 2)) able to visualise burst parameters at the single electrode level downstream of AxIS Navigator software (Axion BioSystems) processing, thus increasing our understanding of an excitable cell network’s spatiotemporal variability. Availability and Implementation The package is hosted on and can be installed from the following GitHub repository: https://github.com/egordon2/MEA-analysis-package. User feedback provided via email or the GitHub issues tab will inform cycles of development. Supplementary Information A website containing example workflows and instructions on how a user may apply MEAanalysis to read and analyse other datasets is available at: https://egordon2.github.io/MEA-analysis-package/.
Volatility-driven learning in human infants.
Adapting to change is a fundamental feature of human learning, yet its developmental origins remain elusive. We developed an experimental and computational approach to track infants' adaptive learning processes via pupil size, an indicator of tonic and phasic noradrenergic activity. We found that 8-month-old infants' tonic pupil size mirrored trial-by-trial fluctuations in environmental volatility, while phasic pupil responses revealed that infants used this information to dynamically optimize their learning. This adaptive strategy resulted in successful task performance, as evidenced by anticipatory looking toward correct target locations. The ability to estimate volatility varied significantly across infants, and these individual differences were related to infant temperament, indicating early links between cognitive adaptation and emotional responsivity. These findings demonstrate that infants actively adapt to environmental change, and that early differences in this capacity may have profound implications for long-term cognitive and psychosocial development.
Pathway to Regulatory Approval of Digital Health Technologies in Progressive Supranuclear Palsy: A Scoping Review.
Background/Objectives: Progressive supranuclear palsy (PSP) is an atypical Parkinsonian disorder characterized by Parkinsonism with gait imbalance, vertical gaze palsy, and frontal cognitive dysfunction. Though digital health technologies (DHTs) are widely used both clinically and in research as outcome measures, there is a lack of consistency applied to these devices and their resulting metrics. This scoping review aims to identify efforts taken to validate wearable DHTs for use in PSP, identify gaps in research, and discuss the steps needed to expand their use and acceptance as primary trial endpoints. Methods: In this scoping review, we conducted a search of the MEDLINE database to examine the use of DHTs as outcome measures in Progressive Supranuclear Palsy. Results: A total of 17 publications were identified and reviewed. Included articles evaluated the use of DHT to measure lower extremity function/gait, balance, upper extremity function, and speech. Conclusions: Our scoping review highlights the importance of standardization of DHT metrics by thorough assessment of their content validity, reliability, construct validity, responsiveness, and discriminant validity. Efforts must be taken to ensure DHTs capture clinically relevant, patient-centered outcome measures that are comparable to conventional rating scales, that consistently discriminate disease progression. Incorporation of DHTs as clinical trial endpoints has the potential to encourage clinical research and to advance patient care.
Achieving robust labeling above the circle of Willis with vessel‐encoded arterial spin labeling
AbstractPurposeTo improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain.MethodsOur proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR‐efficient encodings than previous approaches. Pseudo‐continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients.ResultsIn simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients.ConclusionsCombining IOES with optimized PCASL parameters, the vessel‐decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation.
Case Report: Pediatric age onset CNTN1 antibody-associated neuropathy with nephropathy and literature review
We present a 12-year-old boy with acute onset sensorimotor neuropathy and membranous glomerulonephritis associated with contactin-1 antibodies. This prompted us to explore the clinical characteristics of this condition and assess whether its presentation differs between pediatric and adult patients. A comprehensive search was conducted across multiple online databases, including PubMed and EMBASE, using MeSH terms such as “chronic inflammatory demyelinating polyradiculopathy”, “acute inflammatory demyelinating polyradiculopathy “, “CIDP”, “Guillain Barre syndrome”, “proteinuria”, “nephrotic syndrome”, “nephropathy”, “renal disease”, “glomerulonephritis”, “membranous nephropathy”, “autoimmune nodopathies”, and “membranous glomerulonephritis”. We reviewed publications up to October 2024 and identified 39 patients with anti-contactin associated CIDP (chronic inflammatory demyelinating polyradiculopathy) with membranous glomerulonephritis (MGN), including our case. This rare coexistence typically occurs at advanced ages, with only two pediatric cases. Clinical features were similar regardless of age at onset. We compared the onset, symptoms, progression, renal histopathology, and treatment responses between pediatric and adult patients.