Imaging Neuroscience

Inscapes is a low demand abstract animation used as an alternative to eyes open rest in neuroimaging studies, particularly with pediatric and clinical populations prone to head motion. Although prior work has established that functional connectivity patterns during Inscapes closely resemble those during rest, no study has examined whether the two conditions differ in aperiodic neural activity, a broadband feature of the power spectrum linked to excitation/inhibition balance. Here we used magnetoencephalography (MEG) in 54 healthy adults to compare spectrally parameterised aperiodic and periodic measures between eyes open rest and Inscapes viewing (visual component only, without audio). At the sensor level, both the aperiodic exponent and offset were significantly higher during rest than during Inscapes across widespread frontoparietal and occipital distributions in both magnetometers and gradiometers. Source level analyses at both the parcellation and vertex levels largely supported these patterns. The pericalcarine cortex was a notable exception, where both aperiodic measures were higher during Inscapes than during rest, indicating a regionally specific reversal in primary visual cortex. These results demonstrate that Inscapes and eyes open rest produce distinct aperiodic spectral profiles, indicating that the two conditions are not interchangeable for analyses involving broadband spectral dynamics or excitation/inhibition balance estimation.

Brain activity comprises both rhythmic (periodic) and arrhythmic (aperiodic) components. These signal elements vary across healthy aging, and disease, and may make distinct contributions to conscious perception. Despite pioneering techniques to parameterize rhythmic and arrhythmic neural components based on power spectra, the methodology for quantifying rhythmic activity remains in its infancy. Previous work has relied on parametric estimates of rhythmic power extracted from specparam, or estimates of rhythmic power obtained after detrending neural spectra. Variation in analytical choices for isolating brain rhythms from background arrhythmic activity makes interpreting findings across studies difficult. Whether these current approaches can accurately recover the independent contribution of these neural signal elements remains to be established. Here, using simulation and parameter recovery approaches, we show that power estimates obtained from detrended spectra conflate these two neurophysiological components, yielding spurious correlations between spectral model parameters. In contrast, modelled rhythmic power obtained from specparam, which detrends the power spectra and parametrizes brain rhythms, independently recovers the rhythmic and arrhythmic components in simulated neural time series, minimising spurious relationships. We validate these methods using resting-state recordings from a large cohort. Based on our findings, we recommend modelled rhythmic power estimates from specparam for the robust independent quantification of rhythmic and arrhythmic signal components for cognitive neuroscience.

A common approach for investigating high-level visual cortex with functional magnetic resonance imaging (fMRI) is to define regions of interest (ROIs) in individual participants using functional activation clusters and anatomical landmarks. Although highly productive, this approach requires manual decisions about which clusters correspond to specific canonical regions, limiting reproducibility and posing challenges in populations with lower signal-to-noise ratios, such as children. The Group-Constrained Subject-Specific (GSS) approach reduces this subjectivity by using group-level parcels to constrain subject-specific functional ROI definition. However, the original GSS parcel set provides limited coverage of the occipital place area (OPA) and does not include more recently characterized scene-selective regions. Here, we introduce an updated and expanded set of GSS parcels for scene-selective cortex. Using a larger adult sample and dynamic scene stimuli, we generated updated parcels for OPA, parahippocampal place area (PPA), and retrosplenial complex (RSC), and for the first time, delineated a parcel for a newly discovered scene-selective region in the superior parietal lobule (superior place area; SPA). We evaluated these parcels in independent adult and pediatric datasets by testing whether they improve cross-subject coverage while preserving functional selectivity. The updated OPA parcel increased cross-subject coverage relative to the original parcel by Julian and colleagues. Moreover, ROIs defined using the updated parcels showed equal or greater scene selectivity across OPA, PPA, and RSC, indicating improved functional ROI definition without sacrificing specificity. Across scene-selective regions, the updated parcels reliably identified scene-selective cortex and reproduced canonical response profiles and in pediatric data. These parcels provide more complete and reliable coverage of the scene-processing network, supporting objective and reproducible ROI definition across adult and pediatric fMRI datasets. HighlightsO_LIExpanded group-constrained parcels improve coverage of scene-selective cortex C_LIO_LIDynamic stimuli yield improved cross-subject overlap for OPA C_LIO_LINew parcel introduced for the scene-selective region in the superior parietal lobule, now called superior place area (SPA) C_LIO_LIUpdated parcels reproduce canonical response profiles in adult data C_LIO_LIParcels reliably identify scene-selective voxels in pediatric datasets C_LI

