Imaging Neuroscience

Optically pumped magnetometer magnetoencephalography (OPM-MEG) offers a wearable, movement-tolerant alternative to conventional cryogenic MEG, placing sensors closer to the scalp and, in principle, improving sensitivity to deep sources. This is advantageous for examining subcortical structures that are affected by ageing, disorders and disease, such as the hippocampus. However, it remains unclear whether well-established activity (such as the attenuation of theta oscillations during the imagination of novel scenes) can be recovered from the medial temporal lobe (MTL) with OPM-MEG, and whether an individual structural MRI is required. Here, fifteen adults completed a scene imagination task. Initially we applied a 12-parameter template warping coregistration pipeline to the full sample. Following source reconstruction, we recovered the expected attenuation of theta (4-8 Hz) power during scene imagination compared to a counting baseline, with a significant cluster of activity peaking in the left parahippocampal gyrus. The clusters centre of mass was localised to the left hippocampus (t = -3.55, p = 0.048, whole-brain FWE-corrected) and was mostly confined to the left medial temporal lobe. We further supported our findings by using an individual T1-weighted MRI reconstruction pipeline in six participants who had these scans available. The two approaches produced similar whole-brain topographies and localised the peak MTL theta effect to left hippocampus, with temporal-lobe conjunction centroids 3-mm apart. These findings provide evidence that the theta attenuation of the scene construction network can be recovered at the group level with OPM-MEG, without an individual MRI.

Individualized resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to guide neuromodulation target selection. However, clinical scans are often short and noisy, and standard pipelines for functional network identification do not provide information about confidence of network assignment. With limited data, unstable network assignments can misdirect stimulation toward off-target regions, making it critical to know which assignments can be trusted. We developed Precision Confidence Mapping (PCM), a bootstrap-based framework that makes this uncertainty explicit and actionable. PCM repeatedly resamples the time series and reruns network detection to estimate how consistently each vertex is assigned to a given network. The resulting confidence maps can be thresholded to exclude less stable regions. We evaluated PCM across scan durations from 5 to 70 minutes using positive predictive value (PPV) as the primary measure of network-assignment precision. PPV quantified the proportion of vertices assigned to a network that received the same label in an independent within-subject 70 minute reference map. Confidence thresholding markedly improved PPV across functional networks, with the largest gains for short scan durations. Compared with standard network assignment, PCM significantly increased agreement with this independent reference. Within-subject agreement remained greater than between-subject agreement, indicating that thresholding preserved individual-specific network topography. These precision gains came with modest reductions in reference-network coverage, particularly at shorter scan durations. This tradeoff may be acceptable for neuromodulation applications that prioritize minimizing off-network assignments. By adding a reliability layer to individualized mapping, PCM supports more cautious and precise neuromodulation targeting under real-world clinical scan constraints.

Functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) both rely on the phenomenon of neurovascular coupling (NVC) to probe brain activity through their sensitivity to cerebral blood oxygenation. However, the relationship between fNIRS chromophores (oxy- and deoxyhaemoglobin, HbO and HbR), and fMRI (Blood Oxygen Level Dependent and Arterial Spin Labeling, BOLD and ASL) measurements, and whether this relationship remains consistent across subjects and physiological conditions, has only been partially characterised.. We acquired concurrent continuous-wave fNIRS and gradient-echo (GE) and spin-echo (SE) BOLD-ASL fMRI in healthy adults (n = 10) during visual stimulation. By applying calibrated fMRI methodology, we examined the relationships between fNIRS-derived haemoglobin modulations and fMRI-derived modulations in macrovascular (GE-) and microvascular (SE-) BOLD signals, cerebral blood flow (CBF), and oxygen metabolism (CMRO2). Group-level results showed strong temporal cross-modal agreement, with HbO and HbR tightly mirroring all fMRI signal time-courses (|r| > 0.8). A quantitative analysis of trial-by-trial modulations revealed distinct state-dependent behaviours: HbO maintained a stable relationship with the fMRI-derived metrics across conditions, whereas cross-modal relationships between HbR and fMRI-derived metrics substantially strengthened at higher flow-metabolism coupling (FMC), the ratio of CBF to CMRO2 change, an index of the strength of NVC. Both HbO and HbR were more strongly associated with GE-BOLD than with SE-BOLD. These findings provide a rigorous physiological grounding for fNIRS signal interpretation, demonstrating its utility as a surrogate marker for specific haemodynamic and metabolic parameters.

