Aperture Neuro

A particularly challenging form of missing data is structured missingness, where sets of subjects and variables consistently have missing data. For tabular data from sub-studies or modalities, structured missingness can come from non-participation in followup studies, which creates large blocks of missing data. Canonical Correlation Analysis (CCA) is a multivariate modelling tool commonly used to link two different set of variables, and in neuroimaging has typically been used to find associations between imaging and non-imaging variables. Motivated by CCA, we propose a new method for covariance estimation from incomplete data that handles data with a mix of structured and unstructured missingness, assuming Missing at Random (MAR). Our proposed method is compared to existing methodology by way of evaluation on simulated data and on real data from subjects in the UK Biobank brain imaging cohort.

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the "rating widget" - a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged. RELATED LINKSO_ST_ABSKey reference(s) using this protocolC_ST_ABSEsteban, O. et al. (2017), PLoS ONE 12(9): e0184661. [10.1371/journal.pone.0184661] Esteban, O. et al. (2019), Sci Data 6, 30. [10.1038/s41597-019-0035-4] Esteban, O. et al. (2020), Nat Prot 15, 2186-2202. [10.1038/s41596-020-0327-3] Provins, C. et al. (2023), Front. Neuroinform. 1, 2813-1193. [10.3389/fnimg.2022.1073734] Bissett P. et al. (2024) Sci Data 11: 809. [10.1038/s41597-024-03636-y] Key data used in this protocolAmsterdam Open MRI Collection: Population Imaging of Psychology1 (AOMIC-PIOP1; ds002785 [https://openneuro.org/datasets/ds002785]).

UK Biobank (UKB) brain imaging data is a one-of-a-kind resource for studying the links between the brain and demographic-, lifestyle- and genetic data. When establishing such links, it is crucial to account for confounding effects caused by the acquisition of fMRI images, as well as demographic confounding factors. UKB brain imaging confounds are constructed through variable selection by the proportion of variance explained in the Imaging Derived Phenotypes (IDPs), from tens of thousands of possible confounds. The current implementation of this pipeline is very computationally intensive and has a large memory footprint, largely due to the varying patterns of missing data in IDPs. This makes it impractical for many users of UK Biobank brain imaging data. We propose a fast and memory efficient multivariate pipeline for constructing imaging confounds using mean imputation combined with a bias-corrected estimator of R2, the proportion of confound variance explained in an IDP. Building on this, we also improve the pipeline in order to better select confounds that explain unique variance in IDPs, and non-imaging variables of interest, so called nIDPs. The new implementation leads to a more compact set of confounds that explains roughly the same amount of variance, and runs in around 1 hour on a single CPU.

Large scale research studies combining magnetic resonance imaging data generated at multiple sites on multiple vendor platforms are becoming more commonplace. The Ontario Neurodegenerative Disease Research Initiative (ONDRI - http://ondri.ca/), a project funded by the Ontario Brain Institute (OBI), is a recently established province-wide natural history study, which has recruited more than 500 participants from neurodegenerative disease groups including amyotrophic lateral sclerosis, fronto-temporal dementia, Parkinsons disease, Alzheimers disease, mild cognitive impairment, and cerebrovascular disease (previously referred to as the vascular cognitive impairment cohort). Because of its multi-site nature, all captured data must be standardized and meet minimum quality standards to reduce variability. The goal of the ONDRI imaging platform is to maximize data quality by implementing vendor-specific harmonized MR imaging protocols (consistent with the Canadi-an Dementia Imaging Protocol - http://www.cdip-pcid.ca/), monitoring protocol adherence, qualitatively assessing image quality, measuring signal-to-noise and contrast-to-noise, monitoring system stability, and applying corrections based on the analysis of images from two different phantoms regularly acquired at each site. To maximize image quality, this work describes the use of various automatic pipelines and manual assessment steps, integrated within an established informatics and databasing platform, the Stroke Patient Recovery Research Database (SPReD) built on the Extensible Neuroimaging Archive Toolkit (XNAT), and contained within the Brain-CODE (Centre for Ontario Data Exploration) framework. The purpose of the current paper is to describe the steps undertaken by ONDRI to achieve this high standard of data integrity. Data have been successfully collected for the past 4 years with the pipelines and assessments identifying deviations, allowing for timely interventions and assessment of image quality.

