Short-range association fibers (SAFs) are critical for cortical communications but are often underestimated in conventional resolution diffusion magnetic resonance imaging (dMRI) since they locate within a ~1.5 mm thin layer of superficial white matter. With the advent of high-resolution diffusion imaging techniques, this study evaluated the effects of image spatial resolution on SAF reconstruction using two datasets: (1) prospectively acquired dMRI data from 20 healthy subjects, each scanned at 3 resolutions (i.e., 2, 1.5, and 0.96 mm iso.), and (2) retrospectively down-sampled dMRI data from the Human Connectome Project dataset, as well as 20 representative MRtrix3-based tractography pipelines. It was found that lower resolution degraded superficial white matter model fitting, lowered the SAF streamline counts, and reduced global and regional short-range connectivity fraction (SCF), defined as the fraction of SAF connections among all association fiber connections, across all tested methods. Temporal lobe cortical regions exhibited the greatest SCF declines at lower resolutions. Tractography methods differed in resolution sensitivity, with diffusion tensor imaging (DTI)-based single-tissue single-fiber tractography showing greater decreases in SCF than constrained spherical deconvolution (CSD)-based multi-tissue multi-fiber tractography at lower resolutions. Probabilistic, anatomically constrained tractography combined with spherical-deconvolution informed filtering of tractograms was more robust to decreases in resolution. Up-sampling to a nominally higher resolution partially improved model fitting and SCF accuracy across the evaluated pipelines, with the greatest effect observed for DTI. Using the 0.96 mm iso. gSlider data and optimized tractography pipelines from this study, we constructed the first human brain atlas of RSCF. In summary, this study provides a systematic and quantitative evaluation using MRtrix3 of how spatial resolution, fiber models, and tracking methodologies affect SAF reconstruction and structural connectivity estimation, serving as a reference framework for methodological choices. These advances may enhance the characterization of both healthy and diseased human brains across a wide range of neuroscientific and clinical applications.

Task-based functional magnetic resonance imaging (fMRI) is a powerful tool for studying brain function. However, the reliability and viability of small-sample studies remain a concern. While it is well understood that larger samples are preferable, researchers often need to interpret findings from small studies (e.g., when reviewing the literature, analyzing pilot data, or assessing subsamples). However, quantitative guidance for making these judgments remains scarce. To address this gap, we leverage the UK Biobank and the Human Connectome Project’s Young Adult dataset to survey a range of standard task-based fMRI analyses, from obtaining regional activation maps to performing predictive modeling. These analyses are repeated using volumetric and two types of cortical surface data. For classic mass-univariate analyses (e.g., regional activation detection or cluster peak localization), studies with as few as 40 participants can be adequate depending on the effect size. For predictive modeling, similar sample sizes can be used to detect whether a feature is predictable, but developing stable, generalizable models typically requires cohorts at least an order of magnitude larger, and possibly two (hundreds or thousands). Together, these results clarify how reliability depends on the interplay of effect size, sample size, and analysis type, offering practical guidance for designing and interpreting small-scale task-fMRI studies.

Functional connectivity (FC) is commonly defined as the temporal coincidence of neurophysiological events, often quantified by the statistical dependency among signals from different brain regions and measured by Pearson’s correlation coefficient in fMRI. However, Pearson’s r captures only linear dependencies, potentially overlooking nonlinear interactions. Recently, Multiscale Graph Correlation (MGC) was introduced to measure statistical dependencies of both linear and nonlinear relationships across multiple scales, offering an “optimal scale” at which such dependencies can be inferred. In this study, we systematically compared FC measurements by Pearson’s r and MGC across datasets, evaluating their reliability, sensitivity to data quantity, and ability to capture distinct experimental conditions (deeper anesthesia in macaques) and brain-behavior association. Results showed highly similar spatial connectivity patterns and strong alignment between Pearson’s r and MGC for within-network FC, where optimal scales were frequently global. However, local optimal scales emerged between networks, suggesting the presence of nonlinear dependencies of FC. Reliability was higher for Pearson’s r overall, but both measurements improved as the quantity of data increased. Notably, MGC revealed variability in the optimal scales under altered brain states in deeper anesthesia, highlighting its potential for detecting local-scale dependencies across states. Despite these advantages, MGC required greater computational resources and did not outperform Pearson’s r in detecting brain-behavior associations. Consequently, Pearson’s r remains a sufficient and reliable measure for many standard applications, whereas MGC can offer more nuanced insights in scenarios where nonlinear dynamics are of particular interest. Researchers should, therefore, balance the potential gains from MGC against its added complexity and computational cost when selecting methods to quantify FC.

