Bone

Objective To evaluate the Cartilage Thickness Score (CTh-Score) as a quantitative measure of cartilage damage severity by assessing its association with three osteoarthritis (OA) milestones and comparing its performance with conventional morphometric measures (radiographic minimum joint space width (JSW) and regional average cartilage thickness). Methods Data were obtained from the Osteoarthritis Initiative (OAI) and the publicly available OAI CTh-Maps and CTh-Score dataset. Three matched case-control designs were used to represent major OA milestones: (i) incident radiographic OA onset, (ii) combined pain and structural progression, and (iii) knee replacement (KR) in the coming 2 years. Progression subjects were extracted from the FNIH Biomarkers Consortium cohort. Cases and controls were compared at 4 years (T-4Y), 2 years (T-2Y), and 0 years (T0) before the milestone. MRI-based CTh-Score and regional average cartilage thickness, as well as JSW, were analyzed cross-sectionally and longitudinally. Associations with case status were assessed using adjusted logistic regression models, and responsiveness was evaluated using longitudinal change and standardized response means. Results The onset cohort included 307 matched case-control pairs, the progression cohort 164 cases and 369 controls, and the KR cohort 81 cases and 324 controls. Across all three study designs, the CTh-Score significantly differentiated cases from controls at all timepoints. In the onset cohort, the CTh-Score was higher in future cases than controls at T-4Y (16.2 vs 12.6, p=0.007), T-2Y (23.5 vs 16.7, p<0.001), and T0 (39.8 vs 18.6, p<0.001), whereas JSW and regional thickness measures showed limited or later discrimination. Similar findings were observed for progression (43.2 vs 33.0 at T-4Y; p<0.001) and KR (55.4 vs 46.1 at T-4Y; p=0.02) cohorts. Longitudinally, CTh-Score changes differentiated cases from controls earlier and more consistently than JSW or regional average thickness, and its responsiveness was consistently the highest across OA milestones and time intervals. In adjusted models, the CTh-Score was independently associated with all outcomes at T-4Y and T-2Y, with odds ratios per standard deviation increase ranging from 1.3 to 2.2. Conclusion The CTh-Score captures high-resolution cartilage thickness patterns associated with OA onset, progression, and future knee replacement, outperforming conventional morphometric measures in early discrimination, responsiveness, and predictive association. These findings support CTh-Score as a sensitive quantitative marker of cartilage damage severity across the OA continuum.

Objectives: Knee osteoarthritis (KOA) is a leading cause of disability, yet which patients will experience structural decline remains unclear. Body mass index (BMI) and lower limb alignment are established risk factors for KOA, but their independent and interactive effects on compartment-specific cartilage loss and total knee replacement (TKR) have not been characterized at scale. Methods: We analyzed 5,832 limbs from 3,016 participants in the Osteoarthritis Initiative followed over 7 years. Cartilage thickness in the weight-bearing medial and lateral femur and tibia was quantified, and lower limb alignment was measured using hip-knee-ankle (HKA) angle obtained from full-limb radiographs. Linear mixed-effects models estimated the independent and interactive effects of BMI and lower limb alignment on longitudinal cartilage thinning, and mixed-effects logistic regression modeled TKR risk. Results: In the medial compartment, BMI and varus alignment interacted multiplicatively, with their combined effect exceeding the sum of independent contributions (femur: p = 0.011; tibia: p < 0.001). At +10 kg/m2 BMI and +10 degrees varus, the rate of medial femur cartilage thinning was 243.5% faster than the reference rate. In the lateral compartment, BMI and valgus alignment were independently associated with faster cartilage thinning, with no significant interaction. TKR risk increased exponentially with HKA deviation (odds ratio [OR] = 1.38 per 1 degree; ~five-fold at 5 degrees malalignment) but was not associated with BMI. Conclusion: BMI and lower limb alignment influence structural KOA progression through compartment-specific pathways. The multiplicative interaction in the medial compartment identifies high BMI combined with varus malalignment as a discrete high-risk phenotype, with implications for clinical risk stratification and disease-modifying intervention design.

