Human Brain Mapping

Harmonisation is widely used to mitigate site- and scanner-related batch variability in multisite neuroimaging studies and is particularly critical in longitudinal clinical trials, where detection of subtle biological or treatment-related changes depends on reliable measurement across scanners and timepoints. However, the effectiveness of harmonisation in small, heterogeneous clinical datasets remains insufficiently understood, particularly in relation to subject-level variability and consistency across acquisition settings, and its impact on both removal of technical variability and preservation of biological variation in pooled multisite analyses. We systematically evaluated a range of image-based and statistical harmonisation methods using a clinically realistic multisite, multiscanner structural T1-weighted (T1w) MRI test-retest dataset comprising three controlled acquisition scenarios: repeatability, intra-scanner reproducibility and inter-scanner reproducibility. Methods were applied under different batch specifications (site, scanner, or both) and performance was assessed within each scenario and in pooled data using a multi-metric framework capturing both technical and biological variability in volumetric imaging-derived phenotypes (IDPs) relevant to aging and dementia research. Across IDPs, before harmonisation variability was lowest in the repeatability scenario (median variability=0.6 to 2.7%, rank consistency {rho} [≥]0.9), with modest increases under intra-scanner reproducibility (0.5 to 3.2%, {rho}=0.5 to 1.0) and substantially greater variability under inter-scanner reproducibility conditions (1.7 to 19.2%, {rho} =-0.1 to 0.9). These results offer important information to consider for multisite study design, including sample size calculation in clinical trials. Harmonisation performance was strongly context dependent, with clearer benefits emerged in inter-scanner scenarios where both variability reduction and improvements in subject-level consistency were observed. In pooled data, approaches that explicitly modelled site as batch and accounted for repeated-measure structure showed greater consistency across IDPs in batch effect mitigation and more accurately reflected underlying biological variation. Our evaluation metrics enabled disentangling the removal of global batch effect while highlighting residual variability at the phenotype-specific or multivariate levels. These findings demonstrate that harmonisation cannot be treated as a one-size-fits-all solution and must be interpreted relative to the acquisition context, dataset structure, and downstream analytic goals. Multi-metric evaluation under realistic clinical constraints is essential to support reliable and translatable neuroimaging inference by ensuring appropriate correction of batch effects while preserving longitudinal biological signals and sensitivity to clinically meaningful change in multisite studies.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity--in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI--were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

Multi-site diffusion MRI studies face scanner-induced variability that can obscure biological signal. Harmonization methods such as ComBat have been developed to address this, but have been evaluated primarily on diffusion scalar metrics. Whether scanner reproducibility differs across fundamentally distinct tract-derived representations has not been systematically compared. Here, we compared the reproducibility of three metric families (diffusion, shape, and connectivity) across 36 association tracts using the MASiVar dataset (5 subjects, 4 scanners, 27 sessions). We assessed intraclass correlation coefficients (ICC) and multivariate subject discrimination at baseline, under dimensionality reduction, and after ComBat harmonization. At baseline, shape metrics showed the highest reproducibility (median ICC 0.69), followed by connectivity (0.49) and diffusion (0.34). Shape and connectivity achieved comparable subject discrimination (both 1.75), significantly exceeding diffusion (1.23). ComBat harmonization improved all families but harmonized diffusion (0.58) remained below unharmonized shape (0.69), indicating that metric family selection remains consequential even after harmonization. Under low-dimensional representation, connectivity showed the largest gains (ICC 0.86, subject discrimination 3.0), exceeding other families at any dimensionality. Analysis of principal component loadings identified a small number of cortical regions per tract (median 6) that capture 95% of the reproducible connectivity signal, providing a per-tract reference for selecting the most informative regions in future multi-site studies. These findings indicate that the choice of which tract-derived metrics to analyze in multi-site studies deserves at least as much consideration as how to harmonize them.

