NeuroImage: Clinical

Background: Storage symptoms after stroke-isolated urgency, urgency with frequency, and isolated frequency are common but traditionally attributed to a single overactive bladder mechanism via suprapontine disinhibition. However, clinical heterogeneity in symptom presentation suggests distinct underlying mechanisms. We aimed to characterize the neural substrates of three storage symptom subtypes after stroke using comprehensive lesion-symptom mapping. Methods: We prospectively evaluated 1,498 consecutive subacute stroke patients admitted for inpatient rehabilitation (1,105 men, 73.8%; median age 61 years). Storage symptoms were classified into three subtypes: isolated urgency (n=109), urgency with frequency (n=32), and isolated frequency (n=19). Multivariable logistic regression models with Bonferroni correction identified independent predictors across demographic, clinical, white matter hyperintensity (WMH), brain atrophy, and lesion location variables. Results: The three subtypes demonstrated largely distinct sets of independent predictors. The left genu of the corpus callosum (aOR=20.06, 95% CI 7.78-51.74, P<0.001) and the inferior frontal gyrus (aOR=3.48, 95% CI 1.81-6.67, P<0.001) were independently associated with isolated urgency and survived Bonferroni correction, together with a right IFG-insula synergistic effect (OR=21.46, 95% CI 10.49-43.88, P<0.001). Urgency with frequency was associated with a broad fronto-cingulate network-the IFG (aOR=11.45, 95% CI 3.10-42.33, P<0.001, surviving Bonferroni correction) and the ACC (aOR=11.53, 95% CI 2.40-55.49, P=0.002) with diffuse right-hemisphere dominance, older age and brain atrophy. Isolated frequency was associated with anterior corona radiata involvement (aOR=5.46, 95% CI 1.92-15.54, P=0.002) and male sex (aOR=10.62, 95% CI 1.36-82.98, P=0.024), though none reached the strict Bonferroni threshold. Conclusions: These findings identify three mechanistically distinct post-stroke storage symptom subtypes with separable neural substrates, lateralization profiles, and clinical determinants. The triple dissociation across subtypes supports a discrete pathway model over the traditional unitary OAB framework, providing a neuroanatomically grounded basis for subtype-stratified treatment Keywords: storage symptoms; subacute stroke; hemispheric lateralization; structural synergy; lesion-syndrome mapping

Background: Hippocampal sclerosis (HS) is a common cause of drug-resistant focal epilepsy (DRFE) and amenable to neurosurgical treatment. Detection relies on MRI but can be challenging. 7 Tesla (T) ultra-high field MRI and automated MRI post-processing tools have independently been shown to improve radiological diagnosis of HS. However, combining these approaches remains underexplored. This study evaluated whether AID-HS, a tool for HS detection developed using 3T MRI, generalises to 7T MRI data. Methods: We collated a dataset of paired 3T and 7T T1-weighted MRI from four epilepsy centres, including 23 patients with HS, 39 healthy controls, and 23 individuals with focal cortical dysplasia as disease controls. Histopathology served as the gold standard for defining HS where available (n=7), otherwise radiological findings (n=16). AID-HS was applied to images acquired at both field strengths, and sensitivity and specificity for detection and lateralisation of HS were compared. Additionally, agreement of hippocampal features across 3T and 7T was evaluated. Results: We found no evidence of a difference in performance of AID-HS between 3T and 7T. Sensitivity for detection of unilateral HS was 63% (12/19) at 3T and 68% (13/19) at 7T (McNemar's exact test p=1.0). Specificity in controls was 97% (60/62) at 3T and 100% (62/62) at 7T (p=0.5). Bilateral HS was correctly flagged in 3 of 4 cases using feature-based criteria, with high specificity in controls. Quantitative hippocampal features showed moderate to good agreement across field strengths (ICC 0.70 to 0.98), with small differences observed for volume and thickness estimates. Conclusion: AID-HS provides robust detection and lateralisation of HS across multiple 7T MRI centres, highlighting its potential to enhance lesion detection. Future work is needed to investigate whether models trained on 7T data can leverage the improved image quality for further gains in HS detection performance.

