Brain Research Bulletin

Background: Although opioids are frequently prescribed for refractory chronic neuropathic pain, few data are available on baseline factors that may identify individuals more likely to respond and inform individually tailored practice. Methods: This was a post hoc, exploratory analysis of individual-level data from a placebo-controlled trial of methadone for chronic neuropathic pain (NCT05235191). Baseline clinical, psychological, psychophysical and radiological variables were tested for association with 30% or more pain reduction in the methadone group. The correlation between expectation of improvement and pain reduction was examined in both groups. Results: Of the 81 participants completing the 8-week follow-up, 28 (70%) on methadone and 14 (34.1%) on placebo achieved 30% or more pain reduction. In the methadone group, responders had higher baseline expectation of improvement (median 100 [80-100] vs. 55 [50-81]; p=0.004), which correlated moderately with percentage pain reduction (rs=0.508; p<0.001). Neither finding was observed in the placebo group (p=0.211 and p=0.161, respectively), although the correlation did not differ significantly between groups (p for interaction=0.728). Other clinical, sociodemographic, psychological and psychophysical variables were not associated with response. In the subsample with peripheral neuropathic pain and structural MRI (n=37), lower left precentral cortex volume was associated with response to methadone (p=0.041), and lower right paracentral volume with response to placebo (p=0.003). Conclusions: Baseline expectation may influence treatment outcomes, supporting its inclusion as a covariate to improve the precision of treatment effect estimates in trials, and its use in clinical practice to help tailor treatment to individual patients. These exploratory findings require confirmation in larger studies.

Background Spinal manipulation therapy (SMT) is widely used for low back pain (LBP), yet comparative effectiveness findings across trials remain inconsistent. One potential explanation is incomplete separation of treatment-specific effects from natural recovery and placebo response, which may differ between acute and chronic presentations. Methods We conducted a Bayesian hierarchical analysis of 120 randomized controlled trials (298 arms; 19,059 patients) evaluating SMT, exercise, medical management, physical modalities, self-care, and control conditions. Natural history (NH) trajectories were derived from independent observational cohorts and incorporated as external model components. Treatment effects were estimated as deviations from NH, and sham-corrected effects as deviations from control conditions. Results In acute LBP, NH trajectories predicted substantial improvement by 26 weeks. Estimated treatment effects relative to NH were negative across all categories, and sham-corrected effects were near zero, with credible intervals overlapping zero. In chronic LBP, NH trajectories predicted more gradual improvement. Control conditions exceeded NH, consistent with a substantial placebo response. Exercise and SMT demonstrated positive sham-corrected effects with credible intervals excluding zero; however, estimated magnitudes were modest and did not consistently exceed commonly cited thresholds for clinical importance. Higher study quality was associated with smaller estimated effects in chronic trials. Conclusions Modeling natural history as a structural component of treatment response alters estimates of comparative effectiveness in LBP. Improvements observed in acute LBP appear largely consistent with underlying recovery processes, whereas in chronic LBP, contextual and placebo-related effects contribute substantially to observed outcomes. Explicit separation of these components may help reconcile inconsistencies in the literature and inform interpretation of treatment effects.

