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Linking microscopy to diffusion MRI with degenerate biophysical models: an application of the Bayesian EstimatioN of CHange (BENCH) framework
Abstract Biophysical modelling of diffusion MRI (dMRI) is used to non-invasively estimate microstructural features of tissue, particularly in the brain. However, meaningful description of tissue requires many unknown parameters, resulting in a model that is often ill-posed. The Bayesian EstimatioN of CHange (BENCH) framework was specifically designed to circumvent parameter fitting for ill-conditioned models when one is simply interested in interpreting signal changes related to some variable of interest. To understand the biological underpinning of some observed change in MR signal between different conditions, BENCH predicts which model parameter, or combination of parameters, best explains the observed change, without having to invert the model. BENCH has been previously used to identify which biophysical parameters could explain group-wise dMRI signal differences (e.g. patients vs. controls); here, we adapt BENCH to interpret dMRI signal changes related to continuous variables. We investigate how parameters from the dMRI standard model of white matter, with an additional sphere compartment to represent glial cell bodies, relate to tissue microstructure quantified from histology. We validate BENCH using synthetic dMRI data from numerical simulations. We then apply it to ex-vivo macaque brain data with dMRI and microscopy metrics of glial density, axonal density, and axonal dispersion in the same brain. We found that (i) increases in myelin density are primarily associated with an increased intra-axonal volume fraction and (ii) changes in the orientation dispersion derived from myelin microscopy are linked to variations in the orientation dispersion index. Finally, we found that the dMRI signal is sensitive to changes in glial cell load in the brain white matter, though no single parameter in the extended standard model was able to explain this observed signal change.
A Validated Model to Predict Severe Weight Loss in Amyotrophic Lateral Sclerosis
ABSTRACTSevere weight loss in amyotrophic lateral sclerosis (ALS) is common, multifactorial, and associated with shortened survival. Using longitudinal weight data from over 6000 patients with ALS across three cohorts, we built an accelerated failure time model to predict the risk of future severe (≥ 10%) weight loss using five single‐timepoint clinical predictors: symptom duration, revised ALS Functional Rating Scale, site of onset, forced vital capacity, and age. Model performance and generalisability were evaluated using internal–external cross‐validation and random‐effects meta‐analysis. The overall concordance statistic was 0.71 (95% CI 0.63–0.79), and the calibration slope and intercept were 0.91 (0.69–1.13) and 0.05 (−0.11–0.21). This study highlights clinical factors most associated with severe weight loss in ALS and provides the basis for a stratification tool.
Lower risk of dementia with AS01-adjuvanted vaccination against shingles and respiratory syncytial virus infections.
AS01-adjuvanted shingles (herpes zoster) vaccination is associated with a lower risk of dementia, but the underlying mechanisms are unclear. In propensity-score matched cohort studies with 436,788 individuals, both the AS01-adjuvanted shingles and respiratory syncytial virus (RSV) vaccines, individually or combined, were associated with reduced 18-month risk of dementia. No difference was observed between the two AS01-adjuvanted vaccines, suggesting that the AS01 adjuvant itself plays a direct role in lowering dementia risk.
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.
Designing and Comparing Optimised Pseudo-Continuous Arterial Spin Labelling Protocols for Measurement of Cerebral Blood Flow
1.AbstractArterial Spin Labelling (ASL) is a non-invasive, non-contrast, perfusion imaging technique which is inherently SNR limited. It is, therefore, important to carefully design scan protocols to ensure accurate measurements. Many pseudo-continuous ASL (PCASL) protocol designs have been proposed for measuring cerebral blood flow (CBF), but it has not yet been demonstrated which design offers the most accurate and repeatable CBF measurements. In this work, a wide range of literature PCASL protocols, including single-delay, sequential and time-encoded multi-timepoint protocols, and several novel protocol designs, which are hybrids of time-encoded and sequential multi-timepoint protocols, were first optimised using a Cramér-Rao Lower Bound framework and then compared for CBF accuracy and repeatability using Monte Carlo simulations and in vivo experiments. It was found that several multi-timepoint protocols produced more confident, accurate, and repeatable CBF estimates than the single-delay protocol, while also generating maps of arterial transit time. One of the novel hybrid protocols, HybridT1-adj, was found to produce the most confident, accurate and repeatable CBF estimates of all protocols tested in both simulations and in vivo (24%, 47%, and 28% more confident, accurate, and repeatable than single-PLD in vivo). The HybridT1-adjprotocol makes use of the best aspects of both time-encoded and sequential multi-timepoint protocols and should be a useful tool for accurately and efficiently measuring CBF.
Statistical inference for same data meta-analysis in neuroimaging multiverse analyzes
Researchers using task-functional magnetic resonance imaging (fMRI) data have access to a wide range of analysis tools to model brain activity. If not accounted for properly, this plethora of analytical approaches can lead to an inflated rate of false positives and contribute to the irreproducibility of neuroimaging findings. Multiverse analyses are a way to systematically explore pipeline variations on a given dataset. We focus on the setting where multiple statistic maps are produced as an output of a set of analyses originating from a single dataset. However, having multiple outputs for the same research question—corresponding to different analytical approaches—makes it especially challenging to draw conclusions and interpret the findings. Meta-analysis is a natural approach to extract consensus inferences from these maps, yet the traditional assumption of independence among input datasets does not hold here. In this work, we consider a suite of methods to conduct meta-analysis in the multiverse setting, which we call same data meta-analysis (SDMA), accounting for inter-pipeline dependence among the results. First, we assessed the validity of these methods in simulations. Then, we tested them on the multiverse outputs of two real-world multiverse analyses: “NARPS”, a multiverse study originating from the same dataset analyzed by 70 different teams, and “HCP Young Adult”, a more homogeneous multiverse analysis using 24 different pipelines analyzed by the same team. Our findings demonstrate the validity of our proposed SDMA models under inter-pipeline dependence, and provide an array of options, with different levels of relevance, for the analysis of multiverse outputs.
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).
Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults
The functional connectome changes with aging. We systematically evaluated aging-related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state functional magnetic resonance imaging (fMRI) data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both with permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p < 0.001, 95% CI [7.6% 36.0%]) and 11.5% (p < 0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p < 0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
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
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.
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.