Objectives. The association between skeletal muscle gene expression and knee osteoarthritis (OA) was examined among older adult participants of the Study of Muscle, Mobility and Aging (SOMMA). Methods. Inclusion criteria included knee radiographs and bulk RNA sequencing (RNAseq) in vastus lateralis muscle, resulting in 523 participants (56% female). Radiographic knee OA was determined by Kellgren-Lawrence (KL) grades. Differential gene expression was analyzed using a control group (KL [&le;] 1, n = 326) and two nested case groups: (a) KL [&ge;] 2 (n = 197), (b) KL [&ge;] 3 (n = 112). Results. Compared with controls, there were 27 and 41 genes associated (FDR [&le;] 0.05) with KL [&ge;] 2 and KL [&ge;] 3, respectively, and 16 genes significantly associated in both contrasts. For 15 of the 16 genes, the association magnitude was larger with more severe OA (KL [&ge;] 3). Genes associated in both contrasts included brain-derived neurotrophic factor (BDNF) and interferon regulatory factor-2 (IRF2). Gene sets enriched in KL [&ge;] 2 and KL [&ge;] 3 contrasts included DNA repair and branched chain amino acid (BCAA) catabolism. Conclusions. Our results in older adult SOMMA participants indicate that knee OA is associated with genes and pathways expressed in skeletal muscle that are involved in pain sensitization, BCAA catabolism, muscle function preservation, calcium transport and storage, inflammation, and extracellular matrix remodeling. Additional longitudinal studies will be needed to determine how these genes could affect the progression of knee OA.

Objectives: For suspected hip fractures, prehospital protocols directing patients to an orthopaedic centre rather than the nearest emergency department (ED) could reduce time-to-surgery but may impact EMS travel burden. This study evaluates the impact of transfer protocols by quantifying transport to hospitals from long term care (LTC) facilities across Ontario. Methods: A retrospective cross-sectional analysis of all Ontario LTC facilities and hospitals was performed. Two protocols were modeled: standard transfer to the nearest ED with subsequent transfer if required, and selective transfer based on Collingwood Hip Fracture Rule prehospital screening1 directly to the nearest orthopaedic services (orthoED). Median one-way travel distances were calculated from Google Maps. Results: In Ontario, 15.4% of LTC residents require hospital destination decisions because their nearest ED lacks orthopaedic services; for these facilities, median distances were 2.7km to the ED and 36.0km to the orthoED. Among the 52 LTC facilities where selective transfer was distance-optimal, it substantially reduced travel for patients with hip fracture (31.1km vs 49.6km; P<.01) while only modestly increasing travel for patients without hip fracture. Where standard transfer was distance-optimal, little travel difference was noted for patients with hip fracture, however false positive screened patients traveled significantly further to an orthoED. Greatest negative consequences of selective transfer lie in the 1.3% of residents living farthest (>100km) from an orthoED. Conclusions: EMS direct transportation to hospitals with orthopaedics may improve hip fracture care but can increase EMS burden due to patients identified falsely as having a hip fracture, particularly in remote communities.

BackgroundAdequate maternal nutritional knowledge is essential for healthy pregnancy outcomes, yet many pregnant women lack good nutritional knowledge. This study assessed nutritional knowledge and associated factors among pregnant women in the Krowor Municipality of Ghana. MethodsA facility-based cross-sectional study was conducted among pregnant women attending antenatal clinics in two public health facilities. Structured questionnaires were used to collect data on sociodemographic characteristics and nutritional knowledge. Data were analysed using descriptive statistics and chi-square tests at a 5% significance level. ResultsMost respondents demonstrated moderate nutritional knowledge (mean score =11.24 {+/-} 2.48), with 45% classified as having moderate knowledge. Income level (p = 0.00), education (p = 0.007), gestational age (p = 0.042), employment status (p = 0.007), and religion (p = 0.005) were significantly associated with nutritional knowledge. ConclusionThe study highlights notable gaps in nutritional knowledge among pregnant women in Krowor Municipality. Socioeconomic and obstetric factors strongly influenced nutritional knowledge. Strengthening antenatal nutrition counselling and improving socioeconomic support may help improve the nutritional knowledge of pregnant women.