Background: Temporary 60-day percutaneous peripheral nerve stimulation (PNS) can relieve pain in refractory occipital neuralgia (ON), but whether it alters acute healthcare utilization relative to the natural trajectory of the disease is unknown, because prior studies have lacked a concurrent comparator. Methods: We performed a secondary analysis of prospectively collected data from a tertiary interventional pain center. Adults with medically refractory ON (primary diagnosis occipital neuralgia, ICD-10-CM M54.81) who underwent 60-day occipital PNS (n = 42) were compared with concurrent patients who were clinically eligible for PNS but were denied insurance authorization and continued optimized non-PNS management (usual care; n = 63). Headache-related emergency department (ED) encounters - index (occipital neuralgia) and non-index (other headache/facial-pain diagnoses) - were ascertained over the 12 months before and after the treatment window via the electronic record and the statewide health information exchange. Patients were 1:1 propensity-matched on a priori confounders (age, sex, baseline total headache-related ED utilization, HIT-6, and baseline headache-medication use). The primary analysis was a difference-in-differences (DiD) comparison of the change in headache-related ED utilization, reported as the additive difference (Hodges-Lehmann estimate, distribution-free 95% CI) and, alongside, as a multiplicative rate ratio (RR). Results: Thirty-six matched pairs were well balanced. Baseline headache-related ED utilization was nearly identical between matched groups (6.3 vs. 6.2 visits/year). Over the following year, PNS patients' utilization fell to 2.5 while usual-care patients' rose to 6.9. The DiD was 5.0 fewer headache-related ED visits per patient (95% CI 3.0-6.0; p < 0.001), corresponding to a rate ratio of 0.36 (95% CI 0.27-0.48; p < 0.001). Results were consistent in the full sample, in covariate-adjusted models, and after excluding controls denied for incomplete conservative-therapy trials. The effect was driven by non-index headache visits. Conclusions: Relative to a concurrent, matched usual-care cohort whose headache related ED utilization rose over time, 60-day occipital PNS was associated with a substantial reduction in acute-care use, strengthening the case for a role of PNS on healthcare utilization in refractory ON.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Background: Chronic musculoskeletal pain (CMP) is a common and debilitating symptom of Long COVID (LC), which shares several clinical features with Fibromyalgia Syndrome (FMS). Aims: This longitudinal study investigated whether the symptom overlap between LC-CMP and FMS persists over time, whether patterns differ according to pre-COVID CMP history, and the extent to which neuropathic pain features are present. Methods: Individuals with LC completed questionnaires assessing the symptom-based component of the 2010 American College of Rheumatology criteria for FMS and the presence of neuropathic pain features (PainDetect Questionnaire, PDQ) at baseline, 6- and 12-month follow-ups. Participants were grouped according to pre-COVID CMP history. Group differences were analysed using mixed-effects logistic regression. Results: 240 individuals completed the baseline assessment at a mean of 2.61 years after LC onset, including 131 (100 females) with new-onset CMP and 94 (87 females) with pre-existing CMP. The prevalence of participants meeting the symptom-based criteria for FMS remained high across follow-up (67.5-78.6%), with 50.0-61.0% of those with complete follow-up data fulfilling criteria at all timepoints. No significant subgroup differences were observed (OR = 0.43, 95% CI [0.14 1.39], p = 0.160). Across follow-up, 22.2-36.1% of participants reported likely neuropathic pain features, with significantly higher odds in the pre-existing CMP group (OR = 3.17, 95% CI [1.22 8.18], p = 0.017). Conclusions: FMS-like symptoms remain highly prevalent several years after acute COVID-19 and persist over a further 12 months, suggesting that LC-CMP frequently represents a persistent long-term condition with substantial overlap with FMS. These findings support closer integration of LC and chronic pain rehabilitation services and add to the wider literature linking post-infectious processes with FMS-like symptoms.

Background: Cervical spinal cord (CSC) compression and myelopathy are frequent, severe complications in mucopolysaccharidosis (MPS) types I and VI. Early detection is critical to prevent irreversible neurological deficits and preserve quality of life. This prospective study aimed to identify quantitative neuroimaging biomarkers of structural CSC damage and myelopathy in MPS I and VI. Methods: Structural and diffusion magnetic resonance imaging (MRI) of the CSC was analyzed in 17 MPS I patients, 3 MPS VI patients (19{+/-}7 years), and 19 case-matched healthy controls (20{+/-}7 years). CSC macrostructure (cross-sectional area, diameters) and microstructure (fractional anisotropy [FA], radial [RD], mean, axial diffusivities) were evaluated within and beyond the maximal compression areas. Results: MPS patients demonstrated reduced CSC cross-sectional area and anterior-posterior diameter at upper cervical levels, alongside widespread microstructural alterations (lower FA, higher RD) throughout the CSC versus controls (pFDR<0.05). Restricted foramen magnum space was associated with microstructural damage at lower cervical levels without overt macrostructural compression. Morphological and microstructural metrics were sensitive to CSC compression and myelopathy severity (pFDR<0.05). Lower CSC integrity correlated with more advanced growth retardation (p<0.001). Among patients receiving hematopoietic stem cell transplantation, earlier treatment correlated with preserved upper CSC morphology (p<0.05). Distinct neuroimaging phenotypes were observed across MPS genotypes (pFDR<0.05). Conclusions: CSC MRI provides candidate neuroimaging biomarkers for early detection and characterization of myelopathy in MPS I and VI. Structural damage extends beyond primary maximal compression areas, highlighting these markers for clinical monitoring and future therapeutic trials.

