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.

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.

Focused ultrasound can be delivered through the temporal window to modulate heterogeneously located brain areas. Acoustic simulations allow for safety assessments when dynamically targeting brain structures, but the mismatch between simulation and measured focal pressure can vary across the steerable range due to mechanically inaccurate assumptions made about the skull and transducer. Here, we describe efficient methods for simulation-measurement calibration using axisymmetric projections and sparse sampling across a 3D steerable subspace encompassing deep brain targets across 157 subjects. To address the simulation-reality mismatch in skull transmission, we used the measured and predicted pressure values through eight human temporal window fragments to derive an optimized bone attenuation coefficient. Collectively, the calibration framework and optimized temporal window coefficients can be used broadly across studies to improve the accuracy of reporting and dependent safety assessment for personalized neuromodulation treatments.

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

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.

Emerging non-invasive brain stimulation modalities, including transcranial focused ultrasound and transcranial interferential stimulation, offer the promise of safely and non-invasively modulating deep brain structures. Accurate targeting of these regions typically involves subject-specific MRI, limiting widespread deployment. Here, we introduce an MRI-free, neuronavigation-free method for localizing deep brain targets using only three simple scalp measurements. These measures define an affine transformation that maps the MNI152 standard head model to an individuals head geometry. We evaluated our approach on 50 healthy adults, comparing our model{square}predicted coordinates against ground-truth coordinates obtained via MRI-based nonlinear normalization. Across ten deep brain targets, our method achieved a mean localization error of 3.82 mm demonstrating a more accessible and cost-efficient alternative than MRI- or neuronavigation-based approaches.

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.

Group-averaged network definitions, commonly used in developmental functional connectivity research, limit our understanding of how network topography may change with age and can lead to inaccurate estimates of age effects on intra- and inter-network functional connectivity. Here, we collected a precision fMRI dataset from 24 parent-child pairs (children 6 to 8 years, 13 females; adults 33 to 47 years, 12 females) during passive video viewing and derived individual template-matched functional network maps from 60 minutes of motion-censored data per participant. Overall, large-scale network architecture was broadly shared between children and adults, with no age-effects observed for network-level surface area, few age differences in network boundaries and generally lower assignment confidence in children. Considering inter-individual similarity in network topography, association networks showed stronger within-family similarity and no overall effect of age on similarity of networks between pairs of individuals. We further asked how individualized network definitions might impact estimates of age effects on dense functional connectivity. While all approaches pointed to greater within-network functional connectivity in adults, we found that individualized approaches had larger age effects and lower sensitivity to head motion. Together, our results suggest that, relative to adults, pre-adolescent children show reduced network assignment confidence and weaker within-network connectivity, but limited differences in network borders and size, and underscore the value of individualized mapping for increasing sensitivity to age effects.

Aperiodic, 1/f-like EEG activity is increasingly used to investigate neural dynamics in cognitive and clinical research, with applications ranging from experimental manipulations to clinical biomarker development. However, these applications assume, without systematic testing, that aperiodic parameters primarily reflect neural activity rather than non-neural sources. To address this critical gap, we systematically quantified how data quality and physiological artifacts influence aperiodic parameter estimation across two independent datasets (N=99 and N=103) using complementary methodological approaches. Poor data quality and ocular artifacts significantly increased aperiodic offsets and exponents, whereas muscular artifacts showed opposite effects. These spatially widespread effects reached magnitudes comparable to neurophysiologically meaningful group differences and were only partially attenuated by state-of-the-art preprocessing. Cardiac artifacts showed modest effects limited to the offset. Critically, regression-based statistical correction effectively mitigated artifact-induced biases. Our findings establish that differential artifact rates between experimental conditions or populations can substantially bias neural interpretations of aperiodic parameters and provide methodological guidelines for ensuring valid inference.

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.