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

IntroductionPreterm birth is a major risk factor for disrupted brain development and subsequent neurodevelopmental disorders, yet the underlying mechanisms remain poorly understood. Further, typical neuroimaging analyses are particularly challenging in the neonatal brain: data is frequently low quality and a lack of cellular development violates the assumptions relied on by many commonly-used techniques. In this study, we develop and present an advanced diffusion magnetic resonance imaging method to examine the microstructural organization of white matter in a clinically-acquired cohort of premature neonates. MethodsUsing a novel approach that resolves multiple tissue compartments within the brain, we provide highly detailed orientation and quantification of white matter fibers and tissue signal fraction. We also utilize a series of automated segmentation algorithms to identify and measure these metrics across key tracts and subcortical regions. We investigate how these measures relate to postmenstrual age, as well as to clinical factors reflecting neonatal illness severity. ResultsWe report successful segmentation and reconstruction of numerous white matter tracts throughout the neonatal brain. We further demonstrate the utility and functionality of microstructural analysis in a variety of pathologies commonly encountered in the neonatal clinical environment. Our results demonstrate tract-specific developmental trajectories, with early-maturing pathways showing higher microstructural organization. Exploratory analyses suggest that neonatal illness severity has modest, tissue-specific associations with microstructural properties. DiscussionThis work demonstrates that advanced microstructural imaging methods can extract meaningful white matter measurements from clinically-acquired scans, providing a practical framework for studying neonatal brain development in real-world hospital settings. These metrics are able to be calculated at extremely young ages, potentially allowing non-invasive study of vulnerable populations before detailed behavioral or neurological assessments are feasible.

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.

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) enables direct measurement of cortical reactivity via TMS-evoked potentials (TEPs). Interpretation of early TEP components however, is highly sensitive to stimulation and hardware-related artifacts. We identified and characterised a persistent, non-neural step-drift artifact unexpectedly present in recent TMS-EEG recordings from our group. We show that the artifact is distinct from previously described TMS pulse and discharge/decay artifacts and likely reflects a hardware interaction phenomenon. We demonstrated that amplifier settings, but not TMS pulse shape, substantially influenced artifact expression, with DC-coupled recordings with no online high-pass filter reducing step amplitude compared with AC-coupled recordings with a high-pass filter. Simulations additionally revealed that filtering over the step-drift artifact introduced pronounced ringing and edge artifacts, highlighting the need to address this artifact prior to data processing. We propose a processing pipeline incorporating robust polynomial detrending and a modified Butterworth filter with autoregressive extrapolation that minimised TEP distortion in both simulated and real data containing the step-drift artifact. Together, these findings provide practical recommendations for both preventing and correcting step-drift artifacts and underscore the need for formal definition and routine recognition of this artifact to improve reproducibility and data quality in TMS-EEG research.

