Biology subjects

Polimeni, J. R.

Publications and source records attributed to Polimeni, J. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Progression of visual-stimulus-evoked blood velocity response timing across the vascular network measured with individual-vessel phase-contrast fMRA

Most functional MRI (fMRI) studies of brain function aim to measure hemodynamic responses arising from the microvasculature within the tissue parenchyma, where spatial and temporal coupling with neuronal activity is thought to be tightest. However, the measured hemodynamic responses emerge from an interconnected vascular network that includes upstream feeding arteries, downstream draining veins, and the intervening microvascular compartments. Directly measuring and comparing vascular responses across multiple stages of this network may therefore provide more specific information about network-level hemodynamics--and how hemodynamics in one stage of the vascular hierarchy influence hemodynamics in others--that is not accessible from conventional fMRI signals alone. This can provide new insights into the relationship between the observed fMRI responses and the underlying neuronal activity of interest. This study extends our recently proposed dynamic phase-contrast functional MR angiography (PC-fMRA) method for tracking stimulus-evoked blood-velocity responses in large supplying arteries to enable concurrent hemodynamic measurements across multiple levels of the neurovascular complex. Here, we apply this method across several levels of the vascular hierarchy, from the basilar and posterior cerebral arteries to pial arteries, pial veins, and major draining venous sinuses such as the straight and superior sagittal sinuses. Stimulus-locked velocity increases were observed in individual vessels within each subject. Arterial responses occurred rapidly and nearly simultaneously across upstream and pial arterial segments, whereas venous responses exhibited temporal delays relative to the arterial responses. The average arterial-to-venous response time delay differed between the onset and offset phases, measuring approximately 4.1 s during response onset and 1.3 s during response offset, and also varied across different vessel segments. A simplified Windkessel model--used to interpret these findings--predicted similar inflow-outflow delays, suggesting that these temporal differences may reflect the transient filling and draining dynamics within compliant vascular compartments intervening between supplying arteries and draining veins. Our results demonstrate the feasibility of using dynamic PC-fMRA to measure vessel-specific hemodynamic responses across the human neurovascular complex. By providing quantitative measurements of macrovascular responses that serve as inputs to and outputs from intervening vascular compartments, this approach offers a framework for probing the hemodynamics within these compartments and studying vascular network behavior during neuronal activation, which may also help improve the physiological interpretation of fMRI signals in humans.

neuroscience↗

bioRxiv2026

Spontaneous Hemodynamic Oscillations during Sleep Coordinate Directional CSF Flow in the Subarachnoid Space and Ventricles

During sleep, cerebrospinal fluid (CSF) flow is enhanced and thought to facilitate clearance of metabolic waste from the brain. Human imaging has shown that sleep is associated with large waves of bulk CSF flow in the fourth ventricle; however, the CSF flow dynamics around brain tissue are unknown. Here, we investigated how CSF flow is organized across the subarachnoid space, a key site both for fluid exchange across cortical surface and for transport out of the cranium. We used an MRI technology at 7 Tesla that simultaneously measures cortical hemodynamics and slow subarachnoid CSF flow and applied this during sleep. We found that spontaneous infraslow hemodynamic oscillations were coupled to spatially structured, directional CSF flow throughout both ventricular and subarachnoid compartments. Hemodynamic oscillations and fourth-ventricle bulk flow were both associated with brain-wide CSF flow at different phases, indicating that they reflect a single coordinated CSF movement cycle. Compared to cardiac pulsations, a canonical driver of CSF movement, hemodynamic-driven CSF flow showed a distinct directional pattern and was smaller in instantaneous amplitude but produced substantially greater cumulative displacement and thus greater potential for transport within an oscillation period. Together, these findings show that hemodynamic oscillations during sleep are coupled to temporally coordinated and directional CSF flow throughout the ventricles and subarachnoid space, suggesting that sleep-dependent neuronal activity drives fluid movement across the human brain in a distinct spatial pattern.

neuroscience↗

bioRxiv2026

Quantification of cardiac-locked brainstem velocity at high resolution based on retrospectively-gated DENSE MRI at 7T

