NeuroImage

Voxelwise encoding models trained on functional MRI data can produce detailed maps of cortical organization. However, voxelwise encoding models must be trained on many hours of brain responses from each participant, limiting clinical applications. In this study, we introduce a cross-participant modeling framework for rapid cortical mapping. In this framework, voxelwise encoding models are trained on many hours of brain responses from previously scanned reference participants, and then transferred to a new participant by aligning brain responses using a small set of stimuli. We evaluated cross-participant encoding models on linguistic semantic mapping, non-linguistic semantic mapping, and auditory mapping. In each case we found that cross-participant encoding models had more accurate selectivity estimates and prediction performance than within-participant encoding models trained on the same amount of data from the new participant. We also found that cross-participant encoding models improved with the amount of data from each reference participant and the number of reference participants. These results demonstrate that cross-participant modeling can substantially reduce the amount of data required for accurate cortical mapping, which may facilitate new clinical applications of functional neuroimaging.

Threshold-free cluster enhancement (TFCE) is one of the most used statistical inference methods in neuroimaging, but its computational cost limits some of its applications. The current implementations recompute clusters at each threshold step, creating computation costs that poorly scale with precision increases. Furthermore, as larger samples and reduced noise increase maximum t-statistics, computational burden grows correspondingly. As the field moves towards finer parcellations, the number of FC edges grows quadratically with the number of ROIs, making TFCE computationally infeasible at the scales increasingly demanded by the field. We present Incremental Cluster TFCE (IC-TFCE), an algorithm that produces numerically equivalent results to standard TFCE while decoupling runtime from discretization precision. The IC-TFCE builds clusters incrementally from previous threshold steps rather than recomputing them, stores TFCE results on a region of interest (ROI) based structure instead of a functional connectivity (FC) edge structure for improved speed, and can be applied to voxel data through a novel graph transformation described and validated herein. This algorithm achieves a measured 3-93x speedup for FC TFCE depending on the precision parameter dh, making TFCE analyses with fine parcellations of 1000 or more ROIs computationally tractable for the first time. Finally, we validate correctness through mathematical proof and numerical comparison. The efficiency provided by IC-TFCE allowed a large-scale empirical power analysis across dh values to guide practitioners in parameter selection for their analyses.

The brains functional organization has been extensively studied through pairwise connectivity analyses. While these approaches have provided important insights into brain network organization, they fall short in capturing the complexity of high-order functional interactions (HOI). Particularly relevant is the investigation of redundancy and synergy patterns -not addressable with pairwise interactions-, revealing fundamental mechanisms of brain integration and information processing across various cognitive functions and clinical conditions. Conventional neuroimaging software packages are primarily designed for classical (general linear model-like) analyses but lack native support for HOI metrics. To address this gap, this study introduces a novel framework that bridges high-order information theory with conventional neuroimaging analysis pipelines and is subsequently applied to resting-state functional MRI to demonstrate its practical utility. By representing HOI into voxel-level metrics, our approach allows standard neuroimaging analyses to probe complex multivariate dependencies. Moreover, voxel-level group-comparison analyses show age differences linked with reduced redundancy in default mode network interactions. These findings advance our understanding of the complex relationship between multivariate functional interactions, voxel-level neuroimaging, and behavior, highlighting novel analytic strategies to study high-order information processing underlying cognitive function and its alterations in pathological conditions.