Walking on uneven terrain becomes more difficult as we age, and gait becomes less automatic. Using mobile brain imaging via high-density electroencephalography (EEG) can provide insight into the neural mechanisms contributing to reduced mobility capability with aging. The objective of this study was to quantify age differences in electrocortical dynamics during uneven terrain walking, both averaged across many strides and variations within a stride. We included 31 young adults and 71 older adults for analysis. All participants walked on an uneven terrain treadmill with four levels of terrain difficulty at their self-selected speed. Compared with younger adults, older adults exhibited a greater increase in step duration variability and mediolateral sacral excursion variability as the terrain became more uneven. We identified multiple brain regions involved during walking on uneven terrain. Regardless of age group, walking on uneven terrain compared with flat terrain led to a widespread change of electrocortical dynamics in the brain, especially in the alpha (8–13 Hz) and beta (13–30 Hz) band power. In the parieto-occipital region, younger adults experienced a greater reduction in alpha and beta power with increasing terrain unevenness than older adults. We also assessed how intra-stride power fluctuations changed with terrain unevenness and age group. Greater intra-stride power spectral fluctuations in the occipital area were associated with greater terrain unevenness for younger adults, but not for older adults. In summary, older adults showed a greater increase in gait variability than younger adults as the terrain became more uneven, but exhibited a lack of modulation of parieto-occipital activity in response to terrain unevenness. The lack of task-related power modulation may suggest reduced cortical network flexibility in older adults. The absence of increased parieto-occipital activity when walking on uneven versus flat surfaces in older adults may also indicate that, unlike younger adults, older adults already heavily rely on visual processes during flat surface walking and may, therefore, have reduced occipital modulation range remaining to cope with the visuomotor processing demands of walking on uneven surfaces.

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10–30 distributed modes or parcellations with 100–1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs were not formulated, but emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations and every individual. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a new basis for functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

Diffusion-weighted imaging is a tool that can non-invasively provide insights into the microstructure of a given brain region. Various advanced techniques exist within the diffusion-weighted imaging space that each provides valuable insights into different aspects of microstructure. In the following study, we sought to examine whether the combination of derived diffusion metrics (tensors, neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) MRI) in grey-matter regions could reliably predict cognitive performance in older adults, and whether these findings were replicable across datasets. First, we demonstrated that all combinations of diffusion metrics could reliably determine participant characteristics and were significant predictors of age. Second, we found that a combination of Tensor, NODDI, and MAP-MRI metrics within the hippocampus could predict Rey Auditory Verbal Learning Task (RAVLT) performance in older adults above and beyond any combination of two metrics alone. We also found these diffusion metrics were able to reliably predict RAVLT performance and Trails B performance, but not performance on a One-Back working memory task. We also found that these same combinations of metrics could predict working memory performance, but not memory performance within a region associated with working memory (right hemisphere nucleus accumbens). Taken together, these findings indicate that these diffusion metrics provide valuable information on grey-matter microstructure independent of one another, and that the ability to obtain both NODDI and MAP-MRI-based information from multi-shell diffusion scans more than justifies additional scan time.

PET analysis is conventionally performed as a two-stage process of quantification followed by analysis. We recently introduced SiMBA (Simultaneous Multifactor Bayesian Analysis), a hierarchical model that performs quantification and analysis for all brain regions of all individuals at once, and in so doing improves both the accuracy of parameter estimation and inferential efficiency. However until now, SiMBA has only been implemented for the two-tissue compartment model. We have now extended this general approach to also allow a non-invasive reference tissue implementation that includes both the full reference tissue model (FRTM) and the simplified reference tissue model (SRTM). Although the FRTM did not successfully converge and gave rise to unlikely parameter estimates for this application, the SRTM described the data well. In simulated data, SiMBA improves quantitative parameter estimation accuracy, reducing error by, on average, 57% for binding potential () using SRTM. In considerations of statistical power, our simulation studies indicate that the efficiency of SiMBA modeling approximately corresponds to improvements that would require doubling the sample size if using conventional methods, with no increase in the false-positive rate. We applied the model to PET data measured with [11C]AZ10419369, which binds selectively to the serotonin 1B receptor, in datasets collected at three different PET centres (n = 139, n = 44 and n = 39). We show that SiMBA yields replicable inferences by comparing associations between PET parameters and age in the different datasets. Moreover, we show that time activity curve data from different centres can be combined in a single SiMBA model using covariates to control between-centre parameter differences, in order to harmonise data between centres. In summary, we present a novel approach for non-invasive quantification and analysis of PET time activity curve data which improves quantification and inferences, enables effective between-centre data harmonisation, and also yields replicable outcomes. This method has the potential to significantly expand the range of research questions which can be meaningfully tested using conventional sample sizes with PET imaging.