The UK Biobank Imaging Study, with its dedicated cardiovascular magnetic resonance (CMR) sub-study, has re-defined the scale and scope of cardiovascular research, generating high-quality imaging data in 100,000 participants with linkage to rich genetic, demographic, lifestyle, and clinical data. The resource has enabled transformative discoveries across genomics, epidemiology, and biomedical engineering, and has served as a global blueprint for population imaging studies. Its success has been accelerated by an equitable data access model that fosters international collaboration. Looking ahead, efforts should focus on harmonisation across cohorts, adherence to rigorous methodological standards, and multidisciplinary collaboration to drive meaningful clinical translation - whether through direct improvements in patient care or experimental validation of imaging-derived insights. The UK Biobank CMR experience illustrates the power of large-scale imaging cohorts and sets a benchmark for future initiatives aimed at improving cardiovascular health through integrated, collaborative science. This paper provides an overview of the UK Biobank and its CMR sub-study, systematically reviews key publications, discusses methodological considerations, and highlights important future directions.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows - encompassing data onboarding, preprocessing, and analysis - in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

BackgroundManganese-enhanced magnetic resonance imaging (MEMRI) is a powerful approach for mapping brain-wide neural activity and axonal projections in vivo. Yet standardized computational frameworks for voxel-wise and atlas-based characterization of brain states across large experimental cohorts remain limited. New methodHere, we present methodological advances for preprocessing and statistical analysis of MEMRI datasets to support scalable, reproducible cohort-level analyses. Quality assurance metrics were developed to evaluate images, cohort-level anatomical alignment, and intensity normalization. Using simulated data, we optimized smoothing, effect-size, and cluster-size thresholds to balance sensitivity and specificity in voxel-wise statistical mapping. We developed InVivoSegment software to apply to our new InVivo Atlas for segmentation of MEMRI data and interpretation of brain-wide activity. ResultsQuality assurance analyses established benchmarks for Mn(II)-induced signal- and contrast-to-noise evaluation, precise cohort-level alignment at 100 m isotropic resolution, and robust intensity normalization. Balanced accuracy and Youdens J statistics were calculated from simulated true positive and noise-only intensities, which defined optimal parameters for smoothing kernel, cluster-size and effect-size thresholds during voxel-wise mapping. Segmentation of simulated data demonstrated reliable transformation of voxel-wise results into regional summaries and identified secondary thresholds that minimize noise-driven artifacts. Comparison with existing methodsApproach to optimize correction parameters for statistical mapping using simulated images improves voxel- and segment-wise sensitivity compared to FDR/FWE-based correction procedures. ConclusionsThese methodological advances enable scalable, reproducible, brain-wide quantification of longitudinal changes in MEMRI studies, strengthen mechanistic investigation of brain-state dynamics relevant to human health, and provide broadly applicable tools for neuroimaging analyses beyond MEMRI applications. HighlightsO_LIQuantitative assurance of image quality complements visual assessment for cohort-level batch processing. C_LIO_LIOptimization of parameters using simulated noise-only images with and without investigator-embedded signal for voxel-wise mapping. C_LIO_LIA new software, "InVivoSegment" together with a labeled atlas, automates reliable user-friendly segmentation of voxel-wise data. C_LIO_LIMethodological advances in MEMRI data processing and computational analyses support scalable voxel- and segment-wise quantification of brain-wide neural activity. C_LI

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as "glass-box" methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