Presurgical mapping of key white matter (WM) fiber tracts is crucial for intracerebral hemorrhage (ICH) surgery, but it currently relies on tractography from diffusion MRI (dMRI), which has limited applicability in urgent or resource-constrained settings due to long scan times and limited MRI availability. To bridge this gap, we developed a deep learning approach designed to reconstruct critical fiber tracts directly from routinely acquired CT scans, focusing on the corticospinal tract (CST) due to its high clinical relevance. By training a novel network on a curated dataset of 150 paired CT-dMRI scans (101 ICH patients and 49 healthy subjects), we enabled the direct mapping of the CST from CT images alone, bypassing the traditional requirement for dMRI. Our results demonstrate that the CT-derived tracts achieve high anatomical plausibility, with neurosurgeon expert assessments yielding a Likert score of 3.64. Furthermore, the clinical relevance of these reconstructions was validated by a significant correlation between CT-derived tract integrity and patient motor scores (r = 0.726, p = 3.731x 10-7). These findings suggest that in complex clinical scenarios, particularly where dMRI signal quality is compromised by lesion-induced distortion, this CT-based mapping may serve as a useful anatomical reference. Overall, by enabling CT-based WM mapping, our method potentially offers a practical route to gain the key advantages of tractography in resource-limited or time-critical settings

The human neocortex is organized into six laminae forming the structural basis for feedforward and feedback connections across the brain, yet their functional contributions have remained largely inaccessible for non-invasive imaging methods. Leveraging the ultrahigh field of a 10.5 Tesla scanner, we acquired anatomical and functional MRI data at 0.37mm ([~]0.05 {micro}L) and 0.35mm ([~]0.04 {micro}L) isotropic resolution, respectively, approaching the scale of individual cortical layers in humans. Using the Stria of Gennari as an in-vivo anatomical landmark, we extend our previous finding that feedforward visual activation in layer IV of the primary visual cortex during visual stimulation was resolved in laminar BOLD profiles. These laminar features were reproducible across sessions and were not clearly visible with more typical 0.8 mm resolutions at 7T, underscoring the benefits of further increases in magnetic field strength and resolution. This imaging domain, however, comes with increasing challenges of distortion, alignment, and cortical depth estimation, which must be addressed and mitigated to realize its benefits. In this paper we discuss the promises and challenges of this new regime of high resolutions. Our findings showcase the potential of ultrahigh field, ultrahigh resolution human fMRI to bridge the gap with invasive imaging of cortical layers.

Diffusion MRI tractometry characterizes white matter microstructure along fiber bundles, but standard along-tract profiling collapses measurements across the bundle cross-section, obscuring radial heterogeneity and producing spatially inconsistent units of inference. We present SPECTRA (Spatial Inference for Tractometry), a framework designed to address these limitations through a unified design of parameterization and statistical inference. First, we propose a 2D bundle parameterization that extends along-tract profiling to include a radial dimension defined on the atlas bundle. Second, we develop a two-stage hierarchical false discovery rate (hFDR) procedure for multi-bundle inference, which aggregates evidence at a coarser spatial scale before proceeding to finer-grained inference, with spatial scales derived from a Matern kernel. Across extensive simulation conditions, we found that hFDR improves statistical power and reduces the sample size required to detect effects compared to global FDR correction, while maintaining appropriate error control. We further characterized how sensitivity-specificity tradeoffs depend on sample size, the magnitude, spatial extent, and configurations of effects, thereby providing practical guidance for tractometry study design. In an empirical analysis of mild cognitive impairment and dementia in more than 4,000 subjects across 63 bundles, SPECTRA revealed spatially localized patterns that were absent in 1D profiles. Together, these results demonstrate that spatially resolved parameterization and adaptive error control jointly enable precise mapping of white matter microstructure in large-scale tractometry studies. SPECTRA is openly available as a Python package.

The goal of the resting-state functional connectivity studies is to determine the inherent dynamics of the brain networks while the body is at rest. These networks get differentially activated when the brain is involved in various tasks such as processing of sensory inputs, initiating motor activities, or various cognitive tasks. Resting state functional connectivity networks are commonly revealed by determining Pearson Correlation Coefficients of the Blood Oxygenation Level Dependent (BOLD) signals collected from different brain regions using functional Magnetic Resonance Imaging (fMRI) while the subject is not actively performing any task. However, the functional connectivity thus determined does not correlate well with the known structural connectivity between different brain regions. Here, we used Empirical Mode decomposition (EMD), followed by Hilbert Transformation (HT), to determine the resting state functional connectivity of the somatomotor network in the human brains. We show that the time series data decomposed by this method improves correlation of the derived functional connectivity with the known structural connectivity (especially for low -TR fMRI data) as compared to the methods commonly used.