Typically, event-related brain responses are calculated invariant to the underlying event duration, even in cases where event durations observably vary: with reaction times, fixation durations, word lengths, or varying stimulus durations. Additionally, an often co-occurring consequence of differing event durations is a variable overlap of the responses to subsequent events. While the problem of overlap, for example, in fMRI and EEG is successfully addressed using linear overlap correction, it is unclear whether overlap correction and duration covariate modeling can be jointly used, as both are dependent on the same inter-event distance variability. Here, we first show that failing to explicitly account for event durations can lead to spurious results and thus are important to consider. Next, we propose and compare several methods based on multiple regression to explicitly account for stimulus durations. Using simulations, we find that non-linear spline regression of the duration effect outperforms other candidate approaches. Finally, we show that non-linear event duration modeling is compatible with linear overlap correction in time, making mass-univariate models combining non-linear spline regression of duration and linear overlap correction a flexible and appropriate tool to model overlapping brain signals. This allows us to reconcile the analysis of brain responses to stimuli in situations where durations differ between conditions, for example, different reaction times, different stimulus durations, or fixation-related potentials with different fixation durations. While in this paper we focus on EEG analyses, we additionally show that our findings generalize to fMRI BOLD responses, and argue that they should generalize to other overlapping signals such as LFPs, or pupil dilation responses.

Neural populations synchronise their activity with either zero-phase delay (activity in interacting regions occurs simultaneously) or a phase delay (activity in one region follows the other). In electroencephalography and magnetoencephalography functional connectivity analyses, artefactual connectivity can also occur with zero-phase delay. To minimise artefact, contemporary analyses typically exclude all zero-phase-delay interactions. However, the extent to which “true” interactions are resultingly lost—and the impact this has on the performance of functional connectivity metrics as biomarkers—remains unknown. Here, we show that most cortico-cortical functional connectivity occurs with zero- or near-zero phase delay, even where such connectivity is unlikely to be artefactual. Including, rather than excluding, zero-phase-delay connectivity increases the reliability, convergence with neurobiology (structure–function concordance, homotopic interhemispheric connectivity, and age-related connectivity changes), and prognostic ability of functional connectivity metrics. We find that excluding zero-phase-delay connections penalises functional connectivity strength between the strongest structurally connected regions: stronger structural connections lead to functional connections with phase delays closer to zero, mediated by a shorter signal propagation time. Our findings challenge generally accepted assumptions that zero-phase-exclusive methods are superior to zero-phase-inclusive methods.

Audiovisual integration (AVI) is linked to the development of several cognitive abilities, rendering it a vehicle to better understand and anticipate a wide range of sequelae associated with preterm (PT) birth. In the present study, we aimed to complement the scarce literature on PT birth and AVI early in life, by investigating neonatal brain networks encompassing areas reported in the infant AVI literature. We used data from the Developing Human Connectome Project (http://www.developingconnectome.org/) to build two localized and one whole-brain connectomes from functional and structural connectivity data. Using graph analysis on a multiplex structural–functional brain network, we investigated the association between prematurity and (1) the edges present in functional and structural networks, (2) the integration and segregation properties of functional and structural networks, and (3) the inter-layer assortativity. We found substantial differences between PT and full-term (FT) neonates in the edges of the structural network at the whole-brain level and in one localized connectome. Across parcellation schemes, associations between prematurity and network efficiency were different, but similar inter-layer metrics were observed. In an exploratory analysis, we further showed lower functional connectivity strength in PT neonates. These findings are discussed in the light of perinatal brain developmental trajectories and deepen our understanding atypical AVI abilities in PT infants. More generally, the present work contributes to our understanding of whole-brain network development by investigating functional–structural coupling from a network neuroscience perspective.