Optically pumped magnetometers (OPMs) can be used for magnetoencephalography (MEG) with equivalent or improved signal to noise ratio, relative to cryogenic MEG, when sensors are placed close to the scalp. OPM-based MEG can also be used in mobile contexts if sensors are placed in lightweight, wearable arrays. Individually tailored, rigid helmets known as scannercasts are currently the only method capable of achieving on-scalp, mobile recordings with high precision. However, these scannercasts are expensive to produce, require structural imaging in advance of the experiment, and can incur lengthy downtime while sensors are transferred between scannercasts. Here, we introduce a solution to these challenges that retains the advantages of scannercasts. We provide detailed steps for constructing a modular, cap-based design, suitable for all head sizes. Using simulations, we compare the leadfield power of this array against an idealised array and a commercially available mobile solution. We then validate our proposed solution empirically, in five participants, and provide a complete data preparation and analysis pipeline. Our design expands the accessibility of OPM-based MEG, and increases participant throughput to levels comparable to other imaging modalities. Crucially, it removes the trade-off between signal quality, mobility and practicality, promoting the unique potential of OPM-based MEG as a tool for studying naturalistic behaviour, and clinical assessment with high precision.

Transcranial magnetic stimulation (TMS) is commonly dosed by setting stimulation intensity as a fixed percentage of the resting motor threshold (RMT), although a motor-derived intensity may not produce comparable neural recruitment across non-motor targets. We present TIDE (Tractography-Informed Dose Estimation), an open-source, SimNIBS-based pipeline designed to derive individualised stimulation intensities for non-motor white-matter targets. TIDE combines individual RMT measurements, finite-element electric-field modelling and diffusion MRI tractography to rescale the stimulation intensity according to the geometry and stimulation efficiency of the pathway of interest. Specifically, it computes the activating function along subject-specific streamlines and estimates the stimulator output, expressed as a percentage of maximum stimulator output, required for the target pathway to reach the activation level produced in the corticospinal tract at RMT. In an independent dataset of 19 participants, in which stimulation had been dosed conventionally as a fixed percentage of RMT, the relative difference between delivered and TIDE-estimated intensity was associated with the magnitude of TMS-induced behavioural effects at two frontal aslant tract (FAT) stimulation sites, while the delivered intensity alone was not. TIDE therefore extends conventional E-field dosing from cortical field magnitude to subject-specific pathway geometry, providing a method to move beyond the assumption of homogeneous pathway engagement while accounting for inter-individual variability in pathway-specific stimulation efficiency.

Introduction: A new method that corrects for time-dependent bias in standardized-uptake value ratios (SUVRs) was adapted and optimized for [11C]PiB (PiB) amyloid-beta (A{beta}) PET, across low-to-high A{beta} loads, relying only on PET data collected during the SUVR time-window. This modeling approach was evaluated in cross-sectional and longitudinal cohorts for earlier and shorter SUVR time-windows (30-45 min, 45-60 min) than commonly applied, to enable higher throughput imaging. Methods: The SUVR correction (SUVRc) approach was optimized and tested on separate cross-sectional (n=88), and longitudinal (36 participants, two time-points, 72 images) cohorts from the Harvard Aging Brain Study. The cross-sectional cohort spanned low, intermediate and high levels of cortical A{beta} pathology and the longitudinal images included two cohorts with low (5-10%) and high levels (~40%) of A{beta} change. SUVR and SUVRc were compared against SRTM DVR (0-60 min) to quantify A{beta} burden through Pearson's and Lin's correlations, difference plots and longitudinal change. Results: The mean regional bias in PiB SUVR (5-15%, depending on time-window and A{beta} burden) was significantly reduced to < 3% by SUVRc (corrected p < 0.05) in the cross-sectional cohorts for all time-windows, along with reductions in bias variability. SUVRc also showed higher Pearson's correlation (r) and Lin's concordance (LCC) with DVR across time-windows (r=0.98, LCC=0.99 at 30-45 min and 45-60 min) compared to uncorrected SUVR (r=0.96, LCC=0.95 at 30-45 min, r=0.97, LCC=0.92 at 45-60 min). Bland-Altman plots confirmed better agreement between SUVRc and DVR (mean bias at 30-45 min: 0.02 for SUVRc, 0.10 for SUVR; mean bias at 45-60 min: 0.01 for SUVRc, 0.17 for SUVR). Longitudinal DVR changes were more accurately represented by SUVRc, compared to uncorrected SUVR. Conclusions: SUVRc for [11C]PiB PET enables more accurate quantification of A{beta} burden than SUVR in cross-sectional and longitudinal studies (relative to SRTM DVR), while enabling imaging at earlier and shorter time-windows. The improved accuracy would be beneficial in better quantifying amyloid re-emergence post anti-amyloid therapy and could be used for kinetic harmonization across time-windows and radiotracers.