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.

Neural oscillations are central to brain function and communication, yet they are typically characterized in terms of spectral power within predefined frequency bands, potentially obscuring their underlying functional organization. An alternative framework focuses on oscillatory frequency rather than power, revealing that each brain region exhibits a characteristic, or natural, frequency that can be estimated at the voxel level using a data-driven approach. Although this framework has been successfully applied to MEG, its broader use remains limited by cost and availability. Here, we extended this approach to EEG and validated it against MEG-derived maps, assessing its robustness across EEG channel densities (high-density, 64 channels; low-density, 32 channels) and physiological states (eyes open and closed). EEG-derived maps revealed a coherent spatial organization of natural frequencies across the cortex, reproducing the large-scale posterior-to-anterior and medial-to-lateral gradients of increasing frequency previously described with MEG. Differences between MEG and EEG were mainly confined to frontal and temporal regions, likely reflecting the differential sensitivity of the two techniques to neural source configurations, whereas posterior regions showed highly similar patterns. Importantly, this organization remained stable despite reductions in EEG sensor density and was modulated by physiological state, reproducing the well-known posterior alpha dominance during eyes-closed conditions. Together, these findings demonstrate that natural frequency mapping can be extended beyond specialized MEG research environments to low-density EEG settings, offering an accessible and scalable tool for investigating brain oscillations and their alterations in neuropsychiatric conditions.

Dynamic positron emission tomography (PET) provides a powerful tool for studying in vivo neurochemical processes, including transient neurotransmitter release. However, widely used models such as the simplified reference tissue model (SRTM) assume time-invariant kinetic parameters, limiting their ability to capture dynamic changes in binding. Existing extensions introduce time-varying effects through parametric response functions or basis expansions, but are often constrained by restrictive functional assumptions, computational complexity, or limited uncertainty quantification. We propose a latent Gaussian process extension of SRTM (LGPE-SRTM), in which the apparent efflux parameter is modeled as a smooth, time-varying function within a hierarchical framework. By applying a first-order implicit discretization of the governing differential equation, the model admits a representation that is linear in all kinetic parameters in the mean structure, while nonlinearity is confined to a parameter-dependent covariance. This yields a conditionally linear mixed-effects model with structured covariance, enabling efficient likelihood-based inference. The proposed approach integrates mechanistic modeling, nonparametric flexibility, and hierarchical inference in a unified and computationally scalable framework. By representing functional effects on a shared temporal domain, all core computations reduce to operations on low-dimensional matrices whose size is independent of the number of subjects. This enables robust population-level inference on timevarying neurotransmitter dynamics without imposing restrictive parametric forms. The method is demonstrated on both simulated and empirical PET data, where it accurately recovers transient effects, provides well-calibrated uncertainty, and distinguishes constant from time-varying dynamics.

A practical barrier in multimodal neuroimaging is that structural MRI, fMRI, and EEG are often analyzed in modality-specific spaces or reduced to atlas- and sensor-based summaries, limiting the construction of common, interpretable subject-level brain signatures. We evaluate cortical Laplace-Beltrami eigenmode coordinates as a shared geometry-aligned language for structural MRI (sMRI), resting-state fMRI (rs-fMRI), and EEG. In this framework, sMRI morphometric fields are represented by cortical eigenmode coefficients, rs-fMRI by covariance among eigenmode time-series coefficients, and EEG by mode-frequency-condition summaries. Using the Max Planck Institute Leipzig Mind-Brain-Body dataset (MPI-LEMON), we compared unimodal eigenmode-coordinate summaries, multimodal cortical eigenmode-coordinate PCA, conventional atlas/sensor-based PCA and ridge representations, and geometric eigenmode multiview factorization (GEMF). GEMF is a structured decomposition that preserves the modality-native organization of the data objects while separating shared from modality-specific variation. Eigenmode-coordinate representations yielded compact subject-level signatures with strong external validity for chronological age and a secondary cognitive outcome. Multimodal eigenmode-coordinate PCA was among the strongest-performing approaches, reached high age-prediction performance at moderate dimension, and consistently outperformed conventional low-dimensional PCA. GEMF selected an even lower-dimensional shared representation and remained competitive with the benefit of providing interpretable shared and modality-specific factors. These findings support cortical eigenmode coordinates as a practical foundation for compact, interpretable, and multimodally aligned subject-level brain signatures.