BackgroundIntrinsic functional coupling at multiple temporal scales is a hallmark of human brain dynamics. Among these coupling modes, slow co-fluctuations of oscillatory amplitudes, termed amplitude coupling, are thought to represent a key organizing principle of the large-scale functional architecture, constraining and gating network activity. Yet, despite extensive correlational evidence, direct causal access to amplitude coupling remains limited, restricting insight into its functional relevance. ObjectivesHere, we investigated whether dual-site amplitude-modulated transcranial alternating current stimulation (AM-tACS) can selectively modulate interhemispheric amplitude coupling in human resting-state networks. MethodsTwenty-eight participants received AM-tACS with a carrier frequency in the beta-band whose amplitude was modulated by low-frequency, scale-free dynamics. By applying dual-site AM-tACS either coherently or incoherently across bilateral parieto-occipital cortices, we tested whether stimulation could systematically enhance or disrupt amplitude co-fluctuations in the electrophysiological aftereffect. ResultsIncoherent AM-tACS significantly reduced interhemispheric amplitude coupling between targeted parieto-occipital cortices, with the strongest effects observed in the stimulated beta-band carrier frequency range. This modulation occurred independently of changes in local power or inter-areal phase coupling, indicating a selective effect of AM-tACS on amplitude-based connectivity. Moreover, reductions in amplitude coupling were correlated with the induced electric field strength, suggesting a dose-dependent relationship between stimulation intensity and coupling modulation. ConclusionsOur findings demonstrate that dual-site AM-tACS can causally and selectively modulate amplitude coupling in the human brain. By establishing causal control over lasting amplitude coupling dynamics, this work provides a methodological foundation for future investigations into the functional and behavioral relevance of amplitude coupling in both healthy and pathological brain states. HighlightsO_LIDual-site AM-tACS selectively modulates amplitude coupling in humans C_LIO_LIAM-tACS was designed to mimic natural, scale-free amplitude fluctuations C_LIO_LIStimulation effects are spatially confined to interactions between target regions C_LIO_LIE-field strength predicts the change in amplitude coupling, suggesting a dose-response relationship C_LIO_LIAmplitude coupling modulations are not mediated by band-limited power changes C_LI

Much of a typical individuals mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional Magnetic Resonance Imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience-sampling (rt-fMRI-ES). We analyzed blood-oxygenation-level-dependent signals in real time during a wakeful resting state (n=60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external-attention time-locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation compared to other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendationhttps://osf.io/sd4hu (Date of in-principle acceptance: 07/24/2024; under temporary private embargo)

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

This work introduces Brain2VLM, a framework for analyzing how cortical representations align with latent spaces of pretrained diffusion-based vision-language models for brain-to-image reconstruction. While recent approaches achieve strong performance by mapping functional Magnetic Resonance Imaging (fMRI) signals to model latents, the structure of this mapping remains poorly understood. We hypothesize that brain-to-latent alignment is hierarchical, with early visual cortex exhibiting approximately linear correspondence to structural diffusion latents, and higher-order visual areas requiring nonlinear mappings to align with semantic embedding spaces. To test this, we decode diffusion latents and CLIP embeddings from fMRI signals using both linear ridge regression and a nonlinear residual MLP on the Natural Scenes Dataset. Our results reveal that nonlinear decoding provides only marginal improvements for diffusion latents ({approx}{Delta} 0.05 - 0.06 in correlation), but yields substantial gains for semantic embeddings ({Delta}{approx}0.47), significantly improving distributional alignment (MMD: 0.042 vs 0.358). However, increased decoder expressivity can introduce shifts in latent distributions, highlighting a trade-off between prediction accuracy and generative compatibility. Despite using a simple reconstruction pipeline, Brain2VLM achieves strong performance (PixCorr 0.33, CLIP 85%), suggesting that improvements in brain-to-latent alignment play an important role in reconstruction quality alongside generative modeling. These findings provide empirical evidence for hierarchical alignment between cortical representations and model latent spaces, positioning the brain-to-latent interface as a primary bottleneck in brain decoding systems. Our code can be found at https://github.com/adarsh-crafts/Brain2VLM

In animal models, the primary somatosensory cortex (S1) exhibits columnar organisation, where vertically arranged neurons share functional properties. In humans, however, the thinness and folding of S1 have limited non-invasive investigations of such columnar structures. In this study, we aimed to identify columns in human S1 by delivering alternating bursts of 3 Hz and 30 Hz fingertip vibration while acquiring functional MRI time series at 7 Tesla. Using cortical surface modelling, we identified functional patterns in S1 that showed higher reliability, stronger differential responses, and greater statistical sensitivity than those observed in a frontal cortex control region (p = .001-.012 for reliability; p < .001 for differential signal; p = .004-.011 for sensitivity). Laminar analyses revealed depth-consistent frequency preferences in approximately 20-45% of S1 nodes, a pattern compatible with vertically organised functional structure. Although the relative signal difference between 3 Hz and 30 Hz was small (0.14% signal change), frequency tuning was reliably observed. Taken together, these findings reveal functional patterns in human S1 consistent with aspects of columnar-like organisation, providing non-invasive evidence of fine-scale functional architecture. TeaserfMRI reveals highly reliable but modestly selective responses in human S1, consistent with column-like functional organisation.