PurposeThe motion of the brain tissue within the skull is thought to be induced by cardiac pulsations and other hemodynamic processes and may influence CSF flow, but its precise drivers and downstream effects are unclear. Understanding these phenomena requires an accurate and precise method of tissue motion quantification that can be extended to investigate multiple potential drivers of tissue motion in vivo. MethodsHere, a version of the Displacement ENcoding with Stimulated Echoes (DENSE) pulse sequence was implemented to measure cardiac-locked velocity responses in the pons and midbrain in eight healthy volunteers. The method featured retrospective cardiac gating, a single mixing time, and measured multiple voxel sizes. ResultsA previously undescribed double-peak pattern of cardiac-locked longitudinally directed brainstem velocity was identified that appears to reflect both nonrigid and rigid motion components. This pattern was only visible when estimating the cardiac response using absolute time after systole instead of the percentage of the cardiac cycle. Measurements were performed in a custom-built slow-flow phantom, and repeat sessions were acquired in two volunteers to assess accuracy and precision. Despite potential increased influence of CSF motion with larger voxel sizes, no voxel-size-dependent bias was found in the velocity estimations. Lack of voxel size bias was attributed to the complexities of partial-volume effects between CSF and tissue in phase-valued data that were evaluated using numerical simulations. ConclusionIn sum, a method to measure brain tissue motion with high spatiotemporal precision is presented that can be extended to applications beyond measuring cardiac-locked motion.

neuroscience↗

bioRxiv2026

Neural activity drives directional subarachnoid cerebrospinal fluid flow in the human brain

Neural activity is a potential driver of cerebrospinal fluid (CSF) dynamics and brain waste clearance, yet how neural activity shapes localized CSF flow in the subarachnoid space remains poorly understood in humans. Here we introduce an MRI framework that enables concurrent mapping of ultra-slow subarachnoid CSF flow at velocity scales of tens of micrometers-per-second together with hemodynamic activity measured by simultaneous functional MRI in the human brain. Using this framework, we found that neural activity drives spatiotemporally coherent, directional CSF flow in the subarachnoid space near sites of neural activation in awake humans. These flow responses were highly spatially specific, shaped by local cortical and subarachnoid anatomy, while exhibiting oscillatory temporal dynamics modulated by the stimulus cycle and strongly coupled to hemodynamic responses. These findings establish a link between neural activity, vascular dynamics, and localized subarachnoid CSF flow, and provide a framework for investigating neural-activity-driven CSF transport in humans.

neuroscience↗

bioRxiv2026

Human Cerebral Cortex Organization Characterized by Functional PET-FDG "Metabolic Connectivity"

PurposeIn this study, we characterize the spatiotemporal organization of resting-state metabolic connectivity (RSMC) in the human brain, as measured by [18F]- fluorodeoxyglucose (FDG) functional PET (fPET-FDG). We examine the relationship between RSMC organization and resting-state functional connectivity (RSFC) derived from functional magnetic resonance imaging and other known cortical organizational principles. MethodsResting-state fPET-FDG data from 24 individuals were obtained from a publicly available repository. We characterized local metabolic organization using connectivity-based boundary mapping, with adaptations to account for the low signal-to-noise ratio of fPET-FDG data. We then estimated global metabolic organization through community detection-based network and principal gradient analyses. Furthermore, we examined how metabolic connectivity is shaped by temporal-frequency-specific components of fPET-FDG signal. Finally, we contextualized metabolic organization by relating metabolic gradients to anatomical, functional, and energetic reference measures. ResultsAt the local scale, boundary mapping results indicated structured transitions shaped by a combination of both fast and slow fPET-FDG signals, partly overlapping with RSFC boundary maps. Globally, RSMC analyses revealed a robust metabolic structure organized along a superior-inferior cortical gradient. This pattern remained consistent across network community detection and principal gradient analyses and was primarily driven by low-frequency, minute-scale fPET-FDG dynamics. The identified large-scale metabolic profile aligns closely with several known anatomical and energetic constraints. ConclusionThis study characterizes the spatiotemporal organizational principles of RSMC, deepening insight into the brains energetic framework and providing a basis for future cognitive and clinical investigations of metabolic connectivity organization.

neuroscience↗

bioRxiv2026

Energetic implications of fMRI-based nodal complex network metrics: a complex picture unfolds across diverse brain states