ObjectiveSocial difficulties are transdiagnostic in childhood, but their heterogeneity is poorly characterised and rarely treated as a primary neurodevelopmental phenotype. This matters because childhood and adolescence are sensitive periods for peer relationships and brain development. We used data-driven modelling and non-linear mapping to derive social profiles and test their clinical, cognitive, and neural correlates. MethodsParticipants were 992 children aged 5-18 years from CALM (Mage = 9.6). Social items from the SDQ, CCC-2, and Conners-3 were modelled using a regularised partial correlation network to derive core social dimensions. A self-organising map captured graded social profiles. Simulated archetypes, SVM-based island identification, and permutation testing defined profile regions and centroid-distance scores. Profiles were related to referral, diagnosis, cognition, BRIEF indices, and T1-derived MIND network structure in an MRI subsample (n = 431). ResultsWe identified four profiles: social engagement, friendship difficulties, social withdrawal, and peer victimisation. Profile expression tracked variation in referral and diagnostic pathways. Social withdrawal showed the clearest disadvantage across cognitive domains, whereas social engagement was associated with fewer executive function difficulties across BRIEF indices. MIND strength components covaried with profile expression (a significant PLS latent variable, p = 0.02), with covariance strongest for social withdrawal and peer victimisation. ConclusionsChildhood social functioning organises graded signatures that relate to clinically relevant pathways, cognitive and executive outcomes, and brain structure. Profiling social signatures provides a scalable framework for identifying social need beyond diagnostic categories, motivating studies to test directionality and improve developmental outcomes.

Brain network analysis has emerged as a critical framework for understanding the complex organization and function of the human brain, underpinning insights into cognition, behavior, and neuropsychiatric conditions. Central to this approach is the parcellation of the brain into discrete regions, which simplifies high-dimensional connectome data and facilitates the investigation of network architectures. However, the proliferation of brain parcellation schemes introduces significant challenges: different parcellations often yield varying network sizes and measures, complicating cross-study comparisons and the reproducibility of findings. Moreover, most connectome construction pipelines are rigid, typically outputting connectivity matrices from only one or a few parcellation schemes, which limits flexibility. In this paper, we address these issues by introducing BridgeBP, a novel toolbox designed to bridge brain parcellations by leveraging continuous brain connectivity concepts. BridgeBP transforms structural connectivity matrices derived from one parcellation scheme into matrices corresponding to more than 40 alternative schemes, standardizing analyses and enhancing the robustness of network studies. Through extensive evaluations, we demonstrate that BridgeBP enables consistent network comparisons across diverse parcellation frameworks, paving the way for more reproducible and generalizable insights in brain connectome research.

Electroencephalography (EEG) preprocessing is a critical yet time-consuming step that often relies on expert-driven, semi-automatic pipelines, limiting scalability and reproducibility across large datasets. In this work, we present sEEGnal, a fully automated and modular pipeline for EEG preprocessing designed to produce outputs comparable to expert-driven analyses while ensuring consistency and computational efficiency. The pipeline integrates three main modules: data standardization following the EEG extension of the Brain Imaging Data Structure (BIDS), bad channel detection, and artifact identification, combining physiologically grounded criteria with independent component analysis and ICLabel-based classification. Performance was evaluated against manual preprocessing performed by EEG experts at two complementary levels: preprocessing metadata (bad channels, artifact duration, and rejected components) and EEG-derived measures. In addition, test-retest analyses were conducted to assess the stability of the pipeline across repeated recordings. Results show that sEEGnal achieves performance comparable to expert-driven preprocessing while preserving key neurophysiological features. Furthermore, the pipeline demonstrates reduced variability and increased consistency compared to human experts. These findings support sEEGnal as a robust and scalable solution for automated EEG preprocessing in both research and large-scale applications. HighlightsFully automated and modular EEG preprocessing pipeline. Benchmarked against expert-driven preprocessing. Comparable performance in metadata and EEG-derived measures. Demonstrates stable performance in test-retest recordings. BIDS-based framework for reproducible EEG data handling.

INTRODUCTIONAlzheimers disease (AD) manifests a specific spatial progression pattern, but its propagation mechanisms remain unclear. METHODSWe employed nine brain connectomes spanning multiple biological levels to investigate the mechanisms underlying cortical atrophy propagation in AD. Individual gray matter atrophy maps were quantified using normative modeling and were then mapped onto the connectomes by assessing the relationship between regional atrophy and the atrophy of neighboring regions defined by each connectome. RESULTSCross-sectionally, node-neighbor relationship was weak in the preclinical stage, suggesting limited influence of connectome architecture. Longitudinally, atrophy became progressively more aligned with the neurotransmitter receptor similarity connectome in individuals with MCI converting to AD dementia and dementia patients. DISCUSSIONOur findings described a stage-dependent shift in cortical atrophy propagation, with neurotransmitter receptor similarity playing an increasing role as AD progresses.