Big neuroimaging data enable researchers to study subtle structural and functional brain changes and relationships between brain characteristics and genetics, lifestyle, and disease factors. However, substantial effort is needed to minimise technical, non-biological differences between data batches to avoid incorrect inferences. In this study, we address a previously identified bias in UK Biobank FreeSurfer IDPs derived from only the T1 image compared to those using both T1 and T2-FLAIR by treating the bias as a batch effect and using harmonisation approaches. We investigate and characterise this bias through direct within-participant comparison at the image and IDP level, comparing the results with those seen in the wider UKB sample. We then assess different methods of addressing the effect of missing T2-FLAIR, starting from simple linear regression before moving to ComBat, a widely used harmonisation method, testing different approaches for applying ComBat and showing its similarity to simple linear regression. Finally, we examine how ComBat estimates vary with batch and sample size. Our results show clear benefits in using both T1 and T2-FLAIR data in FreeSurfer, as opposed to just the T1, which is more common, with the pial surface fitting being less likely to fail and showing greater biologically plausible inter-subject variability. This is particularly important for cortical thickness IDPs, where T2-FLAIR omission leads to reduced true variability and systematic underestimation, as shown through within-participant repeat testing. We demonstrate that ComBat can address this bias, with its standard use (i.e., applied separately on different IDP categories) showing the best improvement in cortical thickness measures where the bias is strongest, and we find that it is important not to pool ComBat priors across different classes of IDPs. Our proposed version of ComBat with a reference batch (i.e., estimating mean and variance only from data with T2-FLAIR available) performed best in recovering both mean and variance differences between batches across different IDP classes and offers a promising approach for cases where a reference batch is clearly identifiable. While ComBat reliably corrects mean (additive) batch effects with relatively small sample sizes ({approx}30 subjects per batch), we show that its variance (multiplicative) correction is substantially less stable, requiring much larger sample sizes and becoming unreliable when batches are small or imbalanced, or when there is a large variance difference between them.

Dopaminergic (DA) function and basal ganglia neurobiology are central to reward learning, motivation, and cognitive control, and dysregulation of these systems contributes to neuropsychiatric conditions that emerge during development. Adolescence is marked by profound reorganization of DAergic basal ganglia circuitry, yet direct in vivo assessment of the DA system remains limited in youth. Brain tissue iron is a developmentally sensitive marker of DA-related neurobiology that can be measured non-invasively via magnetic resonance imaging (MRI). Iron is an essential co-factor for DA synthesis and a foundational metabolic resource that supports cellular metabolism, myelination, and energetic demands of the basal ganglia. T2*-weighted echo-planar imaging (EPI), collected during functional MRI (fMRI), is sensitive to magnetic susceptibility of non-heme brain iron. Leveraging this property, we demonstrate the validity and broad applicability of an iron-sensitive metric that can be derived from conventional single-echo fMRI: {Delta}R2*. In a longitudinal developmental dataset (N = 151; age range 12-31), {Delta}R2* showed high reliability, strong longitudinal stability, and validity via robust convergence with established quantitative relaxometry-based iron measures (R2* and R2). Critically, {Delta}R2* can be retrospectively estimated from extant fMRI data and derived in large-scale consortium data repositories, demonstrated here in the Adolescent Brain and Cognitive Development (ABCD) baseline cohort (N = 8,366; ages 9-11). We show that {Delta}R2* captures known age-related increases in basal ganglia iron, highlighting neurodevelopmental sensitivity at population-scale. Together, these findings establish {Delta}R2* as a reliable, widely accessible marker of basal ganglia iron, enabling scalable investigation of lifespan trajectories and neuropsychiatric risk in existing and future datasets.