Functional MRI-based graph theory has provided profound insights into the brains functional organization, yet the neuroenergetic meaning of widely used graph-theoretical metrics remains poorly understood. Although resting-state research suggests a positive coupling between network topology and glucose metabolism, it remains unclear whether this relationship reflects a general principle of brain organization or a state-specific phenomenon. Here, we test the neuroenergetic interpretability of nodal graph-theoretical metrics by linking complex network topology to cerebral glucose consumption across diverse brain states. Leveraging simultaneous functional PET-MRI, we directly compare state-dependent fluctuations in glucose consumption and network topology during sensory, cognitive, and arousal conditions. We further assess metabolic-topological couplings in disease through a meta-analysis of resting-state FDG-PET and fMRI studies involving Alzheimers disease, Parkinsons disease, major depressive disorder, and schizophrenia. Our results show that nodal graph-theoretical metrics exhibit state- and network-dependent metabolic associations, with coupling patterns diverging across experimental and disease contexts. Notably, frontoparietal and attentional networks show more conserved metabolic-topological coupling than other large-scale networks across states. These findings underscore a dynamic, complex interplay between metabolic demand and complex network organization, highlighting the need for a nuanced interpretation of the energetic underpinnings of nodal graph-theoretical metrics in health and disease.

neuroscience↗

bioRxiv2025

Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity

Technological advances have enabled fMRI acquisition with high temporal resolution, enabling brainwide imaging in just a few hundreds of milliseconds. However, the relationship between fast hemodynamic signals and spontaneous neural activity in the resting state is not yet well understood, limiting our ability to infer neural processes from these fast data. We hypothesized that high-frequency fMRI signals are linked to spontaneous neural activity tied to vigilance states, and that these high-frequency signals could be used to infer the dynamic variations in neuronal activity indexed by EEG neural rhythms. Using fast fMRI (TR=378 ms) and simultaneous EEG in 27 humans drifting between sleep and wakefulness, we found that fMRI power increased during NREM sleep (compared to wakefulness) in frequencies up to 1.3 Hz. High-frequency fMRI power was also correlated to canonical arousal-linked EEG rhythms (alpha and delta), with spatiotemporal cross-correlation patterns reflecting both shared arousal dynamics and rhythm-specific signatures. Using machine learning, we found that EEG alpha and delta power can be decoded from high-frequency fMRI signals in subjects held-out from the training set, showing that the high-frequency components of fMRI signals contain neurally-coupled information robust enough to generalize across individuals. These results reveal that high-frequency fMRI signals are coupled to dynamically varying brain states, and that fast fMRI allows for temporally precise quantification of spontaneous neural activity. Significance StatementFunctional MRI enables non-invasive brainwide neuroimaging in humans, but classically had limited temporal resolution. Recently, studies demonstrated that fast fMRI sampling can detect stimulus-evoked neural activity at subsecond timescales. However, it was unknown whether spontaneous neural activity is represented in fast fMRI signals. Using fMRI and simultaneous EEG in subjects who fell asleep, we found that EEG neural activity associated with sleep and wakefulness is represented in high-frequency fMRI signals. The results reveal that fast fMRI sampling can be used to investigate the spontaneous neural activity underlying brain states with high temporal precision.

neuroscience↗

bioRxiv2025

Biophysical simulations of fMRI responses using realistic microvascular models: insights into distinct hemodynamics in humans and mice

1Functional Magnetic Resonance Imaging (fMRI) is broadly used to measure human brain activity, however the hemodynamic changes that comprise the fMRI response to neuronal activity are often interpreted using microscopy data in mice. These microscopy data provide ground-truth observations of how individual blood vessels respond to neuronal activity and thus form the basis of our fundamental understanding of neurovascular coupling. Although these invasive experiments provide invaluable insight, there are striking differences in the vascular architecture of mouse and human brains that may influence the hemodynamic response. Motivated by this, we developed a biophysical modeling framework for realistic hemodynamic simulations in both mouse and human cerebral cortex. For this, we utilized Vascular Anatomical Network (VAN) models that explicitly represent the full microvascular tree as a single connected network, originally based on anatomical reconstructions from a given location of mouse cerebral cortex. We extended the VAN modeling framework using synthetic VAN models representing the microvascular network at a single location of the human cerebral cortex. To account for larger size and complexity of the human VAN models, we developed an efficient computational framework to simulate the full hemodynamic responses in this human model and compared the simulated fMRI responses between mice and humans. Our biophysical simulations are based entirely on first principles (e.g., conservation of mass); model parameter values were fixed across all simulations, not tuned to fit data, as they represent meaningful physical constants taken from previous measurements. Only two simple calibrations were tuned for each simulation, to match baseline perfusion rates (blood flow) and oxygen extraction (OEF). Our results show that differences in microvasculature indeed influenced the hemodynamic response and led to observable differences in timing--e.g., the simulated fMRI response peak in humans was delayed by [~]2 s compared to that of mice, consistent with prior fMRI observations. While there are many known differences in vascular architecture in rodents and humans, we also discovered that, unexpectedly, an asymmetry in the numbers of branches of the penetrating intracortical arterioles and venules appears to be conserved across species. We demonstrate through further simulations that this anatomical property may also be needed for suitable hemodynamic responses. Our framework thus provides a valuable tool for bridging in-vivo microscopy of microvascular dynamics to human fMRI.