MICAFlow is a fully automated MRI preprocessing pipeline designed to translate advanced neuroimaging workflows from research into routine clinical practice. The pipeline emphasizes speed, robustness, and ease of use, focusing on structural and diffusion MRI. Key innovations include a Label-Augmented Modality-Agnostic Registration (LAMAReg) technique driven by deep learning segmentations for reliable cross-modal alignment, integration of state-of-the-art distortion corrections, and adherence to reproducible standards (Snakemake workflow, BIDSApp specifications). We describe the design of MICAFlow and evaluate its performance across heterogeneous datasets. First, accessibility: MICAFlow processes a multimodal MRI exam in minutes with clinically accessible hardware and without requiring GPU access, making it feasible for same-day clinical use. Second, registration accuracy: LAMAReg achieves cutting-edge multi-modal registration accuracy, yielding accurate alignment of diffusion MRI, FLAIR, and intra-subject T1-weighted images while remaining generally robust to common artifacts. Third, data reliability: Using identifiability, we show MICAFlow maintains consistent performance across diverse datasets, including subjects with pathology, and is closely comparable to contemporary pipelines. In sum, MICAFlows combination of machine learning and efficient workflows produces research-grade data quality with clinical-grade speed. This work demonstrates that advanced MRI preprocessing can be done fast and robustly, helping close the gap between research neuroimaging and broad clinical application of quantitative MRI techniques. The source code for MICAFlow is available here: https://github.com/MICA-MNI/micaflow, and for LAMAReg here: https://github.com/MICA-MNI/LAMAReg.

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.

1.Quantitative neuropathology has advanced through whole-slide imaging and digital histology platforms. Yet, these measurements rarely align with neuroimaging coordinate frameworks that may be useful for spatial modeling and other applications. QNPtoVox, short for quantitative neuropathology to voxels, is a reproducible, modular pipeline that transforms quantitative metrics generated by digital pathology software (HALO) into voxel-based maps registered to a standard common coordinate (MNI) template. The workflow integrates digital histopathology, gross tissue photography, ex-vivo MRI, and nonlinear registration to generate spatially standardized 3D pathology representations. This Methods article provides a complete procedural description, including required materials, step-wise instructions, operator-dependent checkpoints, expected outputs, reproducibility evaluation, and troubleshooting. QNPtoVox enables voxel-level integration of neuropathology with neuroimaging tools, unlocking existing histopathology datasets for computational modeling and cross-cohort harmonization.

Simultaneous TMS-fMRI holds significant opportunities for advancing basic and translational neuroscience. However, current configurations face technical limitations, particularly the need to accommodate TMS hardware within the MRI environment. Previous solutions have used low numbers of radio-frequency (RF) channels, limiting fMRI data quality and whole-brain coverage. Here, we introduce a novel 22-channel "Taco" TMS-fMRI configuration that re-purposes flexible RF coils, wrapping them around both the participants head and the TMS coil to preserve whole brain signal reception. Guided by precision fMRI principles, we optimized acquisition protocols to achieve to achieve high temporal signal-to-noise ratio (tSNR) across the cortex. Data from three pilot participants demonstrate robust signal quality, including in regions proximal to the TMS coil. This setup offers a relatively simple and cost-effective approach to integrating precision fMRI into TMS-fMRI research.

In vivo estimation of cerebrospinal fluid (CSF) velocity is crucial for understanding the glymphatic system and its potential role in neurodegenerative disorders such as Alzheimers disease and Parkinsons disease. Current cardiac or respiratory gated approaches, such as 4D flow MRI, cannot capture CSF movement in real time due to limited temporal resolution and in addition deteriorate in accuracy at low fluid velocities. Other techniques like real-time PC-MRI or time-spatial labeling inversion pulse are not limited by temporal averaging but have limited availability even in research settings. This study aims to quantify the inflow effect of dynamic CSF motion on functional magnetic resonance imaging (fMRI) for in vivo, real-time measurement of CSF flow velocity. We considered linear and nonlinear models of velocity waveforms and empirically fit them to fMRI data from a controlled flow experiment. To assess the utility of this methodology in human data, CSF flow velocities were computed from fMRI data acquired in eight healthy volunteers. Breath holding regimens were used to amplify CSF flow oscillations. Our experimental flow study revealed that CSF velocity is nonlinearly related to inflow effect-mediated signal increase and well estimated using an extension of a previous nonlinear framework. Using this relationship, we recovered velocity from in vivo fMRI signal, demonstrating the potential of our approach for estimating CSF flow velocity in the human brain. This novel method could serve as an alternative approach to quantifying slow flow velocities in real time, such as CSF flow in the ventricular system, thereby providing valuable insights into the glymphatic systems function and its implications for neurological disorders.

