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A bstract The Sp(4) gauge theory with two Dirac fundamental flavours provides a candidate for the microscopic origin of composite-Higgs models based on the SU(4)/Sp(4) coset. We employ a combination of two different, complementary strategies for the numerical lattice calculations, based on the Hybrid Monte Carlo and on the Heat Bath algorithms. We perform pure Yang-Mills, quenched computations and exploratory studies with dynamical Wilson fermions. We present the first results in the literature for the spectrum of glueballs of the pure Sp(4) Yang-Mills theory, an EFT framework for the interpretation of the masses and decay constants of the lightest pion, vector and axial-vector mesons, and a preliminary calculation of the latter in the quenched approximation. We show the first numerical evidence of a bulk phase transition in the lattice theory with dynamical Wilson fermions, and perform the technical steps necessary to set up future investigations of the mesonic spectrum of the full theory.

•Developed a functional brain atlas from ALE meta-analysis of upper limb motor tasks.•Quantified lesion load using structural and functional brain atlases in stroke patients.•ROI-based lesion load explained 6% additional variance beyond baseline FMUE.•Total lesion load added minimal predictive value over baseline FMUE alone.•SSCA and SMAA offered compact, movement-specific lesion quantification tools. Stroke is a leading cause of death and disability, with motor deficits contributing significantly to post-stroke disability. Brain atlases hold promise for predicting motor outcomes post-stroke, but existing tools often lack comprehensive coverage of motor-related brain regions and do not integrate both structural and functional measures. This retrospective longitudinal study aimed to develop and evaluate neuroimaging biomarkers for predicting post-stroke motor outcomes by constructing comprehensive sensorimotor brain atlases. We developed two novel atlases: a sensorimotor structural connectivity atlas (SSCA), integrating three existing tractography-based atlases, and a probabilistic sensorimotor activation-based atlas (SMAA), derived from an ALE meta-analysis of 3,252 activation foci related to motor execution and learning. We assessed their predictive value by analysing the relationship between baseline lesion load and Action Research Arm Test scores at 12 weeks post-ischemic stroke in 142 patients, using multivariable linear regression models. Lesion loads from five published atlases were also quantified for comparison. While the SSCA demonstrated moderate predictive performance, it was outperformed by the Sensorimotor Area Tract Template, indicating that broader tract coverage did not improve prediction. Despite comprising only 12.8% of the Brainnetome atlas volume, the SMAA achieved comparable performance with reduced model complexity. Overall, these atlas-based lesion load metrics correlated with upper limb motor outcomes but provided only limited additional predictive value beyond baseline Fugl-Meyer Assessment for Upper Extremity scores, highlighting the need for refinement and future multimodal approaches. [Display omitted]

We review the current status of the long-term programme of numerical investigation of Sp(2N) gauge theories with and without fermionic matter content. We start by introducing the phenomenological as well as theoretical motivations for this research programme, which are related to composite Higgs models, models of partial top compositeness, dark matter models, and in general to the physics of strongly coupled theories and their approach to the large-N limit. We summarise the results of lattice studies conducted so far in the Sp(2N) Yang–Mills theories, measuring the string tension, the mass spectrum of glueballs and the topological susceptibility, and discuss their large-N extrapolation. We then focus our discussion on Sp(4), and summarise the numerical measurements of mass and decay constant of mesons in the theories with fermion matter in either the fundamental or the antisymmetric representation, first in the quenched approximation, and then with dynamical fermions. We finally discuss the case of dynamical fermions in mixed representations, and exotic composite fermion states such as the chimera baryons. We conclude by sketching the future stages of the programme. We also describe our approach to open access.

IntroductionStroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.MethodsWe designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.ResultsRecovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).ConclusionsOur work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

Focal brain lesions cause neurophysiological changes in local and distributed neural systems. While electroencephalography (EEG) has a long history in post-stroke neurophysiological assessment, the observed changes have rarely been linked to specific lesion locations, leaving neuroanatomical-neurophysiological relationships after stroke unclear. Current data-driven methods, such as voxel-based lesion symptom mapping (VLSM), relate lesion locations to single-feature “symptoms” but currently cannot associate anatomical injury with multidimensional data such as EEG, with its rich spatiotemporal information. To overcome this limitation, we introduce MD-VLM, an extension of VLSM to multidimensional “symptoms” that identifies relationships between lesion locations and neurophysiology. MD-VLM is data-agnostic, compatible with various lesion (e.g., lesion maps, lesion network maps) and neurophysiological (e.g., channel-level or source-localized EEG) inputs, and uses robust statistics to test for the existence of significant neuroanatomical-neurophysiological relationships. We demonstrate MD-VLM’s feasibility by applying it to EEG from chronic stroke patients performing a cued-movement task. MD-VLM revealed significant associations between frontal white-matter lesions and reduced ipsilesional parietal cue-evoked responses, consistent with damage to known fronto-parietal networks. MD-VLM is a novel data-driven extension to VLSM for multidimensional “symptoms”. Applying MD-VLM to link lesions to neurophysiological data can improve mechanistic understanding of post-stroke neurological impairments and guide future biomarker development.