Multimodal machine learning leverages complementary information from diverse data sources and has shown strong promise in medical imaging, where multimodal data is critical for clinical decision making. In glioma grading, integrating MRI modalities with clinical data can improve diagnostic accuracy, yet systematic comparisons of fusion strategies remain limited. This study evaluates early, intermediate, and late fusion approaches, addressing the question: How does the inclusion of clinical data alongside MRI modalities influence grading performance? To assess modality contributions, we design adaptable fusion layers and employ interpretability techniques, including attention-based analysis. Our results show that incorporating clinical data consistently outperforms unimodal and MRI-only baselines, with intermediate fusion yielding the most reliable gains. Beyond accuracy, the framework reveals how MRI and clinical features jointly shape predictions, underscoring the importance of both fusion design and interpretability for clinical adoption.

Regular cannabis use has been associated with alterations in reward-related neural processes, yet findings remain inconsistent and the relationship between neural activity and behavioural performance is not fully understood. The present study aimed to characterise neural and behavioural correlates of reward processing in regular cannabis users (CU) compared with matched non-users (NU) using the Monetary Incentive Delay Task (MIDT). Firstly, we assessed behavioural performance through reaction times, accuracy and monetary earnings to determine whether potential neural alterations were reflected in task performance. Secondly, focusing on reward-related brain regions, we examined group differences in BOLD functional MRI activity during anticipation and outcome phases separately for monetary win and loss conditions. Finally, we explored the association between behavioural performance and neural activation. Our findings indicate that regular cannabis use is associated with altered engagement of key nodes within the mesocorticolimbic circuit during both anticipatory and outcome phases of reward processing, accompanied by impaired behavioural performance. Particularly, compared with NU, CU showed (I) lower striatal activity during anticipation of monetary win and higher ventral striatum and frontal pole activity during anticipation of monetary loss; (II) greater VTA activation during outcome of successful monetary win and loss avoidance and lower frontal pole activity during outcome of unsuccessful loss avoidance; (III) impaired behavioural performance, reflected in lower monetary rewards and a trend towards slower reaction times and reduced accuracy; (IV) disrupted brain-behaviour coupling. Results from this study may help inform future research on the neurobiological mechanisms underlying changes in reward function and the resultant behavioural consequences of cannabis use.

Salient sensory stimuli are known to evoke neural activations across distributed brain regions. However, the temporal dynamics of these responses over sub-second timescales remain poorly understood, in part due to limitations in the temporal resolution of non-invasive neuroimaging methods. We examined the spatiotemporal dynamics of neural activations evoked by salient sensory stimuli (rare sounds) using 1,194 widely distributed intracranial electrodes in 5 neurosurgical patients. Salient stimuli preferentially activated 263 of 1,194 electrodes (22%), with responses segregating into two largely distinct spatiotemporal patterns: (1) phasic activation in sensorimotor regions, and (2) sustained activation within the salience network. Cross-correlation analysis revealed that phasic sensorimotor activation preceded sustained salience network activation on a trial-by-trial basis. These findings support an updated view of salience processing in the human brain, revealing that salient stimuli evoke two sequential stages of neural activation--phasic sensorimotor responses followed by sustained salience network activity--rather than simultaneous widespread activation.

Early behavioral and temperamental differences are important indicators of later socioemotional development and psychopathology risk, yet their neural bases near birth remain incompletely understood. Using resting-state fMRI data from the Developing Human Connectome Project, we examined whether neonatal functional connectivity predicts 18-month behavioral and temperament outcomes in 397 infants (277 term-born, 120 preterm-born). Outcomes were assessed using the Child Behavior Checklist (CBCL) and the Early Childhood Behavior Questionnaire (ECBQ). We applied a stability-driven, ROI-constrained connectome-based predictive modeling framework to identify robust whole-brain connectivity features associated with later externalizing, internalizing, surgency, negative affect, and effortful control. Significant predictive models were observed for multiple outcomes across the whole cohort as well as within term-born and preterm-born groups, with clear differences in predictive architecture between cohorts. Across analyses, prefrontal and temporoparietal systems were repeatedly implicated, alongside medial temporal, fusiform, parahippocampal, and orbitofrontal-related regions. These findings indicate that large-scale neonatal functional organization is meaningfully related to later socioemotional and behavioral variation, and that preterm birth is associated with partly distinct predictive connectivity patterns.