The upper cervical spinal cord is essential for motor, sensory and autonomic function, yet its normative morphometric trajectories and disease-related deviations remain unclear. Using three-dimensional T1-weighted images from 10,159 healthy individuals aged 8-85 years, we established normative references for averaged and level-specific upper cervical spinal cord morphometry, revealing measure-specific trajectories with peak ages of 29.7-44.1 years. These references were applied to 2,394 patients with seven neurological disorders: mild cognitive impairment, Alzheimer' s disease, Parkinson' s disease, cerebral small vessel disease, multiple sclerosis, neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein antibody-associated disease to derive individualized deviation scores. These deviation scores captured disease-specific abnormalities, enabled disease classification, and were associated with cognition, physical disability and disease progression. Finally, a personalized upper cervical spinal cord report was developed to support individualized imaging interpretation. Together, these findings provide a normative framework for quantifying upper cervical spinal cord morphometry and advancing personalized assessment across neurological disorders.

Importance: Pain varies in its intensity and course, yet much of our understanding of pain is based on cross-sectional assessments or longitudinal studies of selected clinical populations. Routinely collected electronic health record data may enable characterization of longitudinal pain patterns at scale. Objective: To identify longitudinal pain profiles among U.S. Veterans and characterize their clinical and polygenic correlates. Design: Cohort study using electronic health record and genomic data from the Million Veteran Program, with up to 16 years of pain assessments collected between 2000 to 2025. Setting: U.S. Department of Veterans Affairs health care system. Participants: Million Veteran Program participants assigned to African-like, Admixed American-like, East Asian-like, European-like, or South Asian-like genetically inferred ancestry groups who have at least three years of outpatient numeric rating scale pain records. Exposures: Outpatient pain ratings and polygenic scores for psychiatric, sleep, and substance use traits. Main Outcomes and Measures: Pain profiles were identified using Gaussian finite mixture modeling of pain intensity, directional change, and within-person variability. Profiles were characterized by pain, psychiatric and substance use diagnoses, opioid exposure, body mass index, and polygenic scores. Results: Among 624,996 participants (Mean [SD] age at first pain rating=53.4 [13.5] years; 568,833 [91.0%] male), 51,731,567 outpatient pain ratings were analyzed. Three profiles were identified: stable no pain, stable mild pain, and mild-to-moderate increasing pain. Compared with the stable no pain profile, the increasing pain profile had higher odds of chronic pain (OR=21.14 [95% CI: 18.96-23.57]), opioid use disorder (OR=9.32 [95% CI: 8.31-10.45]), opioid exposure (OR=8.61 [95% CI: 8.41-8.83]), major depressive disorder (OR=3.77 [95% CI: 3.67-3.87]), and posttraumatic stress disorder (OR=3.72 [95% CI: 3.61-3.84]). Associations were attenuated but remained after adjustment for mean pain severity. Polygenic scores for depression, insomnia, attention-deficit/hyperactivity disorder, and tobacco use disorder were the most consistent predictors of increasing pain across the three largest genetically inferred ancestry groups. Conclusions and Relevance: Longitudinal modeling of routinely collected pain scores identified pain profiles associated with clinical burden and polygenic liability. These profiles captured clinically meaningful information beyond mean pain severity alone, highlighting the value of longitudinal pain phenotyping in electronic health record data.