Mesoscale, layer-specific functional MRI (fMRI) enables noninvasive access to cortical microcircuitry, yet widespread adoption has been constrained by a reliance on ultra-high field ([&ge;]7.0 T) systems and proprietary pulse sequences. To bridge this gap and enhance accessibility, we developed an open-source framework at 5.0 T for mapping laminar brain activity. This framework integrates a Pulseq-based 3D vascular space occupancy (VASO) sequence with an end-to-end data acquisition and analysis pipeline. At matched sub-millimeter resolution (0.8 mm in-plane), the Pulseq-based 3D implementation increased slab coverage by [~]1.82-fold and improved temporal signal-to-noise ratio by [~]1.50-fold relative to a vendor-provided 2D-VASO sequence. Validated using a finger-tapping paradigm, individual cerebral blood volume-weighted (VASO) laminar activation profiles consistently revealed the canonical "double-peak" pattern, with distinct superficial and deep peaks in the primary motor cortex. These profiles exhibited excellent cross-visit reliability (r = 0.80), and peak depths showed good spatial reliability (ICC = 0.69 for deep layers; ICC = 0.58 for superficial layers). Between-subject reproducibility was high (r = 0.86). Deploying the identical Pulseq protocol at an independent imaging site reproduced the characteristic double-peak laminar profiles (r = 0.63). At the group level, 5.0 T laminar profiles closely matched established 7.0 T findings, robustly resolving both deep and superficial peaks despite the lower field strength. Notably, for each participant, a single 13-minute VASO run was sufficient to resolve reliable laminar activation patterns that exhibited high consistency with multi-run averages (r = 0.78), highlighting the potential for high-throughput population studies or clinical research settings. The Pulseq-based 3D VASO sequence file, image reconstruction pipeline, and data analysis scripts are openly available to facilitate the adoption of this framework. This work establishes a practical route towards more accessible and reproducible mesoscale fMRI for studying human laminar functional architecture.

The coupling between brain excitatory activity and positive blood oxygen level-dependent (BOLD) responses is well-established. Although often associated with inhibition, negative BOLD remains partially understood. Moving away from neurovascular coupling, apparent diffusion coefficient (ADC)-fMRI provides a more direct measure of excitatory activity, possibly mediated by transient cellular deformations. While decreases in ADC align with positive BOLD, the possible translation of negative BOLD into positive ADC has not been investigated in humans. Diffusion-weighted fMRI (dfMRI) combines vascular and microstructural contributions. Using interleaved subthreshold transcranial magnetic stimulation (TMS)-fMRI on the primary motor cortex (M1), we induced negative BOLD responses in contralateral M1 and primary somatosensory cortex (S1). This was accompanied by a negative dfMRI response, but no ADC-fMRI response, indicating minimal microstructural fluctuations. In ipsilateral M1/S1, no BOLD response was detected while dfMRI revealed a positive cluster, suggesting sensitivity to subtle neural activity. These findings provide new insights into vascular and neuronal responses underlying subthreshold TMS and negative BOLD.

NEXI is a gray matter (GM) microstructural model designed to probe brain tissue microstructure in vivo using diffusion MRI. NEXI describes GM as two exchanging Gaussian compartments - neurites, modeled as randomly oriented, infinitely long sticks, and the extracellular space - allowing the estimation of biophysically interpretable parameters related to neurite microstructure and intercompartmental exchange. While modeling cell processes as sticks and each compartment as Gaussian are common assumptions for brain biophysical models of diffusion, neurite structural irregularities and the presence of somas, particularly in GM, may violate them and bias NEXI parameter estimates. Furthermore, the barrier-limited exchange assumed in the Karger model that underlies NEXI may also be violated in realistic conditions. Therefore, in this work, we evaluate NEXIs accuracy in numerical substrates that incorporate realistic GM features and membrane permeability. To this end, we generated several GM-like substrates with neurite beading, undulation, orientation dispersion, and somas across a range of membrane permeabilities. Diffusion signals were generated with Monte Carlo simulations of water diffusion and subsequently fitted with NEXI. Overall, NEXI accurately recovered exchange times across permeability levels and successfully disentangled exchange effects from other microstructural features, showing only minor bias in estimates from the realistic geometries. These results support its potential for in vivo GM microstructure mapping and studies of brain disorders.