Aging affects individuals at varying biological rates, prompting the development of the Brain Age Index (BAI) to quantify neurobiological health relative to chronological age and disease risk. While structural MRI has dominated brain age prediction, its high cost, immobility, and low temporal resolution restrict its clinical scalability and responsiveness to transient neurophysiological changes. Electroencephalography (EEG) offers a highly scalable, portable, and temporally precise alternative capable of capturing dynamic brain states. However, the transition of EEG-based models to clinical biomarkers is impeded by methodological limitations, including small or biased datasets, inconsistent preprocessing pipelines, and a distinct lack of interpretable machine learning approaches. To address these persistent challenges, this paper presents a comprehensive, open-source, end-to-end pipeline for large-scale EEG-based brain age modeling. Developed using the Temple University Hospital EEG Corpus (TUEG) the largest publicly available resting-state EEG dataset. The pipeline encompasses rigorous data engineering, reproducible preprocessing, and robust feature extraction. Following quality control and subject-level dataset partitioning to definitively prevent data leakage, exactly 41,181 recordings were successfully retained. Two independent feature sets were extracted: the Catch22 time-series characteristics and a comprehensive set of spectral, aperiodic, and non-linear dynamics from the CCS toolbox. The methodology evaluates seven regression models, optimized via Optuna for hyperparameter tuning, and integrates SHAP (SHapley Additive exPlanations) for transparent feature importance analysis. By making this infrastructure publicly available, this work lowers the barrier to entry for large-cohort studies, fostering reproducible development and clinical validation of dynamic brain age biomarkers.

Subject-independent affect regression from physiological signals remains challenging because emotional responses vary substantially across individuals, widely used datasets provide only coarse trial-level annotations, and heterogeneous physiological modalities may not contribute reliably when treated as if they were interchangeable predictors. We have developed AffectRoute, a protocol-conditioned subject-independent affect regression that is conditioned on the protocol and assigns separate predictive functions to information from the population, electroencephalography (EEG), and peripheral physiological signals (PPS). First, a source-population prior establishes a trial-level affective anchor using only the data from source participants. TrajBridge then combines an EEG representation that is supervised by REFED for participant-specific adjustments with temporal structure obtained from the continuous REFED annotations in order to create a weakly supervised segment-resolved pseudo-trajectory and to establish a frozen trial-level baseline. PhysioRoute next reduces the remaining error by breaking down the residual correction into a source-derived direction, which is estimated from the out-of-fold residuals within the source group, and a channel-specific magnitude derived from the PPS. When evaluated on DEAP and DREAMER using a leave-one-subject-out approach at the participant level, AffectRoute showed consistent step-by-step improvements in both the mean absolute error and the concordance correlation coefficient. A method that relied solely on the source data was clearly worse than PhysioRoute, showing that the final improvement cannot be accounted for by transferable source residual regularity alone. Conventional alternatives to fusing the PPS were also found to be consistently less effective, although analyses at the channel level and with a leave-one-channel-out design showed that the peripheral contributions are axis-dependent yet distributed across channels. These results indicate that structured residual inference is an effective alternative to unrestricted multimodal fusion for subject-independent affect regression.