Background: Creatine monohydrate (typically 5 to 20 g/day) has a well-established safety profile across diverse populations. Creatine hydrochloride (CR-HCl) is a highly soluble creatine formulation that may allow effective supplementation at substantially lower doses (750 mg to 3 g/day); however, controlled human safety data specific to CRHCl remain limited. Objective: To evaluate the short-term laboratory safety and tolerability of low dose CRHCl supplementation administered for 28 days in healthy adults. Methods: This single center, single arm, singl blind pilot safety study enrolled 11 healthy adults (10 females, 1 male; mean age 44.6 plus/minus 7.2 years). Participants consumed 750 mg/day CRHCl for 28 consecutive days while maintaining their usual diet and physical activity patterns. Fasting blood and urine samples were collected at baseline and Day 28. Laboratory assessments included hematological, lipid, and clinical chemistry biomarkers. Pre and post changes were evaluated using paired parametric and nonparametric tests, baseline-adjusted regression models, bootstrap confidence intervals, and false discovery rate (FDR) correction. Results: All participants completed the intervention. No clinically meaningful changes were observed in lipid parameters, hematologic indices, renal markers, or most chemistry analytes after adjustment for multiple comparisons. Fasting glucose increased modestly (8.1 mg/dL) prior to multiplicity adjustment but was not statistically significant after FDR correction and remained within reference ranges. Serum bicarbonate decreased slightly (2.4 mmol/L); although statistically detectable in parametric analysis, values remained within physiological limits and were not consistently supported by nonparametric testing.

Hericium erinaceus (Lions Mane) is a functional mushroom with a long history of culinary and traditional use, as well as potential neurotrophic and mood modulating properties. Evidence for its effects on cognitive performance under real world conditions, however, remains limited. In this randomized, double blind, placebo controlled trial, adults aged 40 to 75 years with self reported cognitive difficulty completed a one week baseline followed by eight weeks of daily supplementation with 2 g of H. erinaceus fruiting body and mycelial biomass or placebo. Cognitive performance using a computerized battery, as well as daily subjective assessments of sleep and wellbeing, were collected remotely. 109 Participants were included in the primary analysis (H. erinaceus, n = 57; placebo, n = 52). H. erinaceus was associated with significantly greater improvement in visual attention and working memory (Juggle Factor task), subjective sleep quality, morning restedness, and mood compared with placebo (p < 0.05). No adverse events were reported in participants receiving H. erinaceus. Together, H. erinaceus supplementation modestly improved visual attention and was associated with faster improvements in sleep quality, restedness, and mood in adults with subjective cognitive concerns.

BackgroundDental caries and periodontal disease represent the most prevalent global oral health conditions, collectively affecting several billion people. The diagnostic interpretation of dental radiographs, a cornerstone of modern dentistry, is associated with considerable inter-observer variability. In routine clinical practice, clinicians are required to evaluate a high volume of radiographic images daily, a cognitively demanding task in which diagnostic fatigue, time constraints, and the inherent complexity of overlapping anatomical structures can lead to the inadvertent oversight of early-stage pathologies. Artificial intelligence (AI) offers a transformative opportunity to augment clinical decision-making by providing rapid, objective, and consistent radiographic analysis, thereby serving as a tireless adjunct capable of flagging findings that may be missed during routine human inspection. MethodsThis study developed and validated a deep learning system for the automated detection of dental caries and alveolar bone loss using a dataset of 1,063 periapical and bitewing radiographs. Two separate YOLOv8s object detection models were trained and evaluated using a rigorous 5-fold cross-validation methodology. To align with the clinical use-case of a screening tool where high sensitivity is paramount, a custom image-level evaluation criterion was employed: a true positive was recorded if any predicted bounding box had a Jaccard Index (IoU) > 0 with any ground truth annotation. Model performance was systematically evaluated at confidence thresholds of 0.10 and 0.05. ResultsAt a confidence threshold of 0.05, the caries detection model achieved a mean precision of 84.41% ({+/-}0.72%), recall of 85.97% ({+/-}4.72%), and an F1-score of 85.13% ({+/-}2.61%). The alveolar bone loss model demonstrated exceptionally high performance, with a mean precision of 95.47% ({+/-}0.94%), recall of 98.60% ({+/-}0.49%), and an F1-score of 97.00% ({+/-}0.46%). ConclusionThe YOLOv8-based models demonstrated high accuracy and high sensitivity for detecting dental caries and alveolar bone loss on periapical radiographs. The system shows significant potential as a reliable automated assistant for dental practitioners, helping to improve diagnostic consistency, reduce the risk of missed pathology, and ultimately enhance the standard of patient care.