AO_SCPLOWBSTRACTC_SCPLOWCerebrospinal and interstitial fluid transport play a central role in brain metabolic waste clearance, yet non-invasive quantification of deep-brain flow dynamics remains challenging. Magnetic Resonance Artificial Intelligence Velocimetry (MR-AIV) is a physics-informed neural network framework that infers three-dimensional velocity, pressure, and permeability fields from dynamic contrastenhanced MRI by embedding porous-media flow physics into the learning process. Here, we present a methodological refinement and systematic evaluation of MR-AIV. We introduce a universal, anatomically informed, region-of-interest-based permeability initialization that improves anatomical alignment and physical consistency across subjects. We quantify the sensitivity of inferred fields to key modelling choices, including initialization strategies, permeability bounds, diffusivity assumptions, signal-concentration relationships, and measurement noise. Across these conditions, MR-AIV yields stable velocity and permeability estimates with preserved spatial structure. Together, these results establish practical guidelines and identify stable operating regimes for reliable deployment of MR-AIV. By improving robustness and reproducibility, this work strengthens MR-AIV as a minimally invasive approach for mapping brain-wide porous fluid transport and supports its application to studies of neurological health and disease.

Diffusion-weighted magnetic resonance imaging (dMRI) is used to study white matter microstructure and to delineate pathways by estimating fiber orientation distributions (FODs). Symmetric FODs represent the conventional model assuming antipodal symmetry in water diffusion. However, in complex regions with bending, branching or fanning fibers, this assumption is not guaranteed. To better capture such underlying fibers geometries, asymmetric FODs (A-FODs), derived from neighboring FODs, have been introduced. Here, we propose an Encoder-based Curvature-Aware Regularization (EnCAR) method for estimating A-FODs. Incorporating curvature features into the regularization weight applied to neighboring voxels improves reconstruction of A-FODs. A self-supervised Transformer network, combined with a Spherical Harmonics Semantic Encoder, learns region-specific regularization parameters from this local neighborhood to capture the diversity of fiber geometries across the brain. The EnCAR method was verified on the DiSCo challenge phantom, and applied to in vivo multi-shell Human data. The model estimated sharp, high-angular-resolution A-FODs that were well aligned with local fiber pathway. Compared with established FOD and A-FOD methods, it performed on par in regions dominated by symmetric FODs and outperformed them in complex asymmetric regions. Quantitative evaluation using the Asymmetry Index (ASI) and Model Discrepancy Index (MDI) confirmed improved consistency with the underlying diffusion signals. By ensuring smooth directional transitions, this work enhances the visibility of continuous fiber segments.

Comparative mapping of functional and structural homologies across humans, small animals, and nonhuman primates has been extensively pursued due to its strong translational relevance. However, these experimental models possess inherent limitations in fully recapitulating the complexity of human cortical organization. The porcine model has recently emerged as a promising alternative, given its neuroanatomical and physiological similarities to the human brain. Despite these advantages, systematic cross-species characterization of functional and structural homologies between humans and pigs remains largely understudied. In the present study, we acquired resting-state functional MRI and diffusion MRI data from pigs and analyzed them alongside corresponding human datasets to investigate cross-species correspondence in large-scale brain organization. First, to enhance functional network alignment across species, group independent component analysis was performed separately within each species to identify intrinsic large-scale functional networks. Our results demonstrated that multiple canonical human resting-state networks are represented in the porcine brain, including sen-sorimotor, default mode, cerebellar, frontal, and central executive networks. Moreover, we observed significant cross-species concordance in intrinsic functional architecture across multiple distributed networks, both in spatial distribution and temporal patterns, indicating homologous large-scale brain organization between pigs and humans. Second, we conducted comparative structural analyses using tractography derived from diffusion MRI and color-encoded fractional anisotropy maps to examine white matter geometry in pigs and humans. Cross-species comparison revealed substantial similarities in major white matter pathways and their spatial organization, supporting structural correspondence at the level of tract geometry. Together, these findings underscore the translational value of the porcine model as a robust and neurobiologically relevant platform for investigating human brain function, structural organization, and related neurological disorders.