Background: The Boston Criteria v2.0 represent the gold standard for diagnosing Cerebral Amyloid Angiopathy (CAA), but their application is currently precluded in mixed small vessel disease (SVD), where deep and lobar hemorrhages coexist. The aims of this study are: (i) to determine which cerebrospinal fluid (CSF) biomarker (A{beta}42, A{beta}40, A{beta}42/40 ratio) is the best candidate to support the CAA diagnosis; (ii) to define a data-driven cut-off, and (iii) to explore if a biomarker-integrated classification significantly improves the phenotypical concordance with the suspected predominant SVD (CAA vs. arteriosclerosis). Methods: We analyzed data from a retrospective multicenter cohort of patients with suspected CAA, defined as probable CAA (Boston criteria v2.0) but allowing deep hemorrhagic lesions, and with available CSF biomarkers. We visually quantified MRI-visible SVD markers (e.g., cerebral microbleeds [CMB], cortical superficial siderosis [cSS], lacunes) and their association with MRI-visible SVD features. We employed a Gaussian Mixture Model (GMM) to identify a data-driven threshold for amyloid positivity (A+). Then, we compared the prevalence of MRI-visible manifestations of SVD between subgroups applying different frameworks, namely the current MRI-based classification (probable CAA vs. mixed SVD) and a CSF biomarker-integrated classification (A+ vs. A-). Results: We enrolled 121 patients (age: 72 [66-77] years; 60% probable CAA, 40% mixed SVD with suspected CAA). The CSF A{beta}42/40 ratio showed a bimodal distribution and consistent associations with all CAA-specific radiological features. The CSF biomarker-integrated reclassification, particularly using the GMM cut-off, significantly improved the distinction between subgroups regarding CAA- and arteriosclerosis-related MRI features (e.g., cSS presence: probable CAA vs. mixed SVD: aOR=2.84 [95%CI 1.27-6.39], p=0.011; A+ vs. A-: aOR=12.68 [95%CI 4.31-37.32], p<0.001; deep lacunes presence: probable CAA vs. mixed SVD: aOR=0.20 [95%CI 0.08-0.50], p<0.001; A+ vs. A-: aOR=0.04 [95%CI 0.01-0.11], p<0.001). Notably, patients classified as A+ never demonstrated more than four deep CMBs. Discussion: A CSF biomarker-integrated classification may improve the classification of CAA compared with the current MRI-based framework. These findings are cohort-specific and would benefit from further validation, especially with a neuropathological reference. Still, these results support a future transition toward an integrated biological-radiological framework, which may refine in vivo CAA diagnosis, particularly in mixed SVD.

Background: The primary neuroanatomical site of Huntington-s disease (HD) pathology resides in the striatum and its atrophy identifies important disease progression from HD-ISS Stage 0 to Stage 1. Immune-associated proteins may capture variation in HD that is incompletely represented by markers of neuroaxonal injury. Objectives: To determine whether cerebrospinal-fluid myeloperoxidase contributes information about striatal volume loss beyond genetic disease burden and neurofilament light. Methods: Cross-sectional data from 88 persons with HD were analyzed. Cerebrospinal-fluid myeloperoxidase and neurofilament light were measured with a nucleic acid-linked immunosandwich assay. Normalized putamen volume was derived from structural magnetic resonance imaging. Linear regression adjusted for genetic disease burden and sex. Results: Higher neurofilament light was associated with smaller normalized putamen volume (standardized {beta} = -0.322, (P=.0066)). Higher myeloperoxidase was associated with larger normalized putamen volume after adjustment for genetic disease burden, sex, and neurofilament light (standardized {beta} = 0.183, (P=.0386)). Adding myeloperoxidase increased explained variance in striatal loss. Conclusions: Cerebrospinal fluid myeloperoxidase contributed modest incremental information about striatal volume in this cross-sectional sample. Independent longitudinal studies are needed to determine its biological source, temporal behavior, and potential biomarker value. Findings advance efforts to characterize multicomponent biological markers of HD.

Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in clinical scans. In particular, many tools underperform in infant scans due to morphological and physical changes such as rapid myelination. Here, we introduce ClinSeg: a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast. We leverage existing registration and synthetic data generation tools to construct a training corpus for a 3d U-Net spanning anatomical and contrast diversity, including scans with morphological abnormalities from a pediatric hospital. Validated against manual segmentations, ClinSeg outperforms existing models in infancy while matching them in childhood and adolescence. Finally, ClinSeg enables the construction of reference brain growth trajectories in 11,699 individuals from 0-21 years of age, leading to the detection of more nuanced age-related findings in clinical groups.

Background: The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely used to define amyloid positivity. As a global summary measure, however, CL may not fully reflect the regional distribution of amyloid deposition, which can carry additional prognostic information about the rate of cognitive decline. Objective: To develop and externally validate a fixed, regional amyloid PET composite score that complements CL for predicting cognitive decline in Alzheimer's disease. Methods: The Regional Amyloid PET Score (RAPS) was derived from 82 FreeSurfer regions using machine learning with bootstrap stability selection to predict the rate of change in CDR-Sum of Boxes (CDR-SB) in 433 amyloid-positive ADNI [18F]florbetapir participants. The fixed nine-region weights were applied without retraining in a cross-tracer ADNI [18F]florbetaben subset (N = 71; largely overlapping the discovery participants) and two external validation cohorts, NACC SCAN (N = 1531; four tracers) and OASIS-3 (N = 428). Results: RAPS comprised nine regions. In ADNI, RAPS correlated more strongly with CDR-SB slope than CL and showed higher discrimination of rapid decliners (AUC 0.813 vs 0.713). Performance was directionally consistent across validation cohorts; in NACC SCAN, RAPS and CL independently predicted clinical progression. Cross-cohort meta-analysis of the three independent cohorts supported incremental discrimination beyond CL (pooled {Delta}AUC +0.066; I2 = 0%). Conclusions: RAPS, a fixed regional amyloid PET-derived score, may complement CL for prognostic stratification in Alzheimer's disease research.

Background: Bipolar disorder is associated with persistent social, cognitive, and functional impairment during euthymia, yet the neural mechanisms underlying these deficits remain unclear. Alterations to self-referential processing are a candidate mechanism, but existing electrophysiological studies rely on emotionally valenced paradigms that potentially confound self-processing with emotional biases. Methods: We analysed electroencephalography from 28 patients with bipolar disorder (type I or II) and 28 age- and sex-matched healthy controls during an emotionally neutral colour judgment task with self-related (preference) and non-self-related (similarity) conditions. Late positive potentials, temporal generalisation decoding, and frequency band decoding (theta, alpha, beta) were used to characterise the temporal dynamics and oscillatory correlates of self versus non-self processing. Results: Controls showed higher overall event-related potential amplitudes and greater self versus non-self differentiation than patients (condition by group interaction, 337 to 946 ms). Broadband temporal generalisation decoding revealed extensive cross-temporal generalisation of the self versus non-self representation in controls, spanning most of the trial, but no significant generalisation in patients. Frequency analyses showed that alpha and beta carried self versus non-self information in both groups, with broader extent in controls, and that anterior theta carried this information in patients but not controls. Exploratory correlations linked decoding measures to rumination and anxiety but not to manic symptoms. Conclusions: The neural representation distinguishing self-referential from externally guided processing was both smaller in amplitude and less temporally sustained in bipolar disorder. Reduced persistence is not detectable by conventional amplitude analyses, and may bear on the self-related and social cognitive difficulties reported in this population.