Background: Ultra-low field (ULF) magnetic resonance imaging (MRI) offers accessible point-of-care neuroimaging, yet its capability to support multivariate network analyses remains understudied. Methods: We systematically evaluated the feasibility and reliability of Morphometric Similarity Networks (MSN) and Morphometric Inverse Divergence (MIND) networks derived from paired high-field (HF, 3T/1.5T) and ULF (64mT) scans in a primary cohort(N=23) and an independent replication cohort (N=19). We tested the impact of super-resolution processing, cortical parcellation granularity (Desikan-Killiany vs. Destrieux), and mesh resolution (fsnative vs. fsaverage5) on edge-level cross-scanner agreement (Lin's concordance correlation coefficient, CCC) and subject-level identifiability. Results: MIND networks demonstrated higher edge-level HF-ULF agreement (mean CCC = 0.95) and ULF reproducibility (mean CCC = 0.98) compared to MSNs (mean CCC = 0.86-0.87 and 0.91-0.94, respectively). In contrast, MSNs yielded higher subject-level differential identifiability across field strengths due to a broader distinction between within- and between-subject similarity. Reconstructing MIND networks on native high-resolution meshes (fsnative) maximised cross-scanner agreement, whereas coarser meshes and finer cortical parcellations enhanced individual network identifiability. Conclusions: Structural similarity networks can be reliably constructed from ULF MRI data. Optimal analytic choices depend on study objectives: MIND on native meshes maximises cross-scanner stability, whereas MSNs excel at preserving individual uniqueness, paving the way for globally scalable connectomic research.

The -Bonacci weighted sequence space {ell}(,) is introduced for [&ge;] 2, 1 [&le;] < {infty}, and > 0). It is proved to be a Banach space which is isometrically isomorphic to {ell}, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order - / are also established. Based on this framework, Scaled &#119898-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant &#119898-Bonacci root and employs depth wise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and a 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset while reducing parameters and FLOPs by 47.10% and 44.48%, respectively. On the CKD ultrasound dataset, Hexanacci-Net achieved 83.75% accuracy and a 0.7941 macro F1-score, with corresponding reductions of 50.37% and 45.10%. These findings support dominant-root scaling as a structured approach to lightweight CNN design.

Many pathological conditions, including chronic pain (CP), can impair cognitive functions. These functions are essential for adapting and evolving in concordance with the environment, which are key processes for coping with CP. However, in CP centers, cognitive assessment is rarely included in CP patient follow-up due to a lack of time, appropriate tools, or trained health professionals. To fill this gap, our team developed EasyCog, a digital tool that enables rapid self-assessment of cognitive functions. It enables cognitive evaluation without an experimenter's help, thanks to speech recognition powered by an artificial neural network. In the initial validation steps, we established normative values for 7 cognitive functions on a cohort of 76 healthy subjects. Because EasyCog includes tests comparable to the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE), we assessed its reliability against available normative values. To assess its specificity, we compared data from healthy subjects with 15 subjects with mild cognitive impairment (MCI) and 15 age-matched CP patients selected from a medical center. Finally, we tested EasyCog's test-retest reliability by repeating sessions at different time intervals. EasyCog showed excellent sensitivity to age-related decline, similar to MoCA normative values. Several cognitive functions were significantly and differentially impaired in MCI subjects and CP patients compared to normative values in healthy subjects, confirming good specificity. Finally, test-retest analysis showed no effect at a 4-month interval. In conclusion, EasyCog appears to be a convenient and reliable numeric tool for rapidly assessing and monitoring cognitive functions in patients.