Conflict monitoring and error processing are fundamental mechanisms underlying cognitive control and decision-making, and have been consistently associated with increased activity in the anterior midcingulate cortex (aMCC). Despite the extensive literature supporting this association, there remains a substantial gap in establishing a causal relationship between the aMCC and cognitive control processes. In the present study, we used low-intensity transcranial ultrasound stimulation (TUS), an emerging technique that enables non-invasive, deep, and focal neuromodulation, to investigate the causal role of the aMCC in behavioral and electrophysiological markers of cognitive control. Nineteen participants received 5Hz rTUS targeting the aMCC and the posterior cingulate cortex (PCC) on separate days, followed by performance of a Flanker task during EEG recording. Our findings demonstrate that TUS of aMCC modulated the relationship between conflict monitoring and the N2 component, but did not affect the coupling between error processing and the ERN, relative to the TUS of PCC, suggesting a dissociable contribution from both regions to cognitive control. These results were further supported by exploratory drift diffusion model (DDM) analyses, which revealed that, for most participants, TUS of aMCC enhanced suppression of flanker distractors. Importantly, after TUS of aMCC, but also PCC, we observed higher accuracy during early compared with later task blocks, corroborating previous findings suggesting that TUS effects are temporally dynamic and characterized by a limited post-stimulation window. HIGHLIGHTSO_LIaMCC-TUS selectively enhances response conflict sensitivity and modulates its coupling with the N2 compared with PCC-TUS. C_LIO_LIExploratory DDM analyses suggest that aMCC-TUS improves suppression of conflict-inducing distractors. C_LIO_LIaMCC-TUS and PCC-TUS both increase behavioral accuracy during the early stages of task performance. C_LIO_LITUS effects show a transient temporal profile, peaking 17-27 minutes after stimulation and declining after [~]37 minutes. C_LI

Mapping brain connectivity in primates remains a major challenge due to difficulties in resolving microscopic white matter architecture, while maintaining whole-brain coverage. Increasing imaging spatial resolution is key for disambiguating fibre configurations within smaller anatomical volumes. Here, we present novel developments that allow high-resolution diffusion MRI of the macaque brain using one of the world's highest-field human MRI scanners operating at 10.5 Tesla, allowing both in vivo and ex vivo macaque brain imaging. Our approach achieves very high spatial resolution across both tissue states, (up to 580 m)3 in vivo and (300 m)3 ex vivo, with diffusion weighting up to b = 6000 s/mm2. We detail methodological advances in data acquisition, image reconstruction, processing and whole-brain tractography that overcome critical challenges associated with ultra-high-field imaging. This work establishes a new framework for high-resolution in vivo and ex vivo neuroimaging of the NHP brain at 10.5 T using a human bore scanner, paving the way for subsequent analyses of brain connectivity across species and tissue states at unprecedented detail. The dataset, along with all processing pipelines, containerised workflows, and reusable web services, is openly shared to support reproducibility and future integration with microscopy for studying white matter microstructure and connections at the mesoscale.

Background: Quantitative MRI (qMRI) provides voxel-wise measurements of tissue properties related to myelin, iron and water content, making it a powerful tool for studying brain aging and microstructural alterations in vivo. However, conventional spatial smoothing can introduce partial-volume effects and blur tissue boundaries, potentially affecting both statistical sensitivity and anatomical specificity. Several tissue-specific smoothing strategies have been proposed to address these limitations, yet their relative impact on voxel-wise statistical analyses remains insufficiently characterized. The present study aims (i) to systematically compare three tissue-specific smoothing strategies: a linear tissue-weighted compensated approach (TWS), a generalized version of nonlinear tissue-masked compensated smoothing approach (gTSPOON), and an intensity-weighted edge-preserving approach based on the Smallest Univalue Segment Assimilating Nucleus smoothing (SUSANs), and (ii) to investigate how smoothing approaches interact with statistical inference frameworks by comparing parametric and non-parametric voxel-wise analyse. Methods: Analyses were performed on a publicly available lifespan qMRI dataset comprising 138 healthy participants (19-75 years) and quantitative maps of MTsat, PD, R1, and R2*. The generalized TSPOON (gTSPOON) method was implemented using tissue-specific masks derived from probabilistic tissue segmentation. All three smoothing approaches (TWS, gTSPOON and SUSANs) were parameterized to achieve comparable nominal spatial smoothing. Age-related effects were investigated separately in GM and WM using voxel-wise general linear models following a previously published framework. Statistical inference was assessed using multiple complementary approaches, including parametric Random Field Theory (RFT), under both stationarity and non-stationarity assumptions, as well as non-parametric permutation-based inference. In addition to conventional thresholded statistical parametric maps, voxel-wise log-likelihood (LL) maps were computed to quantify general linear model (GLM) goodness-of-fit independently of statistical thresholding. Bland-Altman analyses and spatial agreement metrics were subsequently used to compare smoothing strategies. Results: TWS and gTSPOON produced highly similar spatial distributions of age-related effects across all qMRI parameters and tissue classes. However, TWS consistently yielded a larger number of significant voxels and clusters, reflecting slightly higher sensitivity, from slightly wider effective smoothness and reduced RESEL counts. By contrast, SUSANs generated substantially fewer significant voxels and clusters, associated with approximately half the effective smoothness and a markedly larger number of RESELs. Despite these differences in statistical sensitivity, voxel-wise LL analyses revealed distinct anatomical preferences for each smoothing strategy. TWS provided the best model fit predominantly within GM, whereas gTSPOON showed superior performance in homogeneous WM regions. Conversely, SUSANs achieved the highest LL values at GM-WM interfaces, particularly within sulcal and gyral transitions, indicating improved preservation of sharp anatomical gradients. These spatial patterns were consistently observed across MTsat, PD, R1 and R2* maps. Comparisons across stationary and non-stationary RFT assumptions revealed only minor differences, while non-parametric inference produced highly concordant results, indicating that the primary source of variability originated from the smoothing procedure itself rather than the inference framework. Conclusions: Tissue-specific smoothing strategies substantially influence both statistical sensitivity and voxel-wise model fitting in qMRI analyses. While TWS and gTSPOON provide highly consistent results, the edge-preserving SUSANs approach preferentially enhances model fit at tissue boundaries. Importantly, voxel-wise log-likelihood mapping revealed that no smoothing strategy is uniformly optimal throughout the brain; instead, each method exhibits anatomically preferential regions where model fit is maximized. These findings suggest that smoothing should be viewed as a region-dependent optimization problem and highlight voxel-wise LL mapping as a principled framework for selecting or developing adaptive smoothing strategies tailored to specific neuroanatomical structures and biological processes, including age-related brain changes.