Psychosis has long been conceptualized as a disorder of disrupted hierarchical integration across distributed brain systems, yet it remains unclear whether alterations in macroscale cortical hierarchy are already present before illness onset and are associated with subsequent transition to psychosis. Using connectome gradient mapping, we characterized baseline cortical hierarchical architecture along the unimodal-to-transmodal axis in 580 participants from the NAPLS-3 cohort, including converters (CHR-C, n = 56), non-converters (CHR-NC, n = 434), and healthy controls (HC, n = 90). Group differences were assessed at regional, network, and global levels. Group comparisons revealed that CHR-C individuals, relative to the other two groups, exhibited bidirectional alterations selectively along the sensorimotor-to-association gradient, with reduced values in the visual network alongside elevated values in the default mode network, indicating greater separation between sensory and transmodal systems along the gradient. At the global level, CHR-C showed increased explained variance, range, and variation of this gradient, collectively indicating hierarchical expansion. Notably, greater explained variance of this gradient was associated with a shorter time to conversion to psychosis, while increased gradient range and variation were associated with higher positive symptom severity across CHR individuals. These findings indicate that expansion of the sensorimotor-to-association connectome hierarchy is already present before psychosis onset in individuals who subsequently convert to psychosis. This altered hierarchical organization may reflect greater decoupling between sensory and transmodal systems and may characterize neurobiological changes associated with progression from a clinical high-risk state to psychotic illness.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition defined by differences in social communication and restricted, repetitive behaviours. As diagnostic criteria have broadened, ASD is now recognised across a wider range of individuals, raising key questions about its structure: does ASD have discrete sub-types, or is it better conceptualised as a continuous, possibly multidimensional, condition? We aim to explore whether a multidimensional continuum model more accurately captures the variability within ASD. We analysed a large SPARK phenotypic dataset of medical history and diagnostic surveys (background history, SCQ, RBS-R; n=36,710 individuals). We apply and compare two traditional statistical approaches, Factor Analysis and Gaussian Mixture Models, with a modern machine learning technique, the Variational Autoencoder (VAE). VAEs reconstructed unseen test data with ~4-fold better accuracy than Factor Analysis, and ~8-fold better accuracy than Gaussian Mixture Models. We identified four stable latent factors across 100 independently trained VAEs. These four dimensions provide an individual behavioural profile that can be visualized using radar-plots, offering a compact way to compare profiles at the person level. Through further analysis, we found evidence for 3 overlapping clusters or subtypes of ASD identified within the 4D latent space. This work aims to inform new ways of modelling ASD using a VAE that will be able to discern between a continuum or a clustered output and that go beyond binary diagnosis, instead reflecting the complex range of trait profiles, with implications for personalised diagnosis and intervention.

Despite extensive knowledge of the progressive sequence of cognitive and functional deficits in Alzheimer's Disease (AD), the impact of neurodegeneration on the conscious experience of patients remains largely unexplored. Understanding how the content of consciousness, particularly perceptual awareness, changes with the progression of AD is crucial to enable meaningful person-centred care. This is especially important in severe AD when impairments in language and other cognitive domains mean people are unable to report their experiences. We investigated whether electrophysiological (EEG) and fMRI signatures of perceptual awareness described in healthy older people are present in people with mild-moderate and severe AD using two "no-report" paradigms. Firstly, a visual masking paradigm examined visual awareness negativity (VAN) and late positive (LP) electrophysiological responses and activation in visual cortex and fronto-parietal regions that are characteristically associated with conscious perception of faces; and second, a complex audio-visual (movie) task examined activation in fronto-parietal networks previously associated with perceptual awareness. In healthy older controls we found cortical responses characteristic of awareness in both EEG and fMRI modalities, with VAN and LP markers and widespread occipital, fusiform face area and fronto-parietal activation. In people with mild-moderate AD, there were significant reductions in VAN and LP markers and reduced fronto-parietal activation. In participants with severe AD, who were behaviourally minimally responsive, there was only limited evidence of presence of frontoparietal markers of perceptual awareness, however this may reflect attentional and task insensitivity in people with advanced dementia. These results demonstrate that the brain mechanisms associated with perceptual awareness become increasingly impaired with progression of AD. Specifically, involvement of frontoparietal networks is reduced in AD, which may reflect reduced higher-level awareness. This suggests AD should be considered a disorder of consciousness and should motivate further investigation into the dimensions of awareness affected by the disorder with implications for treatment and management of people with dementia.