Local connectivity analyses in fMRI such as the Vogt-Bailey Index, investigate the prevalence of co-fluctuations in the time-series of adjacent voxels. While there have been in silico assessments of the VB Index, this technique has not yet been assessed in vivo. This study has two aims: first, to assess the VB Index using a task paradigm with well established a priori expectations on the brain region predominantly responsible for executing this task to determine whether the VB Index highlights this area. Second, we investigate if, and how, the spatial resolution of the sequence protocols employed, with their inherent effects on the signal-to-noise ratio, affect the resultant VB maps. A cohort of 10 research volunteers underwent fMRI acquisitions utilising a block design finger tapping experiment. Each volunteer was scanned with three sequence protocols, with all parameters equivalent except for the volume of the voxels. The resulting parametric maps derived using the VB Index were compared with those obtained from the conventional General Linear Model approach. Particular emphasis was placed on the identification of the hand portion of the motor homunculus. Across sequence protocols, the VB Index consistently identified elevated local connectivity in, qualitatively, the same portion of the motor cortex as that yielded by the General Linear Model based on the task paradigm. However, the VB Index also detected elevated local connectivity outside the motor cortex while the General Linear Model results were mostly restricted to the motor cortex. The consistently high VB Index, across sequence protocols, in the cortical region associated with an fMRI task paradigm, despite the approachs agnosticism to that task, provides support for the biological relevance of such local connectivity measures.

PurposeStreamlined-qBOLD (sqBOLD) is a refinement of the quantitative BOLD (qBOLD) technique capable of producing non-invasive and quantitative maps of oxygen extraction fraction (OEF) in a clinically feasible scan time. However, sqBOLD measurements of OEF have been reported as being systematically lower than expected in healthy brain. Since the qBOLD framework infers OEF from the ratio of the reversible transverse relaxation rate (R2') and deoxygenated blood volume (DBV), this underestimation has been attributed the overestimation of DBV. Therefore, this study proposes the use of an independent measure of DBV using hyperoxia-BOLD and investigates whether this results in improved estimates of OEF. MethodsMonte Carlo simulations were used to simulate the qBOLD and hyperoxia-BOLD signals and to compare the systematic and noise related errors of sqBOLD and the new hyperoxia-qBOLD (hqBOLD) technique. Experimentally, sqBOLD and hqBOLD measurements were performed and compared with TRUST (T2 relaxation under spin tagging) based oximetry in the sagittal sinus. ResultsSimulations showed a large improvement in the uncertainty of DBV measurements leading to a much improved dynamic range for OEF measurements with hqBOLD. In a group of ten healthy volunteers, hqBOLD produced measurements of OEF in cortical grey matter (OEFhqBOLD = 38.1 {+/-} 10.1 %) that were not significantly different to TRUST oximetry measures (OEFTRUST = 40.4 {+/-} 7.7 %), whilst sqBOLD derived measures (OEFsqBOLD = 16.1 {+/-} 3.1 %) were found to be significantly different. ConclusionThe simulations and experiments in this study demonstrate that an independent measure of DBV provides improved estimates of OEF.