Brain-age models derived from diffusion MRI-based structural connectomes may provide imaging biomarkers of accelerated brain aging, but their biological interpretation and transportability across heterogeneous populations remain uncertain. We developed a calibration-aware and hierarchically interpretable graph-learning framework and evaluated it across four independent aging and Alzheimer's disease-related cohorts: ADNI, Duke/UNC ADRC, HABS-HD, and AD-DECODE. The analysis included 1,093 connectome sessions from 789 participants. Cohort-specific graph neural networks were trained using participant-grouped cross-validation across five imaging and multimodal feature configurations. Prediction performance varied more strongly across cohorts than across feature sets, with imaging-only out-of-fold mean absolute error ranging from 4.72 years in ADNI to 9.75 years in AD-DECODE. The imaging-only graph neural network was competitive with ridge, elastic-net, and gradient-boosted regression models trained on matched vectorized connectome features, but was not uniformly superior. Age-bias-corrected brain-age gap was most consistently associated with reduced diffusion-derived microstructural integrity and structural-network organization across cohorts. In longitudinal analyses, corrected brain-age gap showed moderate-to-good within-person preservation in ADNI and HABS-HD, with intraclass correlation coefficients of 0.67 and 0.81, respectively; higher baseline values also predicted subsequent microstructural and network deterioration in ADNI. Multiscale SHAP analysis identified distributed contributions from global graph topology, regional imaging features, edge-derived regional summaries, and individual structural connections involving thalamic, striatal, frontal, parietal, cerebellar, hippocampal, and entorhinal circuitry. External transfer was highly sensitive to cohort shift: across 12 off-diagonal train-test evaluations, median mean absolute error decreased from 17.39 to 8.22 years after target-cohort linear recalibration, whereas median Pearson correlation remained 0.17. Because recalibration used target-cohort chronological age, it was interpreted as a diagnostic sensitivity analysis rather than deployable external validation. Together, these findings support calibration-aware diffusion-connectome brain age as an interpretable imaging biomarker of structural brain aging and prospective microstructural and network vulnerability, while emphasizing the need for cohort-specific calibration before external application.