Functional magnetic resonance imaging (fMRI) quality assurance has traditionally relied on static, geometrically regular phantoms that cannot generate the dynamic signal changes fMRI analysis pipelines are designed to detect. Here we present the Signal Generating (SiGn) anthropomorphic brain phantom, a 3D-printed cortical model derived from an individual participants structural MRI, filled with tissue-mimicking agar gels and coupled to a hemin-based infusion system that produces controlled, time-varying T *-weighted signal changes. We validated the phantom across two scanning sessions on a 3 T Siemens MAGNETOM Vida scanner, demonstrating that hemin infusion produced spatially localised activation detectable by standard general linear model analyses. Because the phantoms geometry is derived from real participant anatomy, its functional data can be coregistered and spatially normalised to standard brain templates through the same pipeline applied to human data, enabling end-to-end assessment of how each preprocessing step affects a known ground-truth signal. To support adoption and reproducibility, we openly release the full resource at https://doi.org/10.60809/drum.31411158, including 3D-printable STL model files, tissue-mimicking gel recipes, the BIDS-formatted dataset, preprocessing and analysis scripts, and a containerised reproducibility workflow; the corresponding archival container image is also deposited on Zenodo at https://doi.org/10.5281/zenodo.19495290. This framework is intended to lower the barrier for other groups to fabricate, scan, and analyse an equivalent device on their own hardware, adapt it to specific research questions, and iteratively improve the design, thereby supporting more rigorous and transparent fMRI quality assurance practices across the neuroimaging community.

Consistent, high-quality data is key to the success of fMRI studies given the many confounding factors and undesired signals that contaminate these data. Several quality assurance (QA) metrics exist for fMRI (e.g., temporal signal-to-noise ratio (TSNR), percent ghosting, motion estimates), but none of them leverage relationships between echoes that are part of multi-echo (ME) fMRI acquisitions. Here, we fill this gap by proposing a new QA metric for for ME-fMRI that quantifies the likelihood a given ME scan is dominated by BOLD (Blood Oxygenation Level-Dependent) fluctuations. We refer to this metric as pBOLD; the probability of the signal change being primarily BOLD contrast-dominated. Having an estimate of overall BOLD weighting - both before and after preprocessing - is meaningful because BOLD is the intrinsic contrast mechanism used in fMRI to infer neural activity. We introduce pBOLD to the neuroimaging community by first describing the theoretical principles supporting the metric. Next, we validate pBOLD efficacy using a small dataset (N=7 scans) of constant- and cardiac-gated scans that have distinct levels of contributing BOLD fluctuations. Third, we apply pBOLD to a larger publicly available ME dataset (N=439 scans), to evaluate six different pre-processing pipelines, and show how pBOLD provides complementary information to TSNR. Our results show that ME-based denoising increases both pBOLD and TSNR relative to basic denoising; however, including the global signal (GS) as a regressor only improves TSNR, but worsens pBOLD. Further analyses looking at the BOLD-like characteristics of the GS and its relationship to cardiac and respiratory traces suggest that the observed decrease in pBOLD is likely due to a decrease in BOLD fluctuations of neural origin contributing to the GS, and not due to contributions from other physiological BOLD fluctuations (i.e., respiratory and cardiac function). Finally, we also demonstrate how pBOLD can be applied as a data quality metric, by showing how higher pBOLD results in better ability to predict phenotypes based on whole-brain functional connectivity matrices.

There is growing interest in identifying brain function underlying adolescent cognition, personality, and psychopathology. One promising approach is Precision Functional Mapping (PFM) of MRI functional connectivity, a data-intensive method for characterizing individualized brain networks. Foundational studies suggest that PFM can detect stable, task-responsive, and clinically relevant networks. Studies demonstrate that both functional connectivity reliability and network stability improve with increasing data quantity, although benchmark estimates vary across populations, preprocessing pipelines, and MRI acquisition approaches. Accordingly, it is important to understand how PFM performs in adolescent populations and with multi-echo fMRI acquisition. In a case study of eight youth (ages 10-17), we applied PFM to 80-minutes of combined resting-state and task-based fMRI. The resulting networks were highly modular, consistent with adult templates, and without evidence of structural registration artifacts. Functional connectivity reliability compared favorably to prior single-echo studies, with multivariate similarity and ICC estimates showing early stabilization around 10-15 minutes despite continued improvement with additional data. Trait-like stability increased gradually with acquisition time and a Bayesian algorithm (MS-HBM) demonstrated higher stability than Infomap. Across algorithms, stability was greatest in sensory networks (somatomotor, auditory, visual). Furthermore, when evaluating task-based responses to threat and attention paradigms, only the auditory network consistently benefited from individualized mapping over group template networks. These findings suggest that, with constrained scanning time, PFM is especially effective for characterizing sensory and perceptual networks in adolescents. Bridging the methodological divide between deeply sampled individual cases and large-scale developmental studies will require further innovation and validation.

