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The lifetime accumulation of multimorbidity and its influence on dementia risk: a UK Biobank study.

The number of people living with dementia worldwide is projected to reach 150 million by 2050, making prevention a crucial priority for health services. The co-occurrence of two or more chronic health conditions, termed multimorbidity, occurs in up to 80% of dementia patients, making multimorbidity an important risk factor for dementia. However, we lack an understanding of the specific health conditions, and their age of onset, that drive the link between multimorbidity and dementia. Using data from 282 712 participants of the UK Biobank, we defined the sequential patterns of accumulation of 46 chronic conditions over the life course. By grouping individuals based on their life history of chronic illness, we show here that the risk of incident dementia can be stratified by both the type and timing of their accumulated chronic conditions. We identified several distinct clusters of multimorbidity throughout the lifespan (cardiometabolic, mental health, neurovascular, peripheral vascular, eye diseases and low/no multimorbidity). We observed that the odds of developing dementia varied based on when these comorbidities were diagnosed. Until midlife (age 55), the accumulation of cardiometabolic conditions, such as coronary heart disease, atrial fibrillation, and diabetes, was most strongly associated with dementia risk. However, from 55 to 70 years, the accumulation of mental health conditions, such as anxiety and depression, as well as neurovascular conditions, such as stroke and transient ischaemic attack, was associated with an over 2-fold increase in dementia risk compared with low multimorbidity. Importantly, individuals who continuously and sequentially accumulate cardiometabolic, mental health, and neurovascular conditions were at greatest risk. The age-dependent role of multimorbidity in predicting dementia risk could be used for early stratification of individuals into high- and low-risk groups and could inform targeted prevention strategies based on a person's prior history of chronic disease.

Cryoneurolysis: A Novel Treatment for Management of Spasticity. Presentation of a Case Series

Background: Spasticity is a motor phenomenon occurring in disorders of the central nervous system that impacts on active and passive function, and quality of life. Pharmacological, physical and surgical management options are available, each of which have limitations. Cryoneurolysis is a technique developed for the treatment of pain which involves the controlled freezing and thawing of peripheral nerves. Recent case reports and series have suggested it may offer a novel treatment approach for pain associated with spasticity. Objectives: To report on the evaluation of cryoneurolysis in the first cohort of patients treated in a UK spasticity clinic. Methods: Eight patients with a variety of neurological conditions (aged 25-75 years) underwent cryoneurolysis. Each had been receiving regular botulinum toxin injections and had ongoing treatment goals. All patients first underwent diagnostic nerve blocks with local anaesthetic to determine their appropriateness for the treatment. Cryoneurolysis was then performed with ultrasound and nerve stimulator guidance. Assessments included goal attainment, Modified Ashworth Scale (MAS), ArmA, LegA and the patient reported impact of spasticity scale (PRISM), alongside patient satisfaction and side effect questionnaires. Assessments were at baseline and at regular intervals over 9 to 12 months. Results: All patients attained at least one of their goals, with sustained effect for more than 6 months. MAS demonstrated mixed or modest improvements. Functional outcome measures (ArmA/LegA) showed several meaningful improvements, particularly in passive function. There was an indication of an improvement in PRISM across domains, which plateaued at 6 months. Post-procedure pain was the most common side effect but subsided in all affected patients by 3 months. Patient satisfaction was positive. Conclusions: Our findings contribute to a growing base of case reports and series suggesting that cryoneurolysis could be a potentially useful treatment modality for spasticity. Future controlled studies should aim to evaluate cost-effectiveness and compare with existing treatments.

Clinical and cost-effectiveness of lithium versus quetiapine augmentation for treatment-resistant depression in adults: LQD a pragmatic randomised controlled trial.