neuroscience↗

bioRxiv2025

Temporal evolution and spatial heterogeneity of cerebral cortical-depth profiles of the BOLD-fMRI response

Functional MRI measures brain activity by tracking the associated hemodynamic response, which is shaped by the vascular anatomy. Microscopy studies have shown that the hemodynamic response initiates within the cerebral cortex then spreads upwards to the pial surface, where the largest fMRI signal changes are seen, however responses at the cortical surface exhibit poor neuronal specificity. Motived by this, we characterized the time-evolution of the fMRI response in humans. At some cortical locations the fMRI response peaked at the pial surface but in others it peaked within the cortex, likely due to the sparsity of large pial vessels and the dense capillary bed in middle cortical layers. We observed the earliest response onset in middle cortical depths, which is also the site of thalamocortical input. Standard approaches that aggregate fMRI responses over space and time may therefore lose meaningful information, and "physiologically-informed" strategies may enhance neuronal specificity of fMRI measurements.

neuroscience↗

bioRxiv2025

Evaluating BOLD functional MRI biophysical simulation approaches: impact of vascular geometry, magnetic field calculations, and water diffusion models

Biophysical simulations have guided the development of blood oxygenation level-dependent (BOLD) functional MRI (fMRI) acquisitions and signal models that relate the BOLD signal to the underlying physiology, such as calibrated BOLD and vascular fingerprinting. Numerous simulation techniques have been developed, however, few of them have been directly compared, thus limiting the assessment of the accuracy and interchangeability of these methods as well as the accuracy of the quantitative techniques derived from them. In this work, we compared the accuracy and computational demands of eight previously published simulation approaches that adopt different geometries (ranging from infinite cylinders to synthetic vascular anatomical networks (VANs)), field offset calculations (analytical and Fourier-based), and water diffusion implementations (Monte Carlo and convolution-based), all of which are available in an open-source Python toolkit, BOLDs{omega}imsuite. The reference simulation approach for comparison used three-dimensional infinite cylinders, analytical field offsets, and Monte Carlo diffusion. When compared with the reference approach, most of the simulations, including two- and three-dimensional geometries, were in excellent agreement when assuming the intravascular signal contribution was small. Two commonly employed simulation approaches were notably biased; both used two-dimensional geometries with overly simplified vasculature or field offset calculations. In general, the simulated intravascular signal was the least consistent across approaches, thus potentially resulting in larger errors when the intravascular signal contribution is large. Lastly, the VAN results were in good agreement with the reference but they diverged slightly, yet systematically, from each other at smaller radii ([lsim] 3 m), primarily driven by intravascular signal differences. We conclude, therefore, that the reference approach is an attractive option for exploratory simulations in the many cases where anatomical and hemodynamic realism is not needed, balancing ease of implementation, accessibility, versatility, computational efficiency, accuracy of results, and interpretability. These findings help pave the way for a broader adoption of forward modelling of the BOLD signal and more reliable interpretations of biophysical simulations aiming to develop quantitative models of the BOLD signal.