Non-pharmacological adjuncts are needed for chronic non-cancer pain in patients receiving long-term opioid therapy. Music listening may reduce pain, but most previous trials have used passive controls, limiting attribution of effects specifically to music. In this open-label, parallel- group, proof-of-concept randomized controlled trial, adults with chronic non-cancer pain receiving long-term opioid therapy were randomized 1:1 to four weeks of daily home-based music or audiobook listening. Analgesic outcomes included pain intensity, responder rates, quantity of pain relief, and multidimensional pain assessment; secondary outcomes included mood, quality of life, global impression of change, and safety. Of 70 participants entering the run-in period, 56 were randomized (28 per group). Adjusting for baseline pain intensity in a linear mixed-effects model, the between-group difference at end of treatment was -1.26 NRS points (95% CI -1.98 to -0.55; p<0.001; d=-0.70), favoring music. A [&ge;]30% pain-reduction criterion was met by 21.4% of participants in the music group and none in the audiobook group (p=0.023). An exploratory between-group difference in pain intensity favoring music was also observed at follow-up. Other analgesic and secondary outcomes showed no significant between-group differences, although exploratory within-group improvements in mood and health-related quality of life were observed with music. Both groups reported overall improvement in global perceived change, with ratings numerically favoring music. No intervention-related harms were observed. These findings support an analgesic benefit of music beyond the structured listening experience shared with the active comparator. Daily self-administered music listening may represent a feasible, low-cost adjunct for chronic non-cancer pain in patients receiving long-term opioid therapy.

Alterations in impulsivity characterize several neuropsychiatric disorders, however the neural processes that underlie an impulsive choice have not been identified. A recent body of literature suggests that there may be generalizable aspects of neural representations across animals. Whether this is also true for dynamic rather than just static (like geometrical) properties of neural representations, and specifically in the context of impulsive behaviors, is less clear. Therefore, the goal of this study is to characterize generalizable features of latent dynamics that disambiguate an impulsive from non-impulsive choice across many different data sets and animals. A deep learning-based latent factor model was constructed from neural recordings that were acquired in rat anterior cingulate cortex while performing a delay discounting task, which is commonly used to assess impulsivity. Prior work shows this brain region is critical for the performance of this task. We leveraged piecewise linear recurrent neural networks (PLRNNs) to perform a nonlinear latent factor analysis across animals and experimental sessions. Our PLRNN explained a substantially larger percent of variance in fewer dimensions than linear approaches. We cross-validated performance of the model on test data and following the disruption of recurrent connectivity or auto-regressive latent dynamics. These tests confirmed that our fitting procedure consistently embedded generalizable neuronal dynamics within the PLRNN. Further, we found that the latent dynamics exhibited strong rotational features and progressed faster for impulsive choices. In summary, our model captures properties of the latent dynamics that are consistent across multiple data sets and disambiguate impulsive choices.

Background Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and post-COVID condition are complex, debilitating illnesses characterized by persistent fatigue and post-exertional malaise. Recovery is uncommon without targeted intervention; however, emerging clinical reports suggest that a subset of patients experience full resolution of symptoms after engaging in mind-body approaches. Patient perspectives on these interventions are divided, and qualitative evidence exploring successful recovery pathways is rare. This protocol outlines a study to investigate the contextual factors and patient-constructed explanations surrounding full recovery from ME/CFS or post-COVID condition using mind-body approaches. Methods and analysis We will employ a qualitative descriptive design rooted in a constructivist paradigm. Purposive and snowball sampling will be used to recruit approximately 20 adults from established infection-associated fatigue networks who self-report full recovery (lasting three months or more) from ME/CFS or post-COVID condition following a mind-body approach. Data will be collected through semi-structured interviews conducted via Zoom, and investigator reflexivity will be reported. Demographic information, including comorbidities, will be documented to contextualize the sample. Data will be analyzed inductively using reflexive thematic analysis. To ensure research rigor and reporting transparency, the study will adhere to the 15-point checklist of criteria for good thematic analysis proposed by Braun and Clarke. Dissemination We will publish our findings in a peer-reviewed, open-access journal and present at relevant academic conferences. A lay summary of the results will also be shared directly with participating patient recovery networks.