BACKGROUND: Lithium and several atypical antipsychotics are the recommended first-line augmentation options for treatment-resistant depression; however, few studies have compared them directly, and none for longer than 8 weeks. Consequently, there is little evidence-based guidance for clinicians when choosing an augmentation option for patients with treatment-resistant depression. OBJECTIVES: This trial examined whether it is more clinically and cost-effective to prescribe lithium or quetiapine augmentation therapy for patients with treatment-resistant depression over 12 months. DESIGN: This was a parallel group, multicentre, pragmatic, open-label superiority trial comparing the clinical and cost-effectiveness of lithium versus quetiapine augmentation of antidepressant medication in treatment-resistant depression. Participants were randomised 1 : 1 at baseline to the decision to prescribe either lithium or quetiapine. SETTING: Six National Health Service trusts in England. PARTICIPANTS: Eligible participants were aged ≥ 18 years, met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for major depressive disorder, scored ≥ 14 on the 17-item Hamilton Depression Rating Scale and whose depression had had an inadequate response to at least two therapeutic antidepressant treatment trials in the current episode, with a current antidepressant treatment at or above the therapeutic dose for ≥ 6 weeks. Patients with a history of psychosis or bipolar disorder were excluded. Patients were judged suitable for either treatment. INTERVENTIONS: After randomisation, pre-prescribing safety checks were undertaken as per standard care and trial clinicians decided whether to proceed with prescribing the allocated medication. Trial clinicians received recommendations for titration and dosing in line with current clinical guidelines; however, dosing regimens could be altered according to tolerability and response. Participants were followed up using weekly self-report questionnaires and 8-, 26- and 52-week research visits. MAIN OUTCOME MEASURES: The co-primary outcome measures were depressive symptom severity over 52 weeks, measured weekly using the self-rated Quick Inventory of Depressive Symptomatology, and time to all-cause treatment discontinuation of the trial medication. Economic analyses compared costs between the two treatment arms over 52 weeks, from a National Health Service and Personal Social Services perspective, and a societal perspective. RESULTS: Two hundred and twelve participants were randomised, 107 to quetiapine and 105 to lithium. The quetiapine arm showed a significantly greater reduction in depressive symptoms than the lithium arm over 52 weeks (quetiapine vs. lithium area under the differences curve = -68.36, 95% confidence interval: -129.95 to -6.76, p = 0.0296). Median days to discontinuation did not significantly differ between the two arms (quetiapine = 365.0, interquartile range = 57.0-365.0, lithium = 212.0, interquartile range = 21.0-365.0), p = 0.1196. Quetiapine was more cost effective than lithium. Thirty-two serious adverse events were recorded, only one of which was deemed possibly related to the intervention (lithium). LIMITATIONS: The trial was unblinded, therefore expectancies regarding the trial medications may have influenced the results. Further, there was substantial missing data for some of the secondary outcome measures. CONCLUSIONS: As well as being more cost-effective, quetiapine may be a more clinically effective augmentation option for treatment-resistant depression. FUTURE WORK: Examining predictors of treatment response, including clinical, sociodemographic and biological factors, will help establish whether there are additional factors to consider when choosing an augmentation treatment for treatment-resistant depression. TRIAL REGISTRATION: This trial is registered as ISRCTN16387615. FUNDING: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 14/222/02) and is published in full in Health Technology Assessment; Vol. 29, No. 12. See the NIHR Funding and Awards website for further award information.

Negative bias in encoding and recall memory in depressed patients with inadequate response to antidepressant medication.

RATIONALE: Cognitive theories propose that negative biases in emotional processing contribute to the maintenance of depressive states. Previous studies reported that acute antidepressant treatment in depressed patients reversed negative emotional biases. However, studies addressing the differences in emotional processing between healthy volunteers and clinically depressed patients with inadequate response to standard antidepressant treatments are limited. OBJECTIVES: To investigate the differences in emotional processing domains between depressed patients with inadequate response to current antidepressant treatment and healthy controls. METHODS: Fifty-four medicated patients with major depression and 45 age- and sex-equated healthy volunteers were tested using the Oxford Emotional Testing Battery. RESULTS: There was no difference between the two groups in the accuracy of recognising emotional facial expressions. However, there was a significant difference in the pattern of response times in an emotional categorisation task (F1,97 = 6.44, p = 0.013, partial η2 = 0.017) where healthy controls had faster responses towards positive than negative self-referent words (95%CI: -0.291 - -0.054, p = 0.005). In contrast, patients had no significant differences in reaction time for categorizing positive and negative self-referent descriptors. There was also a significant group interaction in an emotional memory task (F1,91 = 7.90, p = 0.006, partial η2 = 0.080) where healthy volunteers recalled significantly more positively valenced words than depressed patients (95%CI: -2.104 - -0.168, p = 0.022). CONCLUSIONS: Depressed patients with inadequate responses toward antidepressants had negative biases in emotional categorisation and emotional memory. These psychological abnormalities may represent targets for treatment in patients with difficult-to-treat depression.