Dual-task paradigms are widely used to detect age-related cognitive and motor decline. Conventional evaluations typically average performance across an entire dual-task condition and compare it with a single-task baseline, implicitly treating performance as stable throughout testing. We challenged this assumption by examining block-wise behavioral adaptation and its dynamic functional-connectivity correlates. Forty older adults (50-80 years) and 20 younger adults (20-40 years) performed a cognitive Go/NoGo task, a motor pedaling task, and a combined cognitive-motor dual task during functional magnetic resonance imaging (fMRI) using a custom-built MRI-compatible pedaling device. Motor reaction-time (RT) variability was assessed across eight dual-task blocks, and dual-task benefit was defined as the relative reduction in variability from the first to the final block. Dynamic functional connectivity was characterized using two complementary approaches: dynamic independent component analysis (dyn-ICA), capturing continuously varying circuit properties, and a hidden Markov model (HMM), identifying recurring discrete network states. Across participants, motor RT variability was highest in the first dual-task block, progressively decreased to its lowest level at Block 6, and remained comparatively stable thereafter. Both age groups achieved behavioral stabilization but followed distinct trajectories. Older adults progressed from pronounced initial variability toward their single-motor reference while continuing to perform the dual task, whereas younger adults began closer to this reference and maintained comparatively stable performance. Greater dual-task benefit was associated with higher mean strength of a broadly distributed dyn-ICA circuit encompassing attentional, control, sensorimotor, visual, cerebellar, and default-mode systems (Circuit 4), as well as greater temporal variability of a functionally distinct circuit (Circuit 2).HMM analyses similarly linked greater benefit to more frequent visits to State 6 and greater occupancy of State 9, configurations involving coordinated sensorimotor, salience, dorsal-attention, and frontoparietal systems. Across both approaches, network features preferentially expressed by older adults were associated with greater relative benefit, whereas younger-enriched features accompanied smaller changes from a more stable initial level.These findings demonstrate that dual-task performance evolves substantially within a single session and that behavioral stabilization is related to both continuous circuit properties and discrete network-state visitation. Healthy older and younger adults may therefore achieve successful cognitive-motor adaptation through distinct regimes of dynamic whole-brain organization.

Cross-dataset generalization of EEG-based classification under weak, proxy-derived labels remains an open problem for altered-states research. We present a reproducible eight-dataset alignment pipeline that maps eight heterogeneous EEG corpora (712,832 windows; 697,906 with valid labels) to a common 14-channel EPOC+ montage with 63-dimensional spectral features, and we recover the real 1-9 arousal self-assessments for MAHNOB-HCI from session.xml metadata. As a benchmark, Random Forest classifiers are trained on seven source domains and evaluated on the held-out target under both zero-shot and 20%-participant few-shot calibration. The benchmark exposes two concrete methodological pitfalls rather than a performance result: (i) per-class recall shows every target collapsing to a single majority class, and (ii) a within-dataset upper-bound experiment (Table 3) shows that six of eight proxy label sets sit at or below three-class chance even when trained and tested on the same dataset, so the cross-dataset failure is a label-validity problem rather than a transfer-method problem. Across the eight targets (20 seeds, 8,000 evaluation windows per target), zero-shot accuracy averages 36.85% (95% CI 34.40-39.30) and calibrated 43.76% (41.77-45.75), but balanced accuracy stays at 33.01-35.62% (Cohen's kappa <= 0.068), i.e. at chance. The +6.91pp mean change is driven almost entirely by a single target, ds006437 (6.31% -> 60.60%): the median paired change across all 160 seed-pairs is 0.00pp, and after Holm-Bonferroni correction only ds006437 and ds004572 remain significant, the latter with a practically null effect (+0.39pp). Balanced accuracy stays between 33.01% and 35.62% and Cohen's kappa at 0.009 +/- 0.032, i.e. at or barely above three-class chance, while per-class recall shows six of eight targets collapsing to Deep (96.7-100% recall) and two to Light (68.5-99.1%). The collapse persists under SMOTE oversampling, under an EEGNet-v4 deep-learning baseline, and under CORAL and AdaBN feature alignment, which locates the bottleneck in proxy-label validity and class overlap in the feature space rather than in classifier capacity. We position this work as a preliminary methodological study: its contribution is a reproducible eight-dataset alignment pipeline, recovered MAHNOB-HCI arousal self-assessments, a quantitative estimate of split-leakage inflation, and a transparently reported negative result rather than a performance claim.