Childhood obesity remains a pressing global health challenge, yet the impact of dynamic adiposity changes during active developmental window retains poorly understood. Leveraging longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (N=8519 at baseline; N=1873 at 4-year follow-up), our study reveals distinct neurodevelopmental implications of central fat dynamics during adolescence. At baseline, central fat indices (body roundness index, BRI / waist-to-height ratio, WHtR) outperformed BMI in predicting cognitive deficits, showing robust associations with impaired inhibitory control and episodic memory. The prediction effect was partially mediated by cortical changes in prefrontal and temporal regions. Longitudinally, the rate of fat accumulation ({Delta}) emerged as a critical predictor: faster adiposity accrual predicted attenuated cortical thinning (i.e., slower development) in parietal lobes and poorer executive function at follow-up, while baseline adiposity showed no significant effects on the follow-up brain morphology or cognitive development. Notably, subgroup analyses uncovered that obese adolescents with central fat reduction exhibited accelerated cortical thinning in posterior cingulate (change difference p=0.006-0.029) alongside rapid improvement in inhibitory control (Flanker slope difference p<0.05), whereas those with persistent adiposity showed delayed thinning in the postcentral gyrus. The study reveals that central fat (BRI/WHtR) is closely linked to neurocognitive risks, and longitudinal fat accumulation?rather than baseline adiposity?drives cortical alteration. Notably, fat reduction activated adaptive neural change in obese adolescents, underscoring the importance of weigh regulation during neurodevelopment.

Accurate segmentation of nasal and paranasal sinus structures from CT scans is critical for surgical planning and treatment evaluation in rhinology. However, the complex anatomical topology and thin-wall boundaries of these structures pose significant challenges for automated segmentation methods. We propose AFS-DSN (Adaptive Frequency-Spatial Dual-Stream Network), a novel deep learning architecture that integrates multi-scale wavelet decomposition with spatial feature learning for binary segmentation of the nasal cavity complex. Our method employs a dual-stream encoder with frequency branch utilizing three wavelet scales (db1, db2, db4) to capture 24 frequency sub-bands, enabling enhanced boundary detection in anatomically challenging regions. Cross-domain attention and adaptive routing mechanisms dynamically fuse spatial and frequency features based on local tissue characteristics. We formulate the task as binary segmentation where all five anatomical structures (maxillary sinus, sphenoid sinus, ethmoid sinus, frontal sinus, and nasal cavity) are treated as a unified foreground region against the background, prioritizing clinical boundary detection over individual structure differentiation. Evaluated on the NasalSeg dataset (130 CT volumes) with a 70/15/15 train/validation/test split, AFS-DSN achieves 94.34% {+/-} 2.30% overall Dice coefficient with statistically significant improvements in thin-wall regions (91.34% vs. 90.57% baseline, p=0.004) and statistically significant improvement in Surface Dice at 1mm tolerance (0.874 vs. 0.868 baseline, p=0.010), demonstrating enhanced boundary precision while maintaining sub-second inference time, making the method suitable for surgical planning applications where sub-millimeter accuracy is clinically relevant. To address concerns regarding model complexity, we further introduce AFS-DSN-Lite, a parameter-efficient variant (27.41M parameters) that achieves comparable performance (94.37% Dice) through depthwise separable convolutions, and validate robustness via 3-fold cross-validation (mean Dice: 94.59% {+/-} 0.31%).