Using arterial spin labelling to investigate spontaneous and evoked ongoing musculoskeletal pain

Clinical pain is difficult to study using standard Blood Oxy-genation Level Dependent (BOLD) magnetic resonance imaging because it is often ongoing and, if evoked, it is associated with stimulus-correlated motion. Arterial spin labelling (ASL) offers an attractive alternative. This study used arm repositioning to evoke clinically-relevant musculoskeletal pain in patients with shoulder impingement syndrome. Fifty-five patients were scanned using a multi post-labelling delay pseudo-continuous ASL (pCASL) sequence, first with both arms along the body and then with the affected arm raised into a painful position. Twenty healthy volunteers were scanned as a control group. Arm repositioning resulted in increased perfusion in brain regions involved in sensory processing and movement integration, such as the contralateral primary motor and primary somatosensory cortex, mid- and posterior cingulate cortex, and, bilaterally, in the insular cortex/operculum, putamen, thalamus, midbrain and cerebellum. Perfusion in the thalamus, midbrain and cerebellum was larger in the patient group. Results of a post hoc analysis suggested that the observed perfusion changes were related to pain rather than arm repositioning. This study showed that ASL can be useful in research on clinical ongoing musculoskeletal pain but the technique is not sensitive enough to detect small differences in perfusion.

Brain and muscle chemistry in myalgic encephalitis/chronic fatigue syndrome (ME/CFS) and long COVID: a 7T magnetic resonance spectroscopy study.

Myalgic encephalitis/chronic fatigue syndrome (ME/CFS) is a common debilitating medical condition, whose main symptoms - fatigue, post-exertional malaise and cognitive dysfunction - are also present in many cases of long COVID. Magnetic resonance spectroscopy (MRS) allows the insight into their pathophysiology through exploration of a range of biochemicals putatively relevant to aetiological processes, in particular mitochondrial dysfunction and energy metabolism. 24 patients with ME/CFS, 25 patients with long COVID and 24 healthy controls (HC) underwent brain (pregenual and dorsal anterior cingulate cortex, respectively, pgACC and dACC) and calf muscle MRS scanning at 7 Tesla, followed by a computerised cognitive assessment. Compared to HC, ME/CFS patients had elevated levels of lactate in both pgACC and dACC, while long COVID patients had lowered levels of total choline in dACC. By contrast, skeletal muscle metabolites at rest did not significantly differ between the groups. The changes in lactate in ME/CFS are consistent with the presence of energetic stress and mitochondrial dysfunction. A reduction in total choline in long COVID is of interest in the context of the recently reported association between blood clots and 'brain fog', and earlier animal studies showing that choline might prevent intravascular coagulation. Importantly, differences in findings between ME/CFS and long COVID suggest that the underlying neurobiological mechanisms, while leading to similar clinical presentations, may differ. An important implication is that patients with ME/CFS and those with fatigue in the course of long COVID should not be studied as a single group, at least until the mechanisms are better understood.

Evaluating functional brain organization in individuals and identifying contributions to network overlap.

Individual differences in the spatial organization of resting-state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting-state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting-state networks can be derived using high-quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that overlap between 2-network pairs is indicative of coupling. These results suggest that regions of network overlap concurrently process information from both contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Longer scans boost prediction and cut costs in brain-wide association studies.

A pervasive dilemma in brain-wide association studies1 (BWAS) is whether to prioritize functional magnetic resonance imaging (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning diverse scanners, acquisitions, racial groups, disorders and ages. For scans of ≤20 min, accuracy increases linearly with the logarithm of the total scan duration, suggesting that sample size and scan time are initially interchangeable. However, sample size is ultimately more important. Nevertheless, when accounting for the overhead costs of each participant (such as recruitment), longer scans can be substantially cheaper than larger sample size for improving prediction performance. To achieve high prediction performance, 10 min scans are cost inefficient. In most scenarios, the optimal scan time is at least 20 min. On average, 30 min scans are the most cost-effective, yielding 22% savings over 10 min scans. Overshooting the optimal scan time is cheaper than undershooting it, so we recommend a scan time of at least 30 min. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-to-whole-brain BWAS. In contrast to standard power calculations, our results suggest that jointly optimizing sample size and scan time can boost prediction accuracy while cutting costs. Our empirical reference is available online for future study design ( https://thomasyeolab.github.io/OptimalScanTimeCalculator/index.html ).

