BOLD Signal Research Articles

Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment.

Cerebrovascular reactivity and response times describe recent ischemic symptomatology in patients with moyamoya

Abstract Moyamoya is a non-atherosclerotic intracranial steno-occlusive condition that places patients at high risk for ischemic stroke. Randomized trials of surgical revascularization demonstrating efficacy in ischemic moyamoya have not been performed, and as such, biomarkers of parenchymal hemodynamic impairment are needed to assist with triage and evaluate post-surgical response. In this prospective study, we test the hypothesis that parenchymal cerebrovascular reactivity (CVR) metrics in response to a fixed-inspired 5% carbon dioxide challenge correlate with recent focal ischemic symptoms. Hypercapnic reactivity blood oxygenation level-dependent (BOLD) MRI (echo time=35ms; spatial resolution=3.5x3.5x3.5mm) and catheter angiography assessments of cortical reserve capacity and vascular patency, respectively, in moyamoya disease and syndromic participants (n=73) were performed in sequence. Cerebrovascular reactivity uncorrected for response time (CVRRAW) was quantified, and time regression analyses were applied to quantify maximum cerebrovascular reactivity (CVRMAX) and cerebrovascular reactivity response time (CVRDELAY). Symptomatology was categorized by a stroke neurologist by hemisphere: symptomatic (lateralizing ischemic symptoms < 6 months) or asymptomatic (no ischemic symptom history). Values are presented as median [interquartile range]; logistic regression assessed the association of cerebrovascular reactivity metrics with symptoms, controlling for age and sex. A total of 109 hemispheres, including 39 symptomatic and 70 asymptomatic hemispheres, met inclusion criteria. Symptomatic hemispheres displayed reduced CVRRAW (p<0.01) (symptomatic=0.45 [0.28-0.70] z-statistic/ΔEtCO2 vs. asymptomatic=0.67 [0.44-0.98] z-statistic/ΔEtCO2), lengthened CVRDELAY (p<0.001) (symptomatic=47.6 [37.7-57.0] seconds vs. asymptomatic=37.7 [30.4-46.4] seconds), and reduced CVRMAX (p=0.037) (symptomatic=1.31 [0.99-1.94] z-statistic/ΔEtCO2 vs. asymptomatic=1.64 [1.29-2.12] z-statistic/ΔEtCO2). CVRDELAY (p<0.001) was found to be significantly related to age in asymptomatic hemispheres (0.33-unit increase/year). Of assessed measures, the receiver operating characteristic curves suggest that CVRDELAY is associated most closely with recent ischemic symptoms (p<0.001). Findings support that cerebrovascular reactivity metrics are uniquely altered in hemispheres with recent ischemic symptoms, further motivating their utilization as biomarkers of ischemic symptomatology and potential treatment efficacy in moyamoya disease and syndrome.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, 1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal; 2) we propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters. In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method. Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions. This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.

Pharmacological manipulation of neurotransmitter activity induces disparate effects on cerebral blood flow and resting-state fluctuations

Abstract Functional MRI methods can assess aspects of drug-induced brain response. Resting blood oxygenation level dependent (BOLD) fMRI and arterial spin labeling (ASL) perfusion MRI indirectly measure brain function through the coupling of activity to cerebral blood flow (CBF) and oxygenation but their relative sensitivity has not been directly compared. We assessed changes in resting measures of BOLD and ASL MRI in response to two neurotransmitter modulators: citalopram, a selective serotonin reuptake inhibitor, and alprazolam, a positive allosteric modulator of GABA type A receptor. Thirty healthy subjects were imaged in a placebo-controlled study, with N=20 subjects receiving each treatment as part of an incomplete block design. Time-averaged CBF images from ASL and measures of resting-state fluctuations of BOLD and ASL images were assessed for significant effects. Following acute citalopram administration, analysis of the ASL data showed a reduction in time-averaged regional CBF in regions associated with high levels of 5-HT1A receptor density. In contrast, following alprazolam administration, BOLD amplitude of low frequency fluctuations showed a highly significant and cortically widespread increase, consistent with the distribution of GABA-A receptors. Only a marginal decrease in ASL CBF was detected after alprazolam intake. BOLD and ASL are each sensitive to drugs targeting neurotransmitter systems, but appear to reflect different aspects of neural metabolism and the balance between excitatory and inhibitory activity. Accordingly, their combination may best capture the effects of neurotransmitter modulations, and thus be advantageous for pharmacological MRI studies.