Whole-brain tractography reconstructs the major white matter pathways as millions of individual streamlines, offering an exceptionally rich description of neural geometry. Yet the statistical methods used to compare these reconstructions across individuals inevitably discard key information. Voxel-based analyses sacrifice pathway continuity, trajectory-based methods rarely support population-level statistical decomposition, and connectome models largely abstract away the underlying geometry. No existing framework jointly characterizes the population-level statistical organization of white matter and the three-dimensional geometry of the pathways from which that organization is expressed. We introduce streamline independent component analysis (SILICA), a framework that links group-level voxel-space statistical decomposition to subject-specific trajectories through a sparse streamline-by-voxel fingerprint. Each streamline is represented by its physical path length within a common anatomical voxel grid while retaining an explicit index-level link to its original trajectory. A two-stage dimensionality reduction reconciles tractograms of differing size and enables continuous component loadings to be back-reconstructed for every original streamline. These subject-specific loadings support weighted trajectory visualization and can be projected into voxel space to generate track-weighted component maps for conventional image-based visualization and future voxel-wise analysis. Separately, the learned group spatial components can be expressed on an independently reconstructed representative whole-brain tractogram to generate a compact trajectory-resolved atlas for group-level visualization. SILICA is a single decomposition expressed simultaneously in statistical and geometric form. SILICA was evaluated in diffusion MRI tractograms from 30 healthy adults. The recovered spatial patterns correspond to recognizable commissural, projection, and association systems. Back-reconstructions preserved individual trajectory variation while isolating components shared across the group, and their projection into voxel and trajectory space yielded interpretable maps and atlases. As a proof of concept, SILICA has not yet been validated against anatomical reference standards or evaluated for reproducibility and performance relative to established methods. Nevertheless, these results establish a coherent foundation for analyzing white matter in a framework that jointly represents population-level statistical structure and streamline geometry.

1.IntroductionUnderstanding mid-term test-retest reliability and within-subject variability is important for interpreting changes observed in longitudinal and intervention studies. The reliability of resting-state functional magnetic resonance imaging (rs-fMRI) is known to vary across measures and brain regions. However, how reliability differs across functional networks and connectivity-and amplitude-based measures, and whether multi-echo acquisition and processing modify these patterns, remain incompletely characterized. MethodsTwenty-two healthy volunteers underwent two rs-fMRI sessions 15.7 {+/-} 4.0 days apart on a Compact 3T scanner. Multi-echo, middle-echo, and independently acquired single-echo datasets were compared, with multi-echo independent component analysis additionally evaluated as a denoising approach. Functional connectivity (FC) and three amplitude-based measures were evaluated using the Schaefer 400 parcellation. Reliability was systematically assessed using intraclass correlation coefficient (ICC), within-subject standard deviation (wSD), and systematic bias at edge or regional, and network levels. ResultsAcquisition-dependent differences in reliability were generally modest. Multi-echo acquisition and processing increased functional connectivity strength and the magnitude of amplitude-based measures and improved inferior cortical coverage, but these enhancements did not consistently translate into substantially higher ICC or lower wSD. In contrast, reliability showed clear network-dependent differences. FC reliability varied markedly across network pairs and was not explained by connectivity strength alone; pairs involving the default mode and control networks generally showed more favorable profiles than several somatomotor and visual network pairs. Fractional amplitude of low-frequency fluctuations (fALFF) also showed network-dependent reliability, with the most favorable regional reproducibility observed in the default mode and control networks and lower reproducibility in the somatomotor and visual networks. ConclusionThese findings provide practical mid-term reliability benchmarks for rs-fMRI on a Compact 3T scanner and show that measurement stability varies more clearly across measures and functional networks than across acquisition approaches. Key pointsO_LIMid-term test-retest reliability varied more clearly across resting-state measures and functional networks than across acquisition and processing approaches. C_LIO_LIMulti-echo acquisition and processing enhanced functional connectivity strength, amplitude-based signal magnitude, and inferior cortical coverage but did not consistently improve reliability. C_LIO_LIFunctional connectivity strength and fractional amplitude of low-frequency fluctuations showed distinct network-specific reliability profiles, with more favorable reproducibility in default mode and control networks than in several somatomotor and visual networks. C_LI

Automated detection of brain tumors from Magnetic Resonance Imaging (MRI) can accelerate diagnosis and reduce inter-reader variability, yet many existing studies report only top-line accuracy on small datasets, omit efficiency analysis, and provide no interpretability, limiting their clinical credibility. We present a reproducible, comparative, and explainable transfer- learning framework for binary brain-tumor classification. Our framework (i) standardizes a configurable preprocessing pipeline combining CLAHE contrast enhancement and unsharp-mask sharpening, (ii) evaluates a custom CNN baseline and pretrained backbones under an identical training budget, (iii) reports a full metric suite (accuracy, precision, recall, F1, ROC-AUC, PR-AUC, parameter count, and inference latency), and (iv) applies Grad- CAM for spatial interpretability. On a public 253-image MRI dataset (38-image held-out test set), MobileNetV2 achieves the best overall performance (94.74% accuracy, 0.994 ROC-AUC, 0.996 PR-AUC) with only 2.59M parameters and 5.9 ms per- image inference, making it the most deployment-friendly model. Larger backbones (Xception, EfficientNetB0) and the custom CNN converge to degenerate all-positive predictions under the same limited budget, illustrating the small-data overfitting risk that accuracy-only reporting conceals. Grad-CAM confirms that the best model attends to the tumor region. All source code, con- figuration files, and trained evaluation scripts are publicly avail- able at https://github.com/blck-iris/explainable-brain-tumor-mr