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling— collecting, analyzing, and modeling neurorehabilitation data— holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation— (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

The objective of this study was to identify key features differentiating multiple system atrophy cerebellar type (MSA-C) from idiopathic late-onset cerebellar ataxia (ILOCA). We reviewed records of patients seen in the Massachusetts General Hospital Ataxia Unit between 1992 and 2013 with consensus criteria diagnoses of MSA-C or ILOCA. Twelve patients had definite MSA-C, 53 had possible/probable MSA-C, and 12 had ILOCA. Autonomic features, specifically urinary urgency, frequency, and incontinence with erectile dysfunction in males, differentiated MSA-C from ILOCA throughout the disease course ( p  = 0.005). Orthostatic hypotension developed later and differentiated MSA-C from ILOCA ( p  < 0.01). REM sleep behavior disorder (RBD) occurred early in possible/probable MSA-C ( p  < 0.01). Late MSA-C included pathologic laughing and crying (PLC, p  < 0.01), bradykinesia ( p  = 0.01), and corticospinal findings ( p  = 0.01). MRI distinguished MSA-C from ILOCA by atrophy of the brainstem ( p  < 0.01) and middle cerebellar peduncles (MCP, p  = 0.02). MSA-C progressed faster than ILOCA: by 6 years, MSA-C walker dependency was 100 % and ILOCA 33 %. MSA-C survival was 8.4 ± 2.5 years. Mean length of ILOCA illness to date is 15.9 ± 6.4 years. A sporadic onset, insidiously developing cerebellar syndrome in midlife, with autonomic features of otherwise unexplained bladder dysfunction with or without erectile dysfunction in males, and atrophy of the cerebellum, brainstem, and MCP points strongly to MSA-C. RBD and postural hypotension confirm the diagnosis. Extrapyramidal findings, corticospinal tract signs, and PLC are helpful but not necessary for diagnosis. Clarity in early MSA-C diagnosis can prevent unnecessary investigations and facilitate therapeutic trials.

A bstract We analyse finite-size scaling behaviour of a four-dimensional Higgs-Yukawa model near the Gaussian infrared fixed point. Through improving the mean-field scaling laws by solving one-loop renormalisation group equations, the triviality property of this model can be manifested in the volume-dependence of moments of the scalar-field zero mode. The scaling formulae for the moments are derived in this work with the inclusion of the leading-logarithmic corrections. To test these formulae, we confront them with data from lattice simulations in a simpler model, namely the O(4) pure scalar theory, and find numerical evidence of good agreement. Our results of the finite-size scaling can in principle be employed to establish triviality of Higgs-Yukawa models, or to search for alternative scenarios in studying their fixed-point structure, if sufficiently large lattices can be reached.

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

A bstract The SU(2)–gauged custodial two-Higgs-doublet model, which shares the same global-symmetry properties with the standard model, is studied non-perturbatively on the lattice. The additional Higgs doublet enlarges the scalar spectrum and opens the possibility for spontaneous breaking of the global symmetry. In this work we start by showing the occurrence of spontaneous breaking of the custodial symmetry in a region of the parameter space of the model. Following this, both the spectrum and the running of the gauge coupling of are examined at weak quartic couplings in the presence of the custodial symmetry. The calculations are performed with energy cutoffs ranging from 300 to 600 GeV on a line of constant standard model physics, obtained by tuning bare couplings to fix the ratio between the masses of the Higgs and the W bosons, as well as the value of the renormalized gauge coupling at the scale of the W boson mass. The realizable masses for the additional scalar states are explored. For the choice of bare quartic couplings in this work, the estimated lower bound of these masses is found to be well below the W boson mass, and independent of the cutoff. We also study the finite temperature electroweak transition along this line of constant standard model physics, revealing properties of a smooth crossover behavior.