A comparative analysis of the sensitivity and BOLD contamination of the VASO response at 3 Tesla: ME-DEPICTING vs. ME-EPI readouts

Abstract ‘Non-BOLD fMRI’ data acquired at non-zero echo time (TE) suffer from contamination by the Blood Oxygenation Level Dependent (BOLD) signal due to the unavoidable signal decay caused by transverse relaxation. This contamination further reduces their already low inherent functional sensitivities and makes their correction essential. The Slice-Saturation Slab-Inversion Vascular Space Occupancy (SS-SI–VASO), for instance, cancels out BOLD contributions from VASO data, reflecting cerebral blood volume (CBV) changes, via a dynamic division approach. Alternatively, multi-echo (ME) data provide the possibility of extrapolating to TE=0. Acquisitions at very short TE would minimize the need for such corrections. The center-out EPI variant (‘DEPICTING’) is one such readout which allows for short TE. The ME 2D DEPICTING was compared here against a traditional ME 2D EPI for its sensitivity to functional changes in the VASO signal. The two BOLD-correction schemes were also evaluated. Clear differences in functional sensitivity were observed for the uncorrected VASO data obtained from the first echo, TE1, of the two readouts. VASO data corrected by ME extrapolation were, however, found to be almost identical in their sensitivity for detecting CBV changes for both readouts. An excessively high increase in VASO signal sensitivity observed with the dynamic division correction for both readouts revealed a near-perfect linear dependence on TE of VASO signal changes. This could be attributed to the substantial intravascular BOLD contributions at 3 T. In the present data, extravascular ΔR2* fraction was found to be around ~50–60%. ME extrapolation is, hence, recommended to avoid overestimation of functional CBV changes at commonly used TEs.

Cerebral hyperactivation across the Alzheimer’s disease pathological cascade

Abstract Neuronal dysfunction in specific brain regions or across distributed brain networks is a known feature of Alzheimer’s disease. An often reported finding in the early stage of the disease is the presence of increased functional MRI (fMRI) BOLD signal under task conditions relative to cognitively normal controls, a phenomenon known as “hyperactivation”. However, research in the past decades yielded complex, sometimes conflicting results. The magnitude and topology of fMRI hyperactivation patterns have been found to vary across the preclinical and clinical spectrum of Alzheimer’s disease, including concomitant “hypoactivation” in some cases. These incongruences are likely due to a range of factors, including the disease stage at which the cohort is examined, the brain areas or networks studied, and the fMRI paradigm utilised to evoke these functional abnormalities. Additionally, a perennial question pertains to the nature of hyperactivation in the context of Alzheimer’s disease. Some propose it reflects compensatory mechanisms to sustain cognitive performance while others suggest it is linked to the pathological disruption of a highly regulated homeostatic cycle that contributes to, or even drives, disease progression. Providing a coherent narrative for these empirical and conceptual discrepancies is paramount to develop disease models, understand the synergy between hyperactivation and the Alzheimer’s disease pathological cascade, and tailor effective interventions. We first provide a comprehensive overview of functional brain changes spanning the course from normal ageing to the clinical spectrum of Alzheimer’s disease. We then highlight evidence supporting a close relationship between fMRI hyperactivation and in vivo markers of Alzheimer’s pathology. We primarily focus on task-based fMRI studies in humans, but also consider studies using different functional imaging techniques and animal models. We then discuss the potential mechanisms underlying hyperactivation in the context of Alzheimer’s disease, and provide a testable framework bridging hyperactivation, ageing, cognition, and the Alzheimer’s disease pathological cascade. We conclude with a discussion of future challenges and opportunities to advance our understanding of the fundamental disease mechanisms of Alzheimer’s disease, and the promising development of therapeutic interventions incorporating or aimed at hyperactivation and large-scale functional systems.

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM.