Structural MRI provides a noninvasive window into brain morphology, yet the reproducibility and interpretability of morphometric analyses remain limited by inconsistent preprocessing, variable spatial alignment, and heterogeneous feature construction. We introduce MORPH2DIAG, a fully automated, atlas-free morphometric pipeline that integrates standardized preprocessing, tissue segmentation, spatial normalization, data-driven subtyping, and machine- and deep-learning classification within a single, modular framework. The pipeline performs intensity normalization, morphological cleanup, PCA-informed affine alignment, isotropic rescaling, and Gaussian Mixture Model (GMM) segmentation to generate quantitative gray-matter (GM), white-matter (WM), and cerebrospinal-fluid (CSF) maps. Global tissue fractions were used to derive latent neuroanatomical subtypes via unsupervised K-means clustering, revealing progressive GM-CSF gradients consistent with patterns commonly observed along normative-to-atrophic structural continua observed in neurodegeneration. To capture finer-grained spatial heterogeneity, a voxel-wise K-means parcellation yielded parcel-level intensity means and variances that served as regional morphometric descriptors. These global and parcel-level features were integrated into a unified evaluation suite comparing classical machine learning models (Random Forests, Logistic Regression, XGBoost) with lean, deep, and hybrid multilayer perceptrons (MLPs) trained using focal loss, label smoothing, stochastic weight averaging, and nested cross-validation with PCA-based dimensionality reduction. Across methods, the hybrid MLP achieved the highest macro-F1 and balanced accuracy, demonstrating strong discriminative performance for the discovered morphometric subtypes. Collectively, MORPH2DIAG establishes a fully automated, atlas-free framework that unites unsupervised structural subtype discovery with interpretable machine and deep learning, providing a reproducible foundation for MRI-based morphometric profiling and automated detection of neurodegenerative-like patterns.

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Accurate brain extraction is a critical step in the analysis of rodent head magnetic resonance imaging (MRI) data. However, current methods often encounter difficulties in handling the diverse range of imaging setups, resolutions, and experimental conditions that are commonly found in this field. Based on the Segment Anything Model (SAM), we introduce here SAMson (SAM for Segmentation Of Neuroimages), an automated tool for robust rodent brain extraction. SAMson integrates a bounding box generator and a mask prediction pipeline, offering fully automated and semi-automated modes to address varying experimental complexities. The performance of SAMson was evaluated using three multi-centre rodent MRI datasets annotated at the pixel level, which differed in terms of acquisition parameters, resolution, and animal age groups. SAMson demonstrates superior performance to existing methods, including BET, RBM, and BEN, in terms of segmentation accuracy, with Jaccard indices exceeding 90% across datasets. The semi-automated mode demonstrates particular efficacy in challenging scenarios, including low-resolution images and cases requiring refined mask precision. In contrast to conventional volumetric techniques, SAMson identifies errors at the level of individual slices, thereby enabling rapid and targeted correction when needed. By providing open-source access, SAMson aims to support large-scale research workflows and advance translational neuroscience. The curated data can be downloaded from https://doi.org/10.20350/digitalCSIC/17000, and the code is available at https://github.com/CanalsLab/SAMson.

Functional MRI (fMRI) studies discard the initial volumes acquired during the approach of the magnetization to its steady-state value. Here, we leverage the higher temporal signal-to-noise ratio (tSNR) of these initial volumes to increase the sensitivity of event-related fMRI experiments. To do this, we introduce Acquisition Free Periods (AFPs) that allow for the full recovery of the magnetization, followed by task or baseline acquisition blocks (AB) of fMRI volumes. An appropriately placed stimulus in the AFP produces a Blood Oxygenation-Level-Dependent (BOLD) response that peaks during the initial high tSNR phase of the AB, yielding up to a [~]50% reduction in the number of trials needed to achieve a given statistical threshold relative to conventional fMRI. The silent AFP can be exploited for the presentation of auditory stimuli or uncontaminated electrophysiological recording and its variable duration allows aperiodic stimulus or response-locked signal averaging as well as gating to physiology or motion.