Post-stroke cognitive recovery is difficult to predict using focal lesion characteristics alone. The brain's capacity to maintain cognitive function depends also on structural integrity of the whole brain. One way to measure brain health is through the severity of cerebral small vessel disease (CSVD) markers, which reflect aging-related pathologies that erode structural integrity. Here, we propose a composite measure of CSVD (cCSVD) integrating three independently validated biomarkers automatically quantified using T1-weighted MRIs: white matter hyperintensity volume (WMH; representing vascular injury), perivascular space count (PVS; putative glymphatic clearance), and brain-predicted age difference (brain-PAD; structural atrophy). We hypothesize that cCSVD, which captures the shared variance across these CSVD biomarkers, will be a robust indicator of whole-brain structural integrity and predict cognitive changes 3 months after stroke. We analyzed 65 early subacute stroke survivors with assessments within 21 days (baseline) and at 90 days (follow-up) post-stroke. WMH volume, PVS count, and brain-PAD were quantified from baseline T1-weighted MRIs, and then residualized for age, sex, days since stroke, and intracranial volume. Principal component analysis (PCA) of the residualized biomarkers was used to derive cCSVD. Beta regression with stability selection using LASSO was used to model three outcomes: baseline Montreal Cognitive Assessment (MoCA) scores, follow-up MoCA scores, and longitudinal change (follow-up score adjusted for baseline score). Logistic regression was used to test if baseline cCSVD predicted improvement in those with baseline cognitive impairment (MoCA < 26). The PCA revealed that the first principal component (PC1) explained 43.1% of the total variance among WMH volume, PVS count, and brain-PAD. The three biomarkers contributed nearly equally to PC1, which was subsequently used as the baseline cCSVD score. Lower baseline cCSVD was significantly associated with better MoCA scores at follow-up ({beta} = -0.19, p = 0.009), even after adjusting for baseline MoCA ({beta} = -0.12, p = 0.042), and, importantly, outperformed all individual biomarkers. Furthermore, lower cCSVD at baseline significantly increased the likelihood of improving to cognitively unimpaired status at three months (OR = 0.34, p = 0.036), independent of age and education. The composite CSVD captures the additive impact of vascular injury, glymphatic dysfunction, and structural atrophy on recovery in a way that individual measures do not. cCSVD accounts for shared variance across these domains, reflecting a patient's latent capacity for cognitive recovery, where relative integrity in one CSVD domain may mitigate effects of another. This automated, T1-based framework offers a scalable tool for predicting post-stroke recovery.

ObjectivePathological beta-band oscillations (13 to 30 Hz) in the subthalamic nucleus (STN) are a hallmark of Parkinsons disease and a primary target for deep brain stimulation therapy, yet the specific pattern of synaptic reorganization that drives their emergence remains incompletely understood. We developed a GPU-accelerated computational framework to systematically investigate combinations of synaptic changes across basal ganglia pathways that produce Parkinsonian beta oscillations while satisfying literature-based electrophysiology constraints. ApproachWe implemented a biophysically detailed spiking network model of the STN, external globus pallidus (GPe), and internal globus pallidus (GPi) in JAX (a high-performance numerical computing Python library), achieving a 490-fold speedup over conventional CPU-based simulation. Using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) we optimized 10 network parameters across two stages: first establishing a healthy baseline matching primate electrophysiology data, then searching within biologically motivated bounds for synaptic modifications that reproduce Parkinsonian firing rates and beta power. Fixed in-degree connectivity ensured optimized parameters produced scale-invariant dynamics from 450 to 45000 neurons. All simulations ran on a single cloud GPU instance at 84 cents per hour. Main ResultsThe optimizer converged on a coordinated pattern of synaptic reorganization dominated by asymmetric changes within the STN-GPe reciprocal loop: STN to GPe excitation increased 2.21-fold while GPe to STN inhibition collapsed to 0.11-fold of its healthy value. STN to GPi and GPe to GPi pathways changed minimally (1.06-fold and 1.45-fold respectively). This configuration transformed asynchronous firing (beta: 0.4 percent of spectral power) into synchronized bursting with prominent beta oscillations (49.4 percent), with firing rate changes matching experimental observations. Network dynamics were invariant across a 100-fold range of network sizes (firing rate deviation less than 2.4 Hz; all metrics p less than 0.001 across 10 random seeds at 45000 neurons). We implemented a simplified deep brain stimulation model for validation purposes, which achieved complete beta suppression (49.4 percent to 0.0 percent) and restored GPi output to healthy levels. SignificanceThese results suggest that pathological beta oscillations emerge from a specific pattern of synaptic reorganization, namely the reduction of GPe inhibitory feedback to STN. The GPU-accelerated optimization framework, running on commodity cloud infrastructure, demonstrates an accessible platform for parameter exploration in neural circuit models and a foundation for generating synthetic training data for adaptive deep brain stimulation algorithms.