Background: Brain age is a biomarker of brain tissue integrity associated with disability in multiple sclerosis. While new lesion formation is central to MS diagnosis and treatment monitoring, its direct relationship to brain aging has not been established. Methods: We analyzed 163 people with MS with clinical and MRI assessments at baseline and years 3, 6, and 8. Brain age was estimated using BrainAgeNeXt. Annualized brain age acceleration was modeled as a function of radiological activity using generalized estimating equations, adjusting for age, sex, disease duration, baseline T2 lesion volume, normalized brain volume (NBV), brain age difference (BAD), and disease-modifying therapy. Secondary analyses examined dose-response effects, post-activity recovery, paramagnetic rim lesion (PRL) associations, and disability associations. Results: 105 participants had at least one new T2 lesion over 8 years. Radiologically active intervals (138 of 333) were associated with +0.19 yr/yr greater brain age acceleration than stable intervals (95% CI: 0.03-0.37; p=0.022), scaling with lesion count (beta=+0.18; p=0.001) and volume. Older age, greater baseline BAD, and NBV were independently associated with reduced brain age acceleration. Brain age acceleration in individuals with new lesions normalized during subsequent stable intervals (0.41 vs -0.06 yr/yr; p=0.001). Both PRLs and non-PRL lesions were associated with greater brain age acceleration than stable intervals. Baseline BAD, but not annualized acceleration, predicted Expanded Disability Status Scale (EDSS) and Nine-Hole Peg Test (9HPT) worsening. Conclusions: New focal lesion formation is associated with a quantifiable, dose-response acceleration of brain aging in MS that normalizes once lesion activity is suppressed.

Background: As cannabis policy changes in the United States, secondhand cannabis smoke (SCS) is increasingly common, including within families. However, prevalence of exposure and clinical correlates over time in adolescents are not fully understood. Objectives: (1) To estimate the prevalence of SCS and personal cannabis use in US-based teens exposed to SCS, and (2) examine the cognitive trajectories of adolescents exposed to SCS compared to non-exposed peers. Methods: Data from the Adolescent Brain Cognitive Development (ABCD) Study was used. Participants (n=11,316 of full cohort with follow-up data; n=776 with self-reported family SCS exposure) attended yearly visits from ages 11-17, completing substance use interviews, toxicological testing, and the NIH Toolbox Cognitive battery. Youth with SCS but no personal cannabis use (n=419; 47% female) were matched on prenatal substance exposure, family substance use history, and sociodemographics to non-SCS exposed and non-cannabis-using youth with a 1:2 ratio (Controls n=838). Linear mixed-effects models assessed cognitive performance by SCS*age interactions, accounting for random effects of subject and family. Covariates included sex and alcohol, nicotine, and other substance use. Secondary models analyzed performance by cumulative waves of reported SCS exposure interacting with age. Results: Of the full cohort, 6.9% (n=776) reported exposure to SCS. Of these individuals, 46% endorsed lifetime personal cannabis use by age 17, relative to 20% of non-SCS exposed youth (OR=3.83[95%CI:3.29,4.44]). Within matched participants, SCS*age demonstrated a significant interaction on attention and inhibitory control ({beta}=-0.32, p=.028), with SCS demonstrating reduced improvement over time. More waves of exposure were also associated with worse performance over time ({beta}=-0.39, p=.057). Discussion: Almost half of those who had been exposed to SCS endorsed personal cannabis use. Cognitive findings were domain specific, similar to findings in secondhand tobacco: SCS exposed youth showed restricted improvement in attention and inhibitory control by age 17. Public health and policymakers should make efforts to curb youth SCS exposure, given the potential for risk which has not been fully explored to date.

Major depressive disorder (MDD) has been associated with accelerated structural brain aging, yet whether this reflects a pre-existing neurobiological vulnerability, a dynamic acute state effect, or an accumulating biological residual remains unresolved. Across two longitudinal cohorts (N=3220), including a unique sample of 78 initially healthy individuals who transitioned into their first depressive episode during the study course, we systematically tested all three hypotheses. Patients with diagnosed MDD showed elevated MRI-derived brain age relative to healthy controls (1.4 and 2.5 years across cohorts). For the vulnerability hypothesis, individuals scanned prior to their first episode showed no baseline elevation, despite already demonstrating subclinical elevations in self-reported symptom severity, indicating that advanced brain age does not precede illness onset. For the state hypothesis, we found no acceleration of brain aging following the first depressive episode, and longitudinal brain age trajectories were independent of acute clinical symptom severity. Finally, neither episode duration nor recurrence scaled with brain age. Accelerated brain aging in depression is therefore neither an antecedent vulnerability nor an acute state marker of the first episode, but rather a stable biological feature of a long term illness course.