Immersive virtual reality (VR) can preserve the logic of laboratory attention tasks while altering the perceptual-action context in which attentional control is expressed. In this study, we examined the neural underpinnings of location-response compatibility in a VR adaptation of the Attention Network Test-Revised (ANT-VR), using a restricted preprocessing multiverse to account for uncertainty arising from defensible EEG analysis choices. Forty-four young adults contributed complete ANT-VR behavioural data. Target-locked EEG analyses were conducted across 192 preprocessing branches, with branch-level participant contributions varying after quality check and trial-count filtering. The contrast compared location-response incompatible with compatible trials and was balanced within participants across cue-target interval, cue condition, flanker congruency, and, for spatial-cue trials, conditions in which spatial cues were valid or invalid for subsequent targets. Across the multiverse, the N2pc-window posterior-lateralisation contrast could be defined in all branches and showed high directional stability: the median incompatible-minus-compatible effect was 0.30 uV [IQR: 0.22 to 0.41], with positive effects in 192/192 branches, nominal evidence in 79/192 branches, and Holm-corrected evidence in 42/192 branches. Comparison across ERP measures indicated that this pattern was more consistent for N2pc-window posterior lateralisation than for P1, posterior N1, frontocentral N2, P3, or response-referenced C3/C4 measures. Comparison across C3/C4 reference frames further constrained the interpretation: target-location-referenced C3/C4 showed the strongest effect, whereas response-referenced C3/C4 was weaker. Behavioural analyses showed no reliable compatibility differences. These findings suggest that location-response compatibility in immersive ANT-VR modulates target-locked lateralised neural activity associated with spatial selection and target-location coding, rather than producing broad sensory, conflict-related, P3-related, or specifically response-referenced modulation.

Despite the growing use of functional near-infrared spectroscopy (fNIRS) hyperscanning to record brain activity simultaneously from interacting individuals in naturalistic settings, most analyses quantify functional connectivity separately for each channel pair. The resulting collection of pairwise estimates is difficult to integrate into a network-level characterization of intra- and inter-brain organization. Here, we present an open, configuration-driven Python toolkit that transforms preprocessed fNIRS hyperscanning time series into functional connectivity graphs. The toolkit constructs a bipartite inter-brain network for each dyad and separate intra-brain networks for each participant, computes node- and graph-level measures, and exports adjacency matrices, edge lists, analysis-ready summary tables, reproducibility metadata, and standardized visualizations. Dataset-specific parameters, including directory structure, participant naming, channel selection, epoch extraction, and edge-retention criteria, are defined in a human-readable YAML configuration file, enabling the same workflow to accommodate differently organized datasets without changes to the source code. We illustrate the pipeline using a representative recording from a mother-infant fNIRS hyperscanning dataset and present the resulting network outputs. The toolkit provides a reproducible framework for moving from pairwise functional connectivity estimates to network-level analyses of dyadic and individual brain organization.

Magnetoencephalography (MEG) offers non-invasive neuroimaging with high temporal and spatial precision - but its adoption is hampered by the prohibitive cost and infrastructure burden of a magnetically shielded room. We have overcome that burden and present a lightweight, low-cost, multichannel magnetoencephalography system that can image brain activity without needing a magnetically shielded room. The multichannel nature of the system facilitates not just detection but also localization of brain signals that are over 300 million times smaller than environmental interference, without requiring passive shielding. Our system weighs less than 75kg, more than 100 times lighter than a typical shielded room. This is made possible through low-cost active shielding and software-based spatial filtering. We also show that the signal to noise ratio of our in-vivo recordings is comparable to what can be obtained from a conventional cryogenically-cooled MEG system sited within a shielded room. This demonstration is a crucial step towards democratizing magnetoencephalography and making it a globally accessible neuroimaging technology for healthcare and discovery research.