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a sparse dynamic causal model (sDCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. sDCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range (31 females), providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model predicting the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects (173 females) selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.Significance statement By inferring region-wise hemodynamic profiles at the individual level, this is the first study providing an exhaustive whole-brain characterization of the hemodynamic sensitivity to healthy aging, reporting further evidence of the vascular changes across the adult lifespan. Using a predictive framework, we analysed the statistical influence of advancing age on individual regional hemodynamic attributes, offering a quantitative evaluation of the diverse hemodynamic bias across different brain regions. We then unveiled a specific set of hemodynamic predictors to discriminate young from elderly people, mainly describing vascular properties of right-hemispheric areas. This suggests the asymmetric nature of vascular degeneration processes affecting the human brain at the latest stage of life, other than a potential biomarker that could be relevant for brain-age prediction.

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Abstract Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Online self-evaluation of fMRI-based neurofeedback performance.

This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level (TL) of blood-oxygen-level-dependent (BOLD) activation. Here, we studied two types of self-evaluation: performance predictions and perceived confidence in the prediction judgement. Participants completed three sessions of SMA regulation in a 7 T fMRI scanner, performing a mental drawing task. During each trial, they modulated their imagery strategy to achieve one of two different levels of SMA activation and reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants' performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not correlate with better and worse predictions. In addition to shedding light on mechanisms of internal self-evaluation during neurofeedback training, these results also point to a dissociation between predictions of performance and confidence reports in the presence of feedback. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Individualized functional magnetic resonance imaging neuromodulation enhances visuospatial perception: a proof-of-concept study.

This proof-of-concept study uses individualized functional magnetic resonance imaging neuromodulation (iNM) to explore the mechanisms that enhance BOLD signals in visuospatial perception (VP) networks that are crucial for navigation. Healthy participants (n = 8) performed a VP up- and down-direction discrimination task at full and subthreshold coherence through peripheral vision, and superimposed direction through visual imagery (VI) at central space under iNM and control conditions. iNM targets individualized anatomical and functional middle- and medial-superior temporal (MST) networks that control VP. We found that iNM engaged selective exteroceptive and interoceptive attention (SEIA) and motor planning (MP) networks. Specifically, iNM increased overall: (i) area under the curve of the BOLD magnitude: 100% in VP (but decreased for weak coherences), 21-47% in VI, 26-59% in MP and 48-76% in SEIA through encoding; and (ii) classification performance for each direction, coherence and network through decoding, predicting stimuli from brain maps. Our findings, derived from encoding and decoding models, suggest that mechanisms induced by iNM are causally linked in enhancing visuospatial networks and demonstrate iNM as a feasibility treatment for low-vision patients with cortical blindness or visuospatial impairments that precede cognitive decline.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Concentric-object and equiangular-object methods to perform standardized regional analysis in renal mpMRI.

Renal multiparametric magnetic resonance imaging (mpMRI) sequences, including T1-T2 mapping, Blood oxygenation level-dependent (BOLD), Renal blood flow (RBF), and Apparent Diffusion Coefficient (ADC) from diffusion-weighted imaging (DWI), provide insights into kidney function. However, consensus on selecting regions of interest (ROIs) is lacking. This study aims to describe and compare the Concentric Objects (CO) and Equiangular Objects (EO) methods for standardized ROI selection and assess their efficacy in capturing regional variations in renal MRI parameters. Twelve healthy volunteers underwent mpMRI renal scans. ROIs were selected manually and by applying the CO and EO algorithms to each mpMRI sequence. The methods were tested across various subregion configurations. Regional differences in renal MRI parameters were evaluated. CO and EO methods demonstrated statistically significant differences in mpMRI parameters across renal regions. ASL-RBF, BOLD-MRI, and T2-map results indicated substantial variations from the lower to upper kidney areas. This study implemented CO and EO algorithms in renal mpMRI, showing their potential for evaluating cortico-medullary and cranio-caudal profiles. The findings validate the CO method for BOLD and ADC measurements and presented ASL-RBF and T1-T2 map profiles. The EO method's utility needs further validation with renal patients.