We evaluated whether participant-separated within-cohort discrimination was retained when electroencephalography (EEG) classifiers for attention-deficit/hyperactivity disorder (ADHD) were applied to a different population and recording task. This exploratory reanalysis included 121 children performing a visual task and 79 adults recorded during eyes-open rest after basic channel screening. Both cohorts used Cz/F4, 128 Hz, and the first fifteen non-overlapping 2-second epochs per participant. Fixed logistic regression and random forest classifiers used spectral, amplitude-derived, and combined features. Internal evaluation used five repetitions of five-fold participant-grouped cross-validation; bidirectional transfer fitted transformations and classifiers only on the source cohort. Adult spectral balanced accuracy (BA) was 0.9519-0.9546, compared with 0.5504-0.5664 in children. Adult spectral logistic regression exceeded all 999 internal label permutations (Monte Carlo p=0.001; within-family Holm-adjusted p=0.002). Paired spectral-minus-amplitude conditional intervals excluded zero in adults but spanned zero in children. Cross-cohort spectral BA was 0.3584-0.4675, with area under the receiver operating characteristic curve (AUC) of 0.0309-0.3857 in the unchanged score direction. Increasing pediatric source-epoch budgets moved random-forest AUC toward 0.5, whereas logistic-regression AUC remained below 0.1. Strong adult internal discrimination thus coexisted with reverse cross-cohort ranking. These findings characterize the evaluated pipelines, not clinical diagnostic utility or a unique mechanism: age, task, and acquisition differences were not separated, target results had previously been inspected, and conditional intervals excluded model-refitting uncertainty.

Focal cortical dysplasia (FCD) is among the leading causes of drug-resistant focal epilepsy and can be challenging to detect on routine MRI due to the subtle and heterogeneous appearance of lesions. Motivated by the scarcity of clinically representative cohorts, we investigate the feasibility of simulating FCD-like lesions in healthy MRI using a composition of hand-crafted image transformations. We develop SynthFCD, a phenomenological FCD simulator, and create 2,164 synthetic examples from healthy controls in the heterogeneous FOMO300k dataset. We then pretrain deep learning (DL) segmentation models on synthetic lesions and fine-tune them on real FCD cases from the University Hospital Bonn (UHB) cohort. Comprehensive evaluations show consistent improvements across simulator presets, with detection-rate gains of up to 18.5 percentage points over training from scratch and 7.4 points over self-supervised pretraining, while achieving state-of-the-art segmentation performance (0.366 DSC) on the challenging held-out UHB benchmark. These findings suggest that simulation-based synthetic data generation can bridge existing radiological knowledge and advances in DL to provide meaningful supervisory signals that could ultimately aid the detection of underrepresented and diagnostically challenging cases.

Existing MRI-based markers have limited sensitivity and specificity for assessing myelin in the brain. This study investigated whether multi-parametric MRI could improve myelin mapping in the developing mouse brain. T1, T2, magnetization transfer, and diffusion MRI data were acquired at 7 Tesla from ex vivo myelin-associated oligodendrocyte basic protein (MOBP)-enhanced green fluorescent protein (eGFP) mouse brains at postnatal day 14 (P14), P35, and P56. Serial two-photon tomography data from the same mouse brains were used to quantify myelin content based on the MOBP signal intensity. Although most MR parameters demonstrated significant age-related changes, only a subset correlated strongly with MOBP signals. Partial least squares regression analysis of MRI and MOBP data from the corpus callosum and other brain regions demonstrated that the multi-parametric approach improved myelin mapping in the developing mouse brain. T2 was ranked as the highest contributor to myelin estimation across multiple brain regions.