Diffusion MRI (dMRI) tractography provides a non-invasive method for mapping whole-brain structural connectivity. However, its application is limited by substantial false-positive and false-negative connections. While deep learning based methods have shown promise in improving tractography, most rely on training data derived from conventional dMRI tractography, therefore inheriting the same limitations. Here, we introduce FiberLM, an attention-based Transformer model for mouse brain tractography. The model was trained using a whole-brain streamline dataset based on viral tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA), allowing the model to learn the properties of both local and long-range axonal trajectories through self-attention. FiberLM was applied to predict anatomically plausible axonal trajectories from ex vivo high-resolution mouse brain dMRI data. Quantitative evaluations demonstrated that FiberLM significantly reduced false-positive and false-negative connections, improved spatial agreement with tracer-defined pathways, and generated whole-brain connectomes that more closely approximated AMBCA results compared to conventional tractography. These findings suggest FiberLM as a potential tool for accurate reconstruction of mouse brain structural connectomics.

Functional brain mapping is an important tool to understand the organization of the human brain, both at the group level, but also to an increasing degree at the level of the individual. There are currently two main approaches to do so. Resting-state fMRI relies on inter-regional correlations of random fluctuations of the signal. In contrast, task-based localizers typically use a single-contrast between a task of interest and a matched control task to identify the location of a functional region in an individual brain. In this paper, we propose and evaluate a third approach: the use of multi-task batteries for both localization of a single functional region and parcellation of multiple functional regions. We show that multi-task localizers produce more consistent estimation of a single functional region across subjects than the single-contrast approach using the same amount of fMRI data. Furthermore, we demonstrate that the multi-task approach is sensitive to true inter-individual differences in region size, and does not suffer the same influence of signal-to-noise ratio that biases the single-contrast localizer. We then address the question of how to select tasks for the battery, and present a data-driven strategy that optimizes the characterization of a brain structure of interest. We show that such batteries outperform randomly selected batteries both for building individual parcellations as well as individual connectivity models. Finally, we demonstrate that an interspersed design - where all tasks are presented in each imaging run - yields more reliable results than splitting the tasks across different runs. We present an open source toolbox for the implementation of multi-task batteries, along with a library containing group-averaged activity patterns that can be used to optimize battery selection for different brain structures of interest.

Inter-individual variability poses a significant challenge in decoding brain activity across subjects. While standard anatomical registration procedures reduce morphological differences, they fail to capture functional variability between subjects. Functional alignment methods address this issue by establishing voxel-to-voxel correspondences between pairs of individuals, thereby constructing a shared functional space. This shared space can be extended at the group level by generating a functional template. However, despite the availability of toolboxes, functional templates remain underused in fMRI analysis. Adopting this approach is currently difficult due to the diversity of existing methods and the lack of clear guidelines. Comprehensive evaluations of functional templates are limited to movie-watching paradigms. Here, we extensively compare functional alignment methods (Optimal Transport, Procrustes, Ridge regression, and Shared Response Model) and template construction strategies (in-sample, out-of-sample, pairwise) within the more general framework of task decoding. In this framework, decoding accuracy measures how well individual activation patterns align. Across multiple tasks and datasets, we demonstrate that population templates built using Optimal Transport (a) yield the highest decoding accuracy, (b) are not significantly biased by individual subjects, which facilitates generalization to new subjects, and (c) preserve the cortical signal topography.