Cognitive reappraisal, the deliberate reinterpretation of emotional events, is widely considered an effective emotion regulation strategy, and modulation of the late positive potential (LPP) during negative affect reduction has become the primary electrophysiological evidence for volitional emotional control. Experimental instructions, however, impose dual-task demands that free viewing does not, confounding reappraisal with cognitive load. By including instructions to increase emotional responses to pictures ("enhance") as well as instructions to decrease ("suppress"), different predictions are generated. If the LPP reflects regulation, then, compared to free viewing, suppress instructions should decrease LPP amplitude, and enhance instructions should increase LPP amplitude. If modulation instead reflects cognitive load, both instructions should reduce the LPP, as both impose an additional cognitive task. In a sample of 107 participants, evaluative ratings confirmed that regulation instructions modulated reported emotional intensity in the expected directions (Enhance > View > Suppress), but that both enhance and suppress instructions reduced LPP amplitude compared to free viewing, with Bayesian model comparisons providing strong evidence against direction-specific regulation and in favor of cognitive load. Whole-scalp multivariate pattern analysis confirmed that no instruction-related neural signal exists at any scalp location or latency within the first second after stimulus onset. These data indicate that LPP modulation following both instruction types reflects dual-task cognitive load rather than volitional emotional control. Significance StatementCognitive reappraisal is considered the gold standard of emotion regulation, and reduced late positive potential (LPP) amplitude during negative emotion suppression is the primary neural evidence that humans can voluntarily control emotional responses. The current data are inconsistent with this regulatory account and instead support a cognitive load interpretation. Whether instructed to enhance or suppress emotional responses, LPP amplitude was reduced in both conditions relative to free viewing, consistent with attentional resource competition rather than directional regulatory control. The same participants reported successfully regulating emotional experience in opposite directions, producing a clear dissociation between neural and behavioral measures. These findings challenge a basic tenet of emotional regulation and raise questions concerning LPP modulation as a biomarker of regulatory capacity.

Parkinson's Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research.

Machine learning models have seen a lot of success in medical image segmentation domain. However, one of the challenges that they face are confounders or shortcuts: spurious correlations or biases in the training data that affect the resulting models. One example of such confounders for surgical machine learning is the setup of surgical equipment, including tools and lighting. Using the task of identification of safe and dangerous zones of dissection in laparoscopic cholecystectomy images and videos as a use-case, we inspect two equipment-induced biases: the presence of surgical tools in the field of view and the position of lighting. We propose methods for evaluating the severity of these biases and augmentation-based methods for mitigating them. We show that our tool bias mitigations improve the models' consistency under tool movements by 9 percentage points in the most inconsistent cases, and by 4 percentage points on average. Our lighting bias mitigations help reduce fraction of true dangerous zone pixels that may be predicted as safe under light changes from 5% to 1.5%, without compromising segmentation quality.

1.Quantitative neuropathology has advanced through whole-slide imaging and digital histology platforms. Yet, these measurements rarely align with neuroimaging coordinate frameworks that may be useful for spatial modeling and other applications. QNPtoVox, short for quantitative neuropathology to voxels, is a reproducible, modular pipeline that transforms quantitative metrics generated by digital pathology software (HALO) into voxel-based maps registered to a standard common coordinate (MNI) template. The workflow integrates digital histopathology, gross tissue photography, ex-vivo MRI, and nonlinear registration to generate spatially standardized 3D pathology representations. This Methods article provides a complete procedural description, including required materials, step-wise instructions, operator-dependent checkpoints, expected outputs, reproducibility evaluation, and troubleshooting. QNPtoVox enables voxel-level integration of neuropathology with neuroimaging tools, unlocking existing histopathology datasets for computational modeling and cross-cohort harmonization.

Hidden (semi-) Markov Models (HsMMs) are increasingly being used to segment neurophysiological signals into sequences of latent cognitive processes. The idea: different processes will leave distinct traces in trial-level recordings of (multivariate) neuro-physiological signals. Markov models, equipped with an emission model of these traces and a latent process model describing the progression through the different latent processes involved in a task, can then be used to infer the most likely process for any time-point and trial. However, the currently used HsMMs remain limited in two important ways. First, they cannot account for subject-level heterogeneity in the latent and emission process. Instead, a single group-level model is assumed to explain the entire data. Second, they cannot account for the potentially non-linear effects of experimental covariates on the latent and emission process. To address these problems, we present a modeling framework in which the HsMM parameters of the emission and latent process are replaced with mixed additive models, including smooth functions of experimental covariates and random effects. We derive all necessary quantities for empirical Bayes and fully Bayesian inference for all parameters and provide a Python implementation of all estimation algorithms. To demonstrate the advantages offered by this framework, we apply such a multi-level model to an existing lexical decision dataset. We show that, even in such a simple task, not all subjects rely on the same processes equally and that at least two semi-Markov states, previously believed to reflect distinct processes, might actually relate to the same cognitive process.