Markerless motion capture has emerged as a potential substitute for traditional marker-based systems, offering scalable, non-invasive acquisition of human movement. Despite increasing adoption in research and sports applications, its clinical utility for children with complex gait patterns remains an open question. To address this gap, simultaneous marker-based and markerless data were collected in 202 pediatric children (12.1 {+/-} 3.9 years). Marker-based kinematics were processed using the Shriners Children's Gait Model (SCGM), while markerless outputs were computed using Theia3D with identical Cardan sequences. Agreement between systems was evaluated using statistical parametric mapping (SPM), root-mean-square error (RMSE), and a gait pattern classification based on the plantarflexor-knee extension index. Markerless output systematically underestimated pelvic tilt, hip rotation, and knee rotation and demonstrated reduced between-subject variance in the transverse plane. SPM revealed widespread waveform differences, although most were of negligible effect, especially in the sagittal plane. Mean sagittal-plane RMSEs were < 5{degrees} for the knee and ankle and < 8{degrees} for the pelvis and hip. Coronal-plane deviations were < 7{degrees}, whereas transverse-plane errors exceeded 10{degrees}. RMSE increased significantly with body mass index and use of a walker (p < 0.001). Agreement in sagittal-plane gait classification was moderate between systems ({kappa} = 0.60; 67% overall concordance). These results indicate that markerless motion capture is suitable for analyses emphasizing sagittal deviations but remains limited for applications requiring precise axial or frontal-plane estimation. Future work should address algorithmic underestimation of transverse motion and evaluate markerless performance across increasing severity of gait deviation.

BackgroundQuantitative cerebral blood flow (CBF) measured with [15O]water positron emission tomography (PET) is the reference standard for quantifying brain perfusion. However, clinical interpretation of individual CBF measurements is limited by the absence of large normative datasets accounting for physiological variability across the adult lifespan. Long-axial field-of-view PET enables high-sensitivity quantitative [15O]water perfusion imaging without arterial blood sampling, allowing normative characterization of cerebral perfusion at unprecedented scale. The aim of this study was to establish normative and covariate-adjusted models of cerebral blood flow across the adult lifespan using total-body [15O]water PET. MethodsQuantitative CBF measurements were obtained in 302 neurologically healthy adults (age 21-86 years) using total-body [15O]water PET. Linear mixed-effects models were used to evaluate the effects of age, sex, body mass index (BMI), and blood hemoglobin concentration on CBF and to generate normative prediction models across the adult lifespan. Between-subject and within-subject variability were estimated from repeated scans in a subset of participants (n=51). ResultsMean grey matter CBF was 46.1 mL/(min*dL), with substantial inter-individual variability but high within-subject reproducibility (intraclass correlation coefficients 0.78-0.89). Advancing age was associated with a decline in CBF of approximately 7% per decade (p_FDR < 10-12). Higher BMI was associated with lower CBF (approximately -6% per 10 kg/m2; p_FDR < 0.01). Women exhibited higher CBF than men (approximately 7.5%), but this difference was largely explained by lower blood hemoglobin concentration in women. Covariate-adjusted models were used to generate normative predictions and prediction intervals describing expected CBF across adulthood. ConclusionThis study establishes a normative database of quantitative cerebral blood flow across the adult lifespan using high-sensitivity [15O]water PET. Age, BMI, and hemoglobin are major determinants of inter-individual variability in CBF. The resulting generative models provide a quantitative reference framework for interpreting cerebral perfusion measurements and may enable automated detection of abnormal brain perfusion in clinical PET imaging.

This work explores the dietary intake of plant bioactives in the European adult population. The information available in the scientific literature is quite fragmented, with only partial knowledge of dietary bioactive intake and their health effects, and without harmonised figures across populations and phytochemical families. In this context, we comprehensively evaluated the intake of (poly)phenols, terpenoids, N-containing compounds, and miscellaneous phytochemicals in the European adult population, using public data from 26 countries reporting on 38,944 individuals. Further research was conducted to investigate the contributions of classes, subclasses, and individual compounds, as well as their relationships. Main food sources of each class and subclass of phytochemicals were also identified. Finally, variability in phytochemical intake across European countries was evaluated. This work significantly advances the current knowledge of plant bioactive intake and sets the stage for future research in nutrition and health fields.