Compared with model-based and phenomenological descriptions of the spatiotemporal dynamics of resting-brain activity, statistical characterizations of resting-state fMRI (rs-fMRI) data remain relatively underexplored. Some sophisticated analysis techniques, such as Mapper-based topological data analysis (TDA) and innovation-driven coactivation pattern analysis (iCAP), can distinguish real data from phase-randomized (PR) surrogates, suggesting that rs-fMRI data are not as simple as stationary Gaussian processes. However, the exact statistical properties that distinguish real rs-fMRI data from PR surrogates have not yet been determined. In this study, we conducted system identification analysis and surrogate data analysis to specify key statistical properties that allow TDA and iCAP to discriminate real rs-fMRI data from PR surrogates. We first analyzed rs-fMRI data concatenated across scans using autoregressive (AR) modeling and found that the scan-concatenated rs-fMRI data were weakly non-Gaussian. However, non-Gaussianity alone was insufficient to reproduce realistic TDA and iCAP results because of non-stationarity across scans. AR modeling of single-scan data revealed that rs-fMRI data were statistically indistinguishable from a Gaussian distribution within a single scan, although TDA and iCAP results still differed between the real data and PR surrogates. A new surrogate dataset designed to preserve non-stationarity successfully reproduced realistic TDA and iCAP results, suggesting that TDA and iCAP likely capture the non-stationarity of rs-fMRI data to distinguish it from PR surrogates. Together, these results indicate approximate Gaussianity and non-stationarity in rs-fMRI data, providing a data-driven and statistical characterization of resting-state brain activity that can serve as a quantitative reference for whole brain simulations and generative models.

In this paper, we propose a novel method that estimates time-resolved communities, or networks, from parcellated fMRI data based on instantaneous phase of the parcel timeseries. Importantly, each community label reference a limited phase range across time and subjects. It thus avoids the relabelling problem that is common for community detection algorithms. Our aim was to enhance the temporal resolution of brain network analysis in a whole-brain context. The method provides insights into how brain regions synchronize both within and across subjects and reorganize during task performance. It offers a complementary perspective on network integration and segregation as concurrent processes, quantified through differences in instantaneous phase. We employed HCP motor task fMRI data to exemplify its practical application. Prior to calculation of instantaneous phase, signal timeseries were decomposed into two signal modes with minimally overlapping frequency ranges. We show that task specific motor movements (hand, feet, tongue) can be separated from block-design related activation (visual and attention networks) where the former was found in the slower mode and the latter in the higher frequency mode.

Global Signal Removal (GSR) is a widely applied step in functional magnetic resonance imaging (fMRI) preprocessing. Although GSR conventionally denotes Global Signal Regression, we use Global Signal Removal to encompass a broader family of spatial filtering operations. GSR in general remains controversial due to concerns about introducing spurious anticorrelations and removing neurally meaningful signals. In this paper, we provide a precise geometric characterization by formalizing GSR as graph spatial filtering. We demonstrate that the most common form of GSR, Regression-GSR, equates to a rank-1 deflation of the covariance matrix (i.e. functional connectivity) by the degree vector. Empirically, the degree vector is dominated by the first principal component of the functional connectivity matrix (correlation = 0.88 {+/-} 0.12 in resting-state HCP data), making Regression-GSR an approximation to first eigenmode removal. This view of GSR as a spatial projection framework allows us to develop a family of GSR variants, each expressible in a unified spatial filter: Naive-GSR removes the uniform vector, PCA-GSR precisely removes the first eigenvector, and SC-GSR, a new variant we introduce that removes the first harmonic of the structural connectivity matrix. A key distinction emerges: while Naive, PCA, and SC-GSR are orthogonal projections, Regression-GSR is an oblique projection that computes regional weights proportional to the degree vector but removes a spatially uniform signal. All GSR variants induce numerical singularity in the covariance matrix, but they differ in their effects on task-state separability, which we examine empirically. In summary, we reframe GSR as a family of graph spatial filters that enable interpretability of its effects, with systematically varying effects on network connectivity across variants.