The gastric-brain axis is a burgeoning field of neuroscience; however, inferences from neuroimaging research are often constrained by the high dimensionality of methodological choices potentially leading to disparate outcomes. This study addresses such concerns by performing a multiverse analysis of gastric-brain coupling in humans. We systematically evaluated 1,728 unique analytic pipelines using electroencephalography (EEG) and electrogastrography (EGG) data to quantify the robustness of observed gastric-brain coupling. Our results reveal that whilst analytic decisions influence the magnitude of observed coupling, at the group level the phenomenon remains relatively robust across the parameter space. High inter-individual variance can, however, be observed. Coupling was observed in the alpha, theta, and beta bands, with the latter two bands showing robust coupling across the largest number of electrodes. Robust coupling across frequency bands was primarily seen in medial electrodes, with some left lateral coupling also observed. Overall, these findings suggest that gastric-brain coupling is likely to be a robust physiological feature in healthy participants, providing a stable foundation for future studies.

Studies in rodents and humans using invasive electrophysiology have established that neural replay is a ubiquitous phenomenon in the brain that is associated with a wide range of cognitive functions, including memory, planning and decision making. Yet, invasively recording in humans remains difficult, and hence knowledge about replay in humans remains scarce. Hence, to comprehensively understand replay in humans, we need reliable approaches that can detect it non-invasively. Several main non-invasive approaches have been proposed, but we lack a full comparative validation against known ground truth signals. In this study, we present FASTIMAGES, a benchmark dataset from seventy participants with parallel fMRI (n = 40, previously published) and MEG (n=30) recordings containing known neural sequences evoked by fast visual stimulation as well as functional localizer trials. The neural sequences were elicited by five different visual stimuli shown in sequences at speeds of 132, 164, 228 and 612 milliseconds onset-to-onset intervals. Using this dataset, we investigate two existing statistical methods for sequence detection, namely Temporally Delayed Linear Modelling (TDLM, developed for MEG by Liu et al., 2021) and Slope Order Dynamic Analysis (SODA, developed for fMRI by Wittkuhn & Schuck, 2021). We examine the underlying assumptions of each method, analyse their resulting strengths and weaknesses in application to MEG and fMRI. We demonstrate that both approaches excel in their native modality (TDLM for MEG and SODA for fMRI), with comparable effect sizes given idealized conditions in this benchmark. Cross-modality transfer remains challenging. Finally, the FASTIMAGES dataset provides data with known and clearly expressed sequences and can be used to benchmark and validate future sequence detection methods under idealized conditions.

The human brain is a unique biological space that hosts complex processes unfolding at multiple scales. To study these processes, an abundance of imaging technologies evolved over many decades to produce large-scale, dense mappings of structural and functional features. In parallel, a rich universe of techniques for cellular and molecular biology supplies us with fine-scale, highly specific and reliable measurements in sparse tissue samples. To represent cortical processes integratively across scales, spatial interpolation is necessary for bridging dense and sparse data. The absence of a field consensus for realistic interpolation of features over the whole brain prompts a comprehensive comparison of existing frameworks from the broader scientific literature. Here, we benchmark the performance of multiple deterministic (Inverse Distance Weighting, K-Nearest Neighbours, and Radial Basis Function) and stochastic (Spatially-Weighted Regression, Ordinary Kriging, and Regression Kriging) strategies first with simulated or empirical ground truths. We then demonstrate two use cases with de novo sparse brain data (intracranial EEG and microarray gene expression). In these experiments, we investigate how differences in data characteristics, such as spatial dependency structure and sampling distribution, impact the performance of different interpolation methods. Throughout the results, we consistently find that maps interpolated through spatially-informed stochastic frameworks such as Ordinary Kriging and Regression Kriging are more accurate and biologically realistic across geometric constraints, data modalities, and sampling conditions. This invites continued development of spatially-informed statistical frameworks for analyzing brain data and, more fundamentally, the biological processes that produce them.