BackgroundPeriodontal disease (PD) and diabetes mellitus (DM) have a well-established bidirectional relationship, affecting glycaemic control and chronic disease outcomes. However, the extent to which medical training supports physician awareness of this association remains unclear especially in resource-limited settings. ObjectiveTo assess exposure to oral health education and to identify predictors of awareness of PD-DM association among physicians. MethodsA cross-sectional study was conducted among 146 physicians managing diabetic patients at a tertiary teaching hospital in Ghana. A structured questionnaire assessed exposure to oral health education, periodontal disease knowledge (score range 0-5), and awareness using a 5-item Likert scale (score range 5-25). Multivariable linear regression identified predictors of awareness. ResultsAlthough 62.1% reported exposure to oral health content during undergraduate training, 59.2% rated its quality as poor. Mean awareness score was 20.6 (SD=2.8). Awareness was independently predicted by years of professional experience (p < 0.001) and periodontal disease knowledge (p = 0.008), but not by structured oral health curriculum exposure. ConclusionAwareness of the PD-DM link was high but was not explained by formal educational exposure. Awareness appears to develop through knowledge of PD and professional experience, suggesting a gap between curricular exposure and competency.

IntroductionMusculoskeletal conditions have high, and increasing, incidence and prevalence. Although there are many clinical guidelines available for common conditions, most are poor quality and sparsely adopted into practice. We aim to improve patient outcomes by developing robust Best Practice Guidelines (BPG) to get research findings into practice for a range of common musculoskeletal conditions. Methods and analysisMixed methods with systematic review of high-quality studies and qualitative elicitation of both patients perspectives and expert clinical reasoning through in-depth interviews will form the basis for the BPGs. A segregated convergent synthesis, informed throughout by stakeholder engagement, will guide the format and structure of the BPGs. Ethics, outputs and disseminationEthical approval for the qualitative studies and implementation events will be obtained from university and health service research ethics committees. Educational packages for each BPG condition will be hosted online and be available for students, clinicians, and education providers. Dissemination will follow traditional routes including publications and presentations; alongside innovative approaches such as collaboration with higher education institutions, online hosting, adoption by professional bodies, and a social media campaign. Implementation will occur adaptively in multiple national contexts to reflect local requirements and resources, deploying participatory and implementation methods that are contextually and culturally appropriate. KEY MESSAGESO_LIWhat is already known on this topic - Clinical guidelines for the management of musculoskeletal conditions are common, but have limitations regarding quality, applicability, editorial independence, and patient perspective. They are rarely adopted into clinical practice. C_LIO_LIWhat this study adds - We have developed a robust (supported by Patient and Participant Involvement) mixed-methods approach that integrates the three components of evidence-based medicine: synthesis of high-quality evidence, patients perspectives/values, and expert clinical reasoning. We have also developed an education, dissemination, and implementation approach to facilitate international adoption of these guidelines. C_LIO_LIHow this study might affect research, practice or policy - The guideline development methods will integrate the three pillars of evidence-based practice and ensure they are robust and clinically applicable. Creation of educational material combined with an implementation and dissemination plan will support adoption into clinical practice of different countries and cultures, designed to lead to improved patient outcomes. C_LI

The modern Western diet (MWD) provides high linoleic acid (LA) exposure, typically contributing 6-9% of total caloric intake. These high LA levels have fueled a longstanding debate regarding whether this dietary pattern confers benefit or risk. Importantly, LA intake is disproportionately elevated among lower socioeconomic populations due to greater reliance on industrial seed oils and ultra-processed foods. Despite decades of research, controlled dietary intervention studies directly evaluating the biological consequences of varying LA exposure remain limited. The current randomized, double-blind intervention compared the effects of a 12-week Low LA diet (2.5% energy) versus a High LA diet (10.0% energy) in healthy adults. Primary outcomes included plasma highly unsaturated fatty acid (HUFA) concentrations and ex vivo zymosan-stimulated whole-blood oxylipin generation. Fifty- two participants completed the intervention. High LA exposure resulted in a marked reduction in plasma n-3 eicosapentaenoic acid (EPA) concentrations compared with the LowLA arm. In contrast, levels of arachidonic acid (ARA), dihomo-gamma-linolenic acid (DGLA) and docosahexaenoic acid (DHA) did not differ by dietary LA exposure. Analysis of oxylipin species revealed that levels of EPA-derived relative to ARA-derived mediators were significantly reduced in the High LA arm. These findings reveal that higher dietary LA selectively suppresses EPA pools and EPA-derived oxylipins without altering ARA, shifting the lipid mediator balance toward a more n-6-dominant profile.