Over the past two decades, open-source research software such as SPM, AFNI and FSL formed the substrate for advancements in the brain functional magnetic resonance imaging (fMRI) field. The spinal cord fMRI field has matured substantially over the past decade, yet there is limited research software tailored for processing cord fMRI data that has distinct noise sources, unique challenges, niche processing requirements and special needs. Spinal cord fMRI data analysis is a different beast, involving specialized pre- and post-processing steps due to the cords unique anatomy and higher distortions/physiological noise, thus requiring extensive and careful quality assessment. Building upon 10+ years of research and development, we present Neptune - a user-interface-based MATLAB toolbox. With 30,000+ lines of in-house code, it is designed to be easy to use and does not require programming knowledge. Neptune builds on our previously published 15-step pre-processing pipeline (Barry et al., 2016) and presents a 19-step pipeline with new processing steps, and enhancements to existing steps. Neptune has a 4-step post-processing pipeline aimed at fMRI connectivity modeling. It generates extensive and novel quality control visuals to enable a thorough assessment of data quality, and displays them in an elegant webpage format. We demonstrate the utility of Neptune on our 7T data. Certain features of the popular Spinal Cord Toolbox (SCT) are integrated into Neptune, and users can import/export between Neptune and other software such as FSL and SPM. The availability of this open-source, easy-to-use software will benefit the spinal cord fMRI community, and also tip the cost-benefit balance for brain fMRI researchers to invest in learning new software to conduct important neuroscientific and clinical research using spinal cord fMRI.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is an important measure of cerebrovascular health. Accurate CVR estimation requires accounting for the time required for the vasoactive stimulus to reach each brain region and the time it takes for local arterioles to modulate cerebral blood flow. The temporal search range used to calculate this spatially varying offset can substantially impact CVR estimates, and the appropriate search range may vary across populations, acquisition protocols, and even brain regions. Here, we present an iterative approach for automatically determining the appropriate maximum shift, using breath-hold fMRI data acquired in a cohort of stroke survivors. This approach selectively expands the delay search range only for voxels with estimated delays at the boundary (i.e., near the minimum or maximum shift) until the estimated delay is no longer constrained or a predefined value is reached. In the context of stroke, this approach significantly increased the number of voxels with statistically significant CVR among those initially at the boundary. It also resulted in CVR polarity reversals in voxels originally at the early-response boundary and amplified negative CVR values in voxels originally at the late-response boundary, suggesting that using an iterative maximum shift can critically impact CVR interpretation. This approach is broadly applicable beyond stroke, but careful parameter tuning is required, as illustrated by our demonstration of the parameter tuning process for a participant with Moyamoya disease. Together, these findings suggest that iterative delay correction allows for improved CVR assessments in clinical populations.

Interest in broadband aperiodic brain activity (1/f phenomenon) has increased exponentially over recent years, partly fueled by the development of tools to parameterize it (i.e., estimate its offset/intercept and exponent/slope) using the M/EEG power spectrum. Broadband aperiodic activity needs to be separated from narrowband periodic activity before its parameters are computed. A popular method, the fooof toolbox (Donoghue et al., 2020), is based on the data-driven detection of narrowband-periodic peaks, whose maximum number is set by the user. While increasing analytic flexibility, variability in the number of detected peaks may increase sensitivity to noise and reduce the reliability of aperiodic parameter estimates and the power of analytic pipelines. Here, we present an investigation of the effects of analytic choices (e.g., number of peaks, spectral estimation method) on metrics indicating the adequacy of spectral parametrization. These include the internal consistency (odd-even reliability) of aperiodic estimates, the number of outliers generated, and their ability to detect effects. Across two different data sets (resting state and task-based) we found a decrease in the reliability of intercept and slope estimates as more peaks were allowed to be extracted. To ameliorate this problem, we propose a theory-driven modification of fooof labelled censored regression, whereby a theory-driven range of frequencies expected to contain periodic activity is removed from all spectra, and the remaining power values are regressed on the remaining frequencies to obtain parameter estimates. This method shows more reliable and robust estimates compared to fooof, while avoiding overfitting.