White matter engagement in brain networks assessed by integration of functional and structural connectivity

Current models of brain networks may potentially be improved by integrating our knowledge of structural connections, within and between circuits, with metrics of functional interactions between network nodes. The former may be obtained from diffusion MRI of white matter (WM), while the latter may be derived by measuring correlations between resting state BOLD signals from pairs of gray matter (GM) regions. From inspection of diffusion MRI data, it is clear that each WM voxel within a 3D image array may be traversed by multiple WM structural tracts, each of which connects a pair of GM nodes. We hypothesized that by appropriately weighting and then integrating the functional connectivity of each such connected pair, the overall engagement of any WM voxel in brain functions could be evaluated. This model introduces a structural constraint to earlier studies of WM engagement and addresses some limitations of previous efforts to relate structure and function. Using concepts derived from graph theory, we obtained spatial maps of WM engagement which highlight WM regions critical for efficient communications across the brain. The distributions of WM engagement are highly reproducible across subjects and depict a notable interdependence between the distribution of GM activities and the detailed organization of WM. Additionally, we provide evidence that the engagement varies over time and shows significant differences between genders. These findings suggest the potential of WM engagement as a measure of the integrity of normal brain functions and as a biomarker for neurological and cognitive disorders.

The effects of locus coeruleus optogenetic stimulation on global spatiotemporal patterns in rats

Abstract Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. We hypothesize that localized effects could be due to engagement of LC modules that support behaviors induced by our specific stimulation parameters, in addition to noradrenergic receptor profile distributions. Nonetheless, these results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting, further supporting emerging evidence of a modular LC.

Laminar multi-contrast fMRI at 7T allows differentiation of neuronal excitation and inhibition underlying positive and negative BOLD responses

Abstract A major challenge for human neuroimaging using functional MRI is the differentiation of neuronal excitation and inhibition which may induce positive and negative BOLD responses. Here, we present an innovative multi-contrast laminar functional MRI technique that offers comprehensive and quantitative imaging of neurovascular (CBF, CBV, BOLD) and metabolic (CMRO2) responses across cortical layers at 7T. This technique was first validated through a finger-tapping experiment, revealing ‘double-peak’ laminar activation patterns within the primary motor cortex. By employing a ring-shaped visual stimulus that elicited positive and negative BOLD responses, we further observed distinct neurovascular and metabolic responses across cortical layers and eccentricities in the primary visual cortex. This suggests potential feedback inhibition of neuronal activities in both superficial and deep cortical layers underlying the negative BOLD signals in the fovea, and also illustrates the neuronal activities in visual areas adjacent to the activated eccentricities.

Functional MRI of imprinting memory in awake newborn domestic chicks

Filial imprinting, a crucial ethological paradigm, provides insights into the neurobiology of early learning and its long-term impact on behaviour. To date, invasive techniques like autoradiography or lesions have been used to study it, limiting the exploration of whole brain networks. Recent advances in fMRI for avian brains now open new windows to explore bird’s brain functions at the network level. We developed an fMRI technique for awake, newly hatched chicks, capturing BOLD signal changes during imprinting experiments. While early memory acquisition phases are understood, long-term storage and retrieval remain unclear. Our findings identified potential long-term storage of imprinting memories across a neural network, including the hippocampal formation, the medial striatum, the arcopallium, and the prefrontal-like nidopallium caudolaterale. This paradigm opens up new avenues for exploring the broader landscape of learning and memory in neonatal vertebrates, enhancing our understanding of behaviour and brain networks.

Feasibility of submillimeter functional quantitative susceptibility mapping using 3D echo planar imaging at 7 T.