Computer vision and deep learning techniques, including convolutional neural networks (CNNs) and transformers, have increased the performance of medical image classification systems. However, training deep learning models using medical images is a challenging task that necessitates a substantial amount of annotated data. In this paper, we implement data augmentation strategies to tackle dataset imbalance in the VinDr-SpineXR dataset, which has a lower number of spine abnormality X-ray images compared to normal spine X-ray images. Geometric transformations and synthetic image generation using Generative Adversarial Networks are explored and applied to the abnormal classes of the dataset, and classifier performance is validated using VGG-16 and InceptionNet to identify the most effective augmentation technique. Additionally, we introduce a hybrid augmentation technique that addresses class imbalance, reduces computational overhead relative to a GAN-only approach, and achieves ~99% validation accuracy with both classifiers across all three case studies. Keywords: Data augmentation, Generative Adversarial Network, VGG-16, InceptionNet, Class imbalance, Computer vision, Spine X-ray, Radiology.

Introduction: Minimum dietary diversity (MDD) is a key indicator of complementary feeding among children aged 6-23 months. This study examines the prevalence, trends, and determinants of MDD in Bangladesh over the period 2014 - 2022. Design: Secondary analysis of the Bangladesh Demographic and Health Survey (BDHS) data between 2014 and 2022. The primary outcome was MDD defined as consumption of at least 5 of 8 food groups (MDD-8). We included 6,080 children aged 6-23 months to assess trends over time. The pooled datasets were used to identify factors associated with MDD-8. Multiple logistic regression was performed to assess the association between different factors and MDD-8, accounting for the complex survey design. Setting: Bangladesh Results: The proportion of children achieving MDD-8 increased from 26.4% in 2014 to 38.7% in 2017, but plateaued at 37.1% in 2022, with an average annual increase of 4.3% between 2014 and 2022. MDD-8 improved with child age. Higher odds of achieving MDD-8 were observed among children surveyed in later years, from wealthier households, with mothers who had >=4 ANC visits, received PNC, had higher education, were employed, and had media exposure. Older age and higher birth order were also associated with achieving adequate MDD. Children in Chattogram and Sylhet were less likely to meet MDD-8 compared to Dhaka. Conclusions: While dietary diversity improved between 2014 and 2017, progress stalled thereafter. Targeted, multisectoral strategies focusing on womens empowerment, health service utilisation, media engagement, and disadvantaged regions are needed to improve child dietary diversity in Bangladesh.

Background: Gamma oscillation dysfunction has been implicated in neuropsychiatric disorders. Restoring gamma oscillations via brain stimulation represents an emerging therapeutic approach. However, the strength of its clinical effects and treatment moderators remain unclear. Method: We conducted a systematic review and meta-analysis to examine the clinical effects of gamma neuromodulation in neuropsychiatric disorders. A literature search for controlled trials using gamma stimulation was performed across five databases up until April 2025. Effect sizes were calculated using Hedge's g. Separate analyses using the random-effects model examined the clinical effects in schizophrenia (SZ), major depressive disorder (MDD), bipolar disorder, and autism spectrum disorder. For SZ and MDD, subgroup analyses evaluated the effects of stimulation modality, stimulation frequency, treatment duration, and pulses per session. Result: Fifty-six studies met the inclusion criteria (NSZ = 943, NMDD = 916, NBD = 175, NASD = 232). In SZ, gamma stimulation was associated with improvements in positive (k = 10, g = -0.60, p < 0.001), negative (k = 12, g = -0.37, p = 0.03), depressive (k = 8, g = -0.39, p < 0.001), anxious symptoms (k = 5, g = -0.59, p < 0.001), and overall cognitive function (k = 7, g = 0.55, p < 0.001). Stimulation frequency and treatment duration moderated therapeutic effects. In MDD, reductions in depressive symptoms were observed (k = 23, g = -0.34, p = 0.007). Conclusion: Gamma neuromodulation showed moderate therapeutic benefits in SZ and MDD. Substantial heterogeneity likely reflects protocol differences, highlighting the need for well-powered future trials.

Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, resting cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion network alterations in psychiatric and neurological disorders. To characterize large scale functional organization in SPECT data, data driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.