Background: Cognitive impairment is the leading cause of disability in schizophrenia with limited treatments. A major barrier to treatment development is the absence of reproducible, mechanistically grounded neural targets. Cross-sectional studies have identified dorsomedial prefrontal cortex (DMPFC)-somatomotor connectivity as a neural marker of cognitive performance on the Auditory Continuous performance task (ACPT), a measure of attention. To test the stability of this marker, we tested the relationship between DMPFC-somatomotor connectivity and ACPT performance in a longitudinal psychosis sample. Methods: Individuals with early psychosis (n=251) and matched controls (n=90) were enrolled and underwent resting-state neuroimaging and neurocognitive assessment. A subset completed longitudinal assessments over 2-4 years. We calculated DMPFC-somatomotor resting-state functional connectivity using a previously identified DMPFC region and a seed in the somatomotor cortex. We performed linear mixed effects models to predict ACPT performance based on connectivity, time, psychosis type, and their interaction. Results: In the psychosis sample, time (p=.0037) and affective psychosis diagnosis (p<.0001) predicted better ACPT performance. In a model predicting ACPT performance, we observed a significant interaction effect of DMPFC-somatomotor connectivity*psychosis subtype (p=.0079) such that DMPFC-somatomotor connectivity predicted ACPT performance only in individuals with non-affective psychosis (p=.0051). We then tested the specificity of this connectivity-cognitive performance relationship. In a model predicting DMPFC-somatomotor connectivity, only ACPT performance (p=.017), but not fluid cognition, was a significant predictor. Conclusions: DMPFC-somatomotor connectivity is longitudinally associated with cognitive performance in early psychosis. This relationship is strongest in nonaffective psychosis, suggesting a novel, reliable target for intervention for cognitive deficits in early psychosis.

Abstract: Objective: Post-traumatic epilepsy (PTE) is a common sequela of traumatic brain injury (TBI). Research indicates that individuals with PTE tend to experience greater cognitive difficulties compared to those with TBI alone. However, it is plausible that a distinct cognitive profile exists that distinguishes between TBI cases with and without PTE. We aimed to identify longitudinal changes in cognitive measures among TBI patients to better assess the changes associated with developing PTE. Setting: Outpatient. Participants: Prospective subjects who had suffered TBI within 6 months post-injury (TBI-6M, n=32), retrospective subjects with pre-existing PTE diagnoses (PTE, n=20), and healthy control subjects (HC, n=41). Design: We examined cognitive performance for TBI patients within 6 months post-injury, then again within 12 months (TBI-12M, n=26), and within 18-months (TBI-18M, n=25), and compared this with cognitive performance among HC and PTE. Main Measures: Cognitive tests administered yielded 15 test components for analysis. We utilized linear mixed effects modeling to examine cohort-level differences cognitive function. Results: 11/15 tests showed a significant performance deficit in the PTE subjects compared to HC. TBI-6M was not significantly different from the PTE subjects; with time, 9/15 tests showed some degree of recovery in TBI subjects. Tests for information processing speed/working memory and executive function showed strong recovery (TBI-6M vs. TBI-18M, SDMT written: p<0.0001, SDMT oral and COWAT: p<0.001). Tests for visual attention/working memory also showed a smaller but significant recovery (TBI-18M vs. PTE, p<0.05). By contrast, tests for verbal memory [HVLT-R Delayed Recall] showed chronic impairment in TBI (TBI-18M vs HC, p<0.0001). TBI subjects generally trend towards recovery in cognitive performance post-TBI. Conclusions: Information processing speed/working memory are strong indicators for TBI recovery, while auditory learning/memory shows chronic impairment. The stagnation of recovery in cognitive domains typically characterized by robust recovery may correlate with an elevated risk of developing PTE.