Quantitative susceptibility mapping (QSM) is a tool for mapping tissue susceptibility. Using QSM for functional brain mapping, it is possible to directly quantify blood-oxygen-level-dependent (BOLD) susceptibility changes. This study presents a submillimeter functional QSM (fQSM) approach compared to BOLD fMRI from data acquired with 3D gradient-echo echo planar imaging (EPI) at ultra-high field. Complex EPI data were acquired in nine healthy subjects with varying temporal and spatial resolutions and used for BOLD fMRI and for fQSM. Right-hand finger tapping experiments were performed as well as one measurement with intentional subject movement. Susceptibility maps were computed using 3D path-based unwrapping, the variable-kernel sophisticated harmonic artifact reduction for phase data, and the streaking artifact reduction for QSM algorithm. Functional data analysis included general linear modeling and computation of z-scores. Submillimeter data were denoised using NOise reduction with DIstribution Corrected (NORDIC), which improved z-scores in the motor cortex for fQSM and fMRI. An expected increase in BOLD fMRI signal and corresponding decrease in magnetic susceptibility was observed in sensorimotor areas during active periods. For all experiments, fQSM showed smaller activation regions compared with fMRI. The percentage of high negative t-values localized in the cortex was higher for fQSM (52%) than for positive or negative t-values for fMRI (45%). For the scans with intentional motion, movement exceeded the size of a voxel, but paradigm dependent signal evolution could be recovered using motion correction. In conclusion, this study demonstrates the feasibility of submillimeter whole-brain fQSM with voxel volume of 0.53 μL. In comparison to traditional BOLD fMRI, fQSM provided improved localization of brain activation within the cortex, especially in submillimeter 3D EPI sequences.

Somatosensory migraine auras evoked by bihemispheric cortical spreading depression events in human parietal cortex.

Cortical spreading depression (CSD) is associated with pronounced alterations in cerebral blood flow. These alterations can be captured using high-field functional magnetic resonance imaging (fMRI). While compelling clinical and experimental data suggest that CSD is involved in the pathogenesis of migraine aura, the mechanistic intricacies remain poorly understood. Here, we use visual stimulus-induced blood oxygen level-dependent (BOLD) fMRI responses to characterize spatiotemporal alterations in cerebral blood flow during spontaneous attacks with migraine aura. Six adult participants diagnosed with migraine with aura underwent BOLD fMRI scans with a visual stimulation paradigm, consisting of flickering checkerboard stimulation. Our results revealed that auras with somatosensory symptoms corresponded with bilateral alterations of stimulus-induced BOLD responses in the somatosensory cortex, exhibiting anterior-to-posterior propagation and absence of antecedent occipital abnormalities. These altered stimulus-induced BOLD responses were bilateral, despite a unilateral manifestation of aura symptoms, and had no relationship with positive or negative aura symptoms. The bilateral abnormalities in stimulus-induced BOLD responses completes our current knowledge on migraine aura.

Asymmetric spin echo multi-echo echo planar imaging (ASEME-EPI) sequence for pre-clinical high-field fMRI.

In functional magnetic resonance imaging (fMRI) of the blood oxygen level-dependent (BOLD) contrast, gradient-recalled echo (GRE) acquisitions offer high sensitivity but suffer from susceptibility-induced signal loss and lack specificity to microvasculature. In contrast, spin echo (SE) acquisitions provide improved specificity at the cost of reduced sensitivity. This study introduces Asymmetric Spin Echo Multi-Echo Echo Planar Imaging (ASEME-EPI), a technique designed to combine the benefits of both GRE and SE for high-field preclinical fMRI. ASEME-EPI employs a spin echo readout followed by two asymmetric spin echo (ASE) GRE readouts, providing an initial T2-weighted SE image and subsequent T2 * -weighted ASE images. A feasibility study for the technique was implemented on a 9.4 T pre-clinical MRI system and tested using a visual stimulation in northern tree shrews. Comparing ASEME-EPI with conventional GRE echo planar imaging (GRE-EPI) and SE echo planar imaging (SE-EPI) acquisitions, results showed that ASEME-EPI achieved BOLD contrast-to-noise ratio (CNR) comparable to GRE-EPI while offering improved specificity in activation maps. ASEME-EPI activation was more confined to the primary visual cortex (V1), unlike GRE-EPI which showed activation extending beyond anatomical boundaries. Additionally, ASEME-EPI demonstrated the ability to recover signal in areas of severe field inhomogeneity where GRE-EPI suffered from signal loss. The performance of ASEME-EPI is attributed to its multi-echo nature, allowing for SNR-optimized combination of echoes, effectively denoising the data. The inclusion of the initial SE also contributes to signal recovery in areas prone to susceptibility artifacts. This feasibility study demonstrates the potential of ASEME-EPI for high-field pre-clinical fMRI, offering a promising compromise between GRE sensitivity and SE specificity while addressing challenges of T2 * decay at high field strengths.