ImportanceBlood neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are entering clinical use in neurology as markers of neuroaxonal and astrocytic injury, but their utility in psychiatry is unclear. ObjectiveTo determine whether psychiatric diagnoses are associated with altered plasma NfL and GFAP levels. Design, Setting, and ParticipantsThis population-based study examined plasma NfL and GFAP among 47,495 participants from the UK Biobank (54.0% female; 93.5% White; mean [SD] age 56.8 [8.2] years) who provided blood samples and sociodemographic and clinical data between 2006 and 2010. Normative modeling was applied to assess associations between 7 lifetime psychiatric diagnostic categories and deviations from expected NfL and GFAP levels, while accounting for neurological diagnoses, cardiometabolic burden, and substance use. Data were analyzed between July 2025 and March 2026. Main Outcomes and MeasuresDeviations in plasma NfL and GFAP levels from normative predictions. ResultsRelative to the reference population, plasma NfL levels were higher among individuals with bipolar disorder (d=0.20; 95% CI, 0.03-0.37; p=0.03), recurrent depressive disorder (d=0.23; 95% CI, 0.07-0.38; p=0.009), and depressive episodes (d=0.06; 95% CI, 0.02-0.10; p=0.01), lower among individuals with anxiety disorders (d=-0.07; 95% CI, -0.12 to -0.02; p=0.008), but did not differ in schizophrenia spectrum, stress-related, or other psychiatric disorders. Plasma GFAP levels were not elevated in any psychiatric disorders. Variability in NfL levels was greater among individuals with schizophrenia spectrum disorders (variance ratio [VR]=1.30; p=0.005), depressive episodes (VR=1.06; p=0.006), and anxiety disorders (VR=1.08; p=0.005). Variability in GFAP levels was increased only in anxiety disorders (VR=1.08; p=0.01). Plasma NfL levels exceeding percentile-based normative thresholds were more common among individuals with schizophrenia spectrum disorders, bipolar disorder, recurrent depressive disorder, and depressive episodes. Neurological diagnoses, cardiometabolic burden, and substance use were associated with plasma NfL and GFAP levels. Conclusions and RelevanceThis study provides population-level evidence of plasma NfL elevation in bipolar and depressive disorders and increased variability in schizophrenia spectrum, bipolar and depressive disorders, supporting its potential as a biomarker in psychiatry and informing its ongoing neurological applications. Plasma GFAP levels, in contrast, were largely unaltered across psychiatric disorders. Key PointsO_ST_ABSQuestionC_ST_ABSAre plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) levels altered in psychiatric disorders? FindingsIn this cohort study including 47,495 individuals, normative modeling revealed that plasma NfL levels were elevated in bipolar and depressive disorders, whereas plasma GFAP levels were not elevated in any psychiatric disorder. Plasma NfL levels also showed higher variability in schizophrenia spectrum, bipolar, and depressive disorders. MeaningPlasma NfL shows distinct alterations in schizophrenia spectrum and affective disorders, supporting its further investigation as a biomarker in clinical psychiatry and highlighting the need to consider psychiatric comorbidity in neurological applications.

BackgroundEarly-life adversity (ELA) is a risk factor for enduring pain in youth and is associated with alterations in brain morphology and function. However, it remains unclear whether ELA-related neurobiological changes contribute to the development of enduring pain in early adolescence. MethodsUsing data from the Adolescent Brain Cognitive Development (ABCD) Study, we examined multimodal magnetic resonance imaging (MRI) markers in children assessed at baseline (ages 9-11 years) and at 2-year follow-up (ages 11-13 years). ELA exposure was defined at baseline to maximise temporal separation between early adversity and later enduring pain. Participants with enduring pain at follow-up (n = 322) were compared to matched pain-free controls (n = 644). Structural MRI, diffusion MRI (fractional anisotropy, mean diffusivity), and resting-state functional connectivity data were analysed. Linear models tested main effects of enduring pain, ELA, and their interaction on brain metrics, controlling for relevant covariates. ResultsELA exposure was associated with smaller caudate and nucleus accumbens volumes, and reduced surface area of the left rostral middle frontal gyrus. No significant effects of enduring pain or ELA-by-enduring pain interaction were observed across grey matter, white matter, or functional connectivity measures. ConclusionsELA was associated with alterations in fronto-striatal regions in late childhood, but these changes were not linked to enduring pain in early adolescence. These findings suggest that ELA-related neurobiological alterations may represent early markers of vulnerability rather than concurrent correlates of enduring pain. Longitudinal follow-up is needed to determine whether these alterations contribute to later chronic pain risk.