An fMRI study of initiation and inhibition of manual and spoken responses in people who stutter

Abstract Stuttering is characterised by difficulties initiating speech and frequent interruptions to the flow of speech. Neuroimaging studies of speech production in people who stutter consistently reveal greater activity of the right inferior frontal cortex, an area robustly implicated in stopping manual and spoken responses. This has been linked to an “overactive response suppression mechanism” in people who stutter. Here, we used fMRI to investigate neural differences related to response initiation and inhibition in people who stutter and matched controls (aged 19-45) during performance of the stop-signal task in both the manual and speech domains. We hypothesised there would be increased activity in an inhibitory network centred on right inferior frontal cortex. Out of scanner behavioural testing revealed that people who stutter were slower than controls to respond to ‘go’ stimuli in both the manual and the speech domains, but the groups did not differ in their stop-signal reaction times in either domain. During the fMRI task, both groups activated the expected networks for the manual and speech tasks. Contrary to our hypothesis, we did not observe differences in task-evoked activity between people who stutter and controls during either ‘go’ or ‘stop’ trials. Targeted region-of-interest analyses in the inferior frontal cortex, the supplementary motor area and the putamen bilaterally confirmed that there were no group differences in activity. These results focus on tasks involving button presses and production of single nonwords, and therefore do not preclude inhibitory involvement related specifically to stuttering events. Our findings indicate that people who stutter do not show behavioural or neural differences in response inhibition, when making simple manual responses and producing fluent speech, contrary to predictions from the global inhibition hypothesis.

Corrigendum to “Relating TMS measures of GABAergic and Cholinergic signalling to attention” [Brain Stimul 18 (1) (2025) 507–508, (S1935861X24010489), (10.1016/j.brs.2024.12.853)]

The authors regret that some of the authors are omitted in the original publication. The correct list of authors is as presented above. The authors also regret the errors in the abstract text. The corresponding corrections are provided below: The first line of paragraph 3 of the abstract should read: Here we investigated the role of GABA and ACh in healthy vision (n = 35). The last two paragraphs of the abstract should read as follows: We found that higher GABAergic Cholinergic inhibition in the motor cortex relates to better orienting attention allocation, as indicated by a significant correlation between the alerting orienting index of the ANT and SICI-1msSAI (r = −0.5942, p = 0.004). Despite the proposed role of Cholinergic signalling024). Our results are in line with evidence suggesting cholinergic mechanisms are responsible for successful orienting of attention, we did not find a significant correlation between SAI and any of the attentional indices (alerting, orienting, executive) of the ANT. Our findings suggest that GABAergic Cholinergic inhibition plays an important role in success fulorienting attention allocation and have guided the design of our ongoing pharmaco-TMS study investigating the effects of Zolpidem (GABA agonist) and Donepezil (cholinesterase antagonist) on behavioural and neurophysiological indices of attention. The authors would like to apologise for any inconvenience caused.

An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank.

Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional organizations. The difference between functional organizations at rest and during task was examined, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate the exploration of brain function-trait association results (http://fmriatlas.org/).

Neurodegenerative disease in C9orf72 repeat expansion carriers: population risk and effect of UNC13A.

The C9orf72 hexanucleotide repeat expansion (HRE) is the most common monogenetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Neurodegenerative disease incidence in C9orf72 HRE carriers has been studied using cohorts from disease-affected families or by extrapolating from population disease incidence, potentially introducing bias. Age-specific cumulative incidence of ALS and dementia was estimated using Kaplan-Meier and competing risk models in C9orf72 HRE carriers compared to matched controls in UK Biobank. Risk modification by UNC13A genotype was examined. Of 490,331 individuals with valid genetic data, 701 had >100 repeats in C9orf72 (median age 55 [IQR 48-62], follow-up 13.4 years [12.3-14.1]). The cumulative incidence of ALS or dementia was 66% [95% CI 57-73%] by age 80 in C9orf72 HRE carriers versus 5.8% [4.5-7.0%] in controls, or 58% [50-64%] versus 5.1% [4.1-6.4%] accounting for the competing risk of other-cause mortality. Forty-one percent of dementia incidence accrued between age 75-80. C-allele homozygosity at rs12608932 in UNC13A increased ALS or dementia risk in C9orf72 HRE carriers (hazard ratio 1.81 [1.18 - 2.78]). C9orf72 HRE disease was incompletely penetrant in this population-based cohort, with risk modified by UNC13A genotype. This has implications for counselling at-risk individuals and modelling expected phenoconversion for prevention trials.