Mood fluctuations in major depressive disorder are difficult to anticipate. The biological neural rhythms that organize mood dynamics over days to weeks remain unknown. In individuals implanted with a chronic neural sensing and stimulation device for treatment-resistant depression, we collected years-long intracranial neural recordings alongside daily mood ratings. Both mood and limbic neural activity fluctuated cyclically with multiday (multidien) periodicities of 2-34 days. An individual's daily phase position within mood cycles tracked depression severity, distinguishing whether symptoms were rising, peaking, or resolving. Neural rhythms led mood cycles and forecast an individual's mood trajectory up to 30 days in advance, outperforming models based on raw neural activity. Electrical stimulation reshaped these rhythms, shifting individuals away from the peak-depression phase of their multidien cycle. Our results identify multidien rhythms as an organizing principle of mood in depression and a forecastable, modifiable target for chronotherapeutic neuromodulation.

Isolated rapid eye movement sleep behavior disorder (iRBD) is characterized by nigro-striatal deficits, comprising dopaminergic denervation of the striatum and loss of dopaminergic cells in the substantia nigra (SN), that may herald phenoconversion to clinically manifest synucleinopathy. While phenoconversion has repeatedly been shown to relate to pre-synaptic dopaminergic deficits in the striatum, potential involvement of loss of dopaminergic cells in the SN remain unclear. In addition, phenoconversion may independently relate to noradrenergic deficits, stemming from cell loss in the locus coeruleus/subcoeruleus (LC/LsC) complex. Fifty-six iRBD patients were included and clinically followed over an 11-years as part of the ICEBERG study. Putamen dopamine denervation was quantified using 123I-FP-CIT single-photon emission computed tomography. Cell loss in the SN and LC/LsC was quantified using neuromelanin-sensitive magnetic resonance imaging (MRI). SN cell loss was additionally characterized as free water, derived from diffusion-weighted MRI. The primary outcome was time to phenoconversion. Cox proportional hazards regression was used to investigate relationships between phenoconversion risk and imaging predictors, estimated as hazard ratios (HRs). Out of 56 patients, 24 (41%) converted to a clinically manifest synucleinopathy [PD=14 (58%), DLB=8 (33%), MSA=2 (8%)] over a maximum period of 11 years. We replicated the well-established finding that reduced putamen DaT confers an increased phenoconversion risk [HR (95%CI)=3.1 (1.7-5.5), P<0.001]. We extend on this by showing a similar relationship for SN neuromelanin [HR (95%CI)=2.5 [1.3-4.6], P=0.004], SN free water [HR (95%CI)=1.54 (1.06-2.24), P=0.025], and LC/LsC neuromelanin [HR (95%CI)=2.1 (1.2-3.7), P=0.011], demonstrating involvement of the broader nigro-striatal dopaminergic system along with potential involvement of noradrenergic neurotransmission. When adjusting for putamen DaT, the relationship between phenoconversion risk and SN neuromelanin was attenuated [P=0.16], suggesting partial overlap between the metrics. In contrast, when modelled together, SN neuromelanin [HR (95%CI)=2.8 (1.4-5.6), P=0.003] and LC/LsC neuromelanin [HR (95%CI)=2.3 (1.1-4.8), P=0.037] contributed to phenoconversion risk independently of each other, indicating a differential contribution of dopaminergic and noradrenergic neurotransmitter deficits to iRBD phenoconversion. We demonstrate that phenoconversion in iRBD relates similarly to dopaminergic denervation of the putamen and cell loss in the SN. This opens possibilities for using NM-MRI, which can simultaneously capture dopaminergic and noradrenergic deficits, as an alternative to nuclear imaging techniques when estimating phenoconversion risk in iRBD.