The pure-tone audiogram remains the cornerstone of hearing assessment, yet it captures only part of the functional consequences of cochlear damage. Most phenotyping schemes rely solely on the audiogram, which cannot detect hidden suprathreshold dysfunction when thresholds are normal. How best to represent audiological data - as discrete phenotypes or as a continuous space - remains unresolved. Using a retrospective database from the Copenhagen Hearing and Balance Centre, Rigshospitalet (Denmark; 1995-2022; 251986 ear-session units from 81883 adults with normal or sensorineural audiograms), we compared linear (PCA) and non-linear (UMAP) embeddings of the audiogram alone and with word recognition in quiet (WRSmax-Q). Here we show that a linear embedding suffices for the audiogram, but a non-linear one becomes advantageous once suprathreshold measures are added. PCA compressed the audiogram onto two interpretable axes, overall level and slope, explaining 91.5% of the variance, with ears forming a continuous cloud that published reference schemes tiled into overlapping regions. For the audiogram alone, UMAP recovered the same organisation. WRSmax-Q varied widely among ears with similar audiograms; adding it left the PCA unchanged but reorganised the UMAP. A synthetic experiment illustrated this: ears with identical audiograms but different WRSmax-Q separated under UMAP, not PCA. Combining thresholds with WRSmax-Q, UMAP yielded a continuous representation - a prototype Hearing Loss Map - where each ear's position reflects threshold and suprathreshold measures. Representing hearing loss as a location in a continuous space rather than as discrete categories may better reflect its graded nature, with clinical relevance for individual patients.

Accurate prediction of individual medical outcomes is essential for optimizing treatment allocation amid rising costs, coverage denials, and limited clinical resources. Traditional predictive models, including linear regression and neural networks, rely on average effects and cannot tailor predictions to the specific circumstances of individual cases. We present relevance-based prediction (RBP), a model-free and task-specific method that predicts outcomes as weighted averages of observed cases, with weights determined by a rigorously defined and theoretically justified measure of relevance. Unlike model-based methods that rely on fixed calibrated parameters, relevance-based prediction revisits the original data for each prediction and customizes both the cases and variables used. We illustrate RBP by applying it to predict opioid treatment outcomes and demonstrate that it provides case-specific insights unavailable from conventional models, including how each prior case informs a prediction, how each variable affects each predictions reliability and value, and how reliable each prediction is before it is made. These individualized insights may prevent misleading average-based decisions and reduce harmful or suboptimal treatment.

Open-source deep learning models promise scalable quantification of MRI-visible perivascular spaces (PVS), an emerging marker of glymphatic dysfunction in Alzheimer's disease (AD). Whether these models remain valid when applied to heterogeneous AD cohorts and preserve ground-truth derived biological associations has not been established. We benchmarked five open-source nnU-Net-based PVS segmentation models (nnU-Net T1, nnU-Net T1+FLAIR, mcPVS-Net, MedNet-PVS, ADNI-PVS-Net) against manual labels in 60 multi-site ADNI-3 participants balanced across cognitively unimpaired, mild cognitive impairment, and AD dementia, and in 12 publicly available scans from the VAscular Lesions object-level and segmentatiOn (VALDO) challenge. We evaluated overall, voxel, object, and lesion-wise Dice; stratified performance by region, diagnosis, scanner, PVS severity, and white matter hyperintensity (WMH) burden; and tested whether model-derived PVS burden reproduced ground-truth derived associations with AD-related biomarkers. Despite high internal performance, all models showed a specialist-generalist trade-off: the AD-trained ADNI-PVS-Net achieved highest internal performance (overall Dice = 0.58 {+/-} 0.14) but degraded most externally (Dice [&le;] 0.11), while MedNet-PVS and mcPVS-Net generalized externally (Dice 0.27-0.30) but systematically misestimated PVS burden. Models were relatively robust across scanner and diagnosis but inconsistent across PVS severity, and some misclassified up to 32% of WMH as PVS. Notably, only AD-trained models reproduced the expected PVS-amyloid biomarker relationship; other models returned a null result, despite comparable or superior Dice scores. These findings show that internal segmentation accuracy and external generalizability alone do not guarantee downstream inferential validity. Careful model selection and cohort-specific validation against manual labels should precede clinical inference in AD.