Human motor cortical gamma activity relates to GABAergic intracortical inhibition and motor learning

Gamma activity (γ, >30 Hz) is universally demonstrated across brain regions and species. However, the physiological basis and functional role of γ sub-bands (slow-γ, mid-γ, fast-γ) have been predominantly studied in rodent hippocampus; γ activity in the human neocortex is much less well understood. We use electrophysiology, non-invasive brain stimulation, and several motor tasks to examine the properties of sensorimotor γ activity sub-bands and their relationship with both local GABAergic activity and motor learning. Data from three experimental studies are presented. Experiment 1 (N = 33) comprises magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), and a motor learning paradigm; experiment 2 (N = 19) uses MEG and motor learning; and experiment 3 (N = 18) uses EEG and TMS. We characterised two distinct γ sub-bands (slow-γ, mid-γ) which show a movement-related increase in activity during unilateral index finger movements and are characterised by distinct temporal–spectral–spatial profiles. Bayesian correlation analysis revealed strong evidence for a positive relationship between slow-γ (~30–60 Hz) peak frequency and GABAergic intracortical inhibition (as assessed using the TMS-metric short interval intracortical inhibition). There was also moderate evidence for a relationship between the power of the movement-related mid-γ activity (60–90 Hz) and motor learning. These relationships were neurochemical and frequency specific. These data provide new insights into the neurophysiological basis and functional roles of γ activity in human M1 and allow the development of a new theoretical framework for γ activity in the human neocortex.

The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.

Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier

T1-weighted (T1w) MRI is widely used in clinical neuroimaging for studying brain structure and its changes, including those related to neurodegenerative diseases, and as anatomical reference for analysing other modalities. Ensuring high-quality T1w scans is vital as image quality affects reliability of outcome measures. However, visual inspection can be subjective and time consuming, especially with large datasets. The effectiveness of automated quality control (QC) tools for clinical cohorts remains uncertain. In this study, we used T1w scans from elderly participants within ageing and clinical populations to test the accuracy of existing QC tools with respect to visual QC and to establish a new quality prediction framework for clinical research use. Four datasets acquired from multiple scanners and sites were used (N = 2438, 11 sites, 39 scanner manufacturer models, 3 field strengths—1.5T, 3T, 2.9T, patients and controls, average age 71 ± 8 years). All structural T1w scans were processed with two standard automated QC pipelines (MRIQC and CAT12). The agreement of the accept–reject ratings was compared between the automated pipelines and with visual QC. We then designed a quality prediction framework that combines the QC measures from the existing automated tools and is trained on clinical research datasets. We tested the classifier performance using cross-validation on data from all sites together, also examining the performance across diagnostic groups. We then tested the generalisability of our approach when leaving one site out and explored how well our approach generalises to data from a different scanner manufacturer and/or field strength from those used for training, as well as on an unseen new dataset of healthy young participants with movement-related artefacts. Our results show significant agreement between automated QC tools and visual QC (Kappa = 0.30 with MRIQC predictions; Kappa = 0.28 with CAT12’s rating) when considering the entire dataset, but the agreement was highly variable across datasets. Our proposed robust undersampling boost (RUS) classifier achieved 87.7% balanced accuracy on the test data combined from different sites (with 86.6% and 88.3% balanced accuracy on scans from patients and controls, respectively). This classifier was also found to be generalisable on different combinations of training and test datasets (average balanced accuracy of leave-one-site-out = 78.2%; exploratory models on field strengths and manufacturers = 77.7%; movement-related artefact dataset when including 1% scans in the training = 88.5%). While existing QC tools may not be robustly applicable to datasets comprising older adults, they produce quality metrics that can be leveraged to train more robust quality control classifiers for ageing and clinical cohorts.

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.

Differences in brain activity during sentence repetition in people who stutter: a combined analysis of four fMRI studies

Dementia fluid biomarkers are enriched in human cervical lymph node fine needle aspirates and their level is selectively age-dependent

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