Background Fluid biomarkers enable the demonstration of the biological effects of novel therapies in Alzheimers disease (AD). However, longitudinal biomarker data are sparse and sample size calculations for fluid biomarkers are often lacking. Here, we provided longitudinal CSF and plasma AD biomarkers measured in samples collected in a placebo arm in a 1.5-year phase 2b trial, allowing us to study natural trajectories, required sample sizes and heterogeneity in early AD clinical trials. Methods We studied individuals from the placebo group (MCI due to AD (n=65) and AD dementia (n=41)) of the T-817MA trial (NCT04191486) with positive CSF AD biomarkers (mean age=69(7) years, Female=63%). Longitudinal biomarker changes in CSF (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, tTau, YKL40, NRGN, ABL1, CHIT1, CLEC5A, ITGB2, MMP10, SDC4, SPON2, THBD) and plasma biomarkers (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, GFAP) were analyzed with linear mixed-effect models. Required sample size estimates for predefined treatment effects were generated. Lastly, we investigated the influence of between person variability in biomarker change by simulating a randomized clinical trial (1:1) 10000 times, and assessed the group differences at 1.5 years. Findings Fourteen biomarkers changed over time, with the largest annual changes observed for plasma pTau217 (+9.8%), CSF MMP10 (+7.1%), and CSF NFL (+6.9%), and CSF A{beta}40 by (-4.0%), CSF pTau217 (-3.0%), and CSF NRGN (-2.5%). To show a 30% change, similar to biomarker effects of approved AD drugs, almost all markers required less than 45 patients per trial arm. To reach normalized levels, established CSF markers required lower sample sizes than plasma markers. The effects of heterogeneity over time were approximately twice as large in plasma compared to CSF. Interpretation These findings offer insights into the biomarker trajectories and power in early AD, supporting more informed endpoint selection and forming a frame of reference for the interpretation of treatment effects in clinical trials.

Background Mirror therapy (MT)-specifically paradigms using mirror visual feedback (MVF)-is widely used in neurorehabilitation; however, mechanistic implementations vary substantially in movement content, rhythmicity and attentional demands. This protocol describes an acute mechanistic, within-participant fNIRS screening study designed to compare three prespecified upper-limb mirror-therapy task paradigms and to quantify associated subjective experience after each condition in healthy adults during a single visit. Methods and analysis This is a single-centre, within-participant, randomised crossover study conducted at Wuhan Wuchang Hospital (Wuhan, China). Healthy adults aged 18-35 years will complete three task conditions once each in a counterbalanced order using a 3*3 Latin-square scheme: UMT1 (task-oriented rhythmic functional movement), UMT2 (open-ended free movement with auditory control), and UMT3 (non-functional rhythmic movement). fNIRS will be acquired using the NirSmart-6000A system during a standardised block design. The primary outcome is ROI-level HbO activation quantified as GLM-derived {beta} estimates within the prespecified primary ROIs (bilateral SM1/M1 and bilateral PMC). Secondary outcomes include ROI-level windowed {Delta}HbO (5-20 s post-onset relative to the immediately preceding rest; descriptive only), ROI-level {Delta}HbR, and post-condition subjective ratings (illusion, immersion, confusion and fatigue; 1-7 Likert). Condition effects will be analysed using linear mixed-effects models with fixed effects for condition and period and prespecified multiplicity-adjusted pairwise contrasts. Ethics and dissemination Ethics approval was obtained from the Ethics Committee of Wuchang Hospital Affiliated to Wuhan University of Science and Technology (Approval No.: 2025-112-01; approved on 2025-08-21). The study is expected to be minimal risk. Findings will be disseminated through publication of this protocol manuscript and subsequent results manuscripts and conference presentations. Trial registration number Chinese Clinical Trial Registry (ChiCTR2600116634). This study is conducted as a prespecified mechanistic sub-study under the overarching registered project.

We propose two new tests to detect drug effects (DE) in trials of one to very few patients followed during two periods (before and after initiation of a treatment). Both methods use longitudinal natural history data to inform the estimation of each patient's DE. The first method uses a non linear mixed effect model (NLMEM) reflecting an expected natural history with a hypothetical drug effect, to estimate the Conditional Distribution of the Drug Effect (CDDE). The second method trains a Pareto Depth Analysis (PDA) algorithm, a machine learning based approach based on outlier detection, that we implement using data simulated under the NLMEM. We evaluated the two tests with a simulation study. We used data from the PROSPAX study in Autosomal Recessive Cerebellar Ataxias (ARCAs, to derive a NLMEM for the Scale for the Assessment and Rating of Ataxia score. The CDDE method provided controlled type I error and, in some scenarios, adequate corrected power, though sensitivity analyses showed vulnerability to misspecification. The PDA method demonstrated lower statistical power except with high score precision. These results highlight different strategies for quantifying treatment effects in ultra rare, patient' specific trials. They can inform methodological design for future ARCA precision therapies.