neuroscience↗

bioRxiv2025

7 Tesla fMRI characterisation of the cortical-depth-dependentBOLD response in early human development

Human cortical development leading up to and around birth is crucial for lifelong brain function. Cortical activity can be studied using BOLD fMRI, however, previously limited sensitivity and spatial specificity has constrained understanding of how its emergence relates to functional cortical circuitry and neurovascular development at the mesoscale. To resolve this, we used ultra-high-field 7 Tesla MRI to acquire sub-millimetre resolution BOLD-fMRI data from 40 newborns and 4 adults. In all subjects, passive right-hand movement elicited localised, positive BOLD responses in contralateral primary somatosensory cortex. In newborns, depth-specific BOLD responses were still evident in the thinner cortex, with developmental changes in response temporal features and amplitudes at different depths. This provides insight into key rapidly evolving factors in early cortical development including neuronal function, vascular architecture, and neurovascular coupling. Our framework and findings provide a foundation for future studies of emerging cortical circuitry and how disruption leads to adverse outcomes.

developmental biology↗

bioRxiv2025

Visual stimulus-evoked blood velocity responses in individual human posterior cerebral arteries measured with dynamic phase-contrast functional MR angiography

Functional MRI (fMRI) tracks brain activity through the associated hemodynamic changes via neurovascular coupling. Neurons communicate with the microvessels of the parenchyma to initiate a hemodynamic response, and these microvessels then communicate with upstream arterioles and arteries. The role of the larger feeding arteries--far upstream from the site of neuronal activity--in coordinating this response is incompletely understood, yet is important for the interpretation of fMRI. Functional Transcranial Doppler (fTCD) can noninvasively measure blood velocity changes in a subset of the largest macrovessels, albeit with poor spatial resolution, whereas existing functional MR angiography (fMRA) methods can assess blood velocity in mid-sized macrovessels but still lack the temporal resolution required to capture dynamic responses. This study aims to propose a new, quantitative fMRA method for measuring blood velocity responses in individual vessels in humans at high spatiotemporal resolution. A dynamic functional phase-contrast MRA approach was developed to quantify responses evoked by visual stimuli in the "P2" segment of the posterior cerebral artery (PCA), located [~]6 cm away from primary visual cortex. The achieved temporal resolution is comparable to that of conventional blood-oxygenation-level-dependent (BOLD) fMRI, enabling block-design stimulation paradigms similar to those used in conventional fMRI studies. The temporal and spatial properties of the blood velocity responses were evaluated using both long- and short-duration visual stimuli presented to either the full visual field or a single hemifield. Robust responses were measured on both 3T and 7T clinical MRI scanners, and an approximately 3.3 {+/-} 1.2 cm/s increase in the blood velocity in the targeted segment was observed, which is roughly a 10% increase from baseline. Visual hemifield stimulation generated a measurable blood velocity response only in the contralateral cerebral hemisphere, indicating that systemic physiological changes occurring with stimulation cannot account for the observed response, suggesting that they instead reflect neurovascular coupling initiated in the visual cortex. The observed arterial blood velocity response is consistent with a downstream reduction in microvascular resistance and may represent a passive response rather than an active vessel dilation at the targeted arterial segment. The proposed method has the potential to extend the capability of commonly used approaches, such as fTCD, in clinical applications.

neuroscience↗

bioRxiv2025

Function of the auditory cortex characterized by its intrinsic dynamic coactivation patterns estimated in individuals

Determining the functional organization of the auditory cortex (AC) has been difficult with conventional task-based approaches due to the broad responsiveness of auditory subregions to various acoustic properties. Moreover, most studies have investigated functional organization of AC with static methods, although brain has shown to be organized into dynamic networks. Here, we investigated dynamically varying coactivation patterns of the local networks in the auditory cortex (AC) determined from 7T fMRI data with a novel individualized network-based algorithm. An eight-pattern solution was selected for closer examination based on its high reproducibility of the occurrence rates (r=0.86) and the spatial topography (r=0.79) between sessions. Dynamic AC patterns successfully captured interindividual variability, as indicated by significantly higher variability between than within individuals for the AC pattern occurrence rates and spatial topographies. The coactivation patterns shared similarities between resting-state and auditory-task data, as indicated by the group-level similarity of 0.84 and individual-level similarity of 0.71 in the spatial topographies. Furthermore, the occurrence rates of AC patterns identified in the task data, using pattern templates derived from resting-state data, correlated with specific task contrast regressors. Our results indicate that the AC function can be characterized by a set of dynamically varying coactivation patterns that are consistently observed during resting state and auditory stimulation, and that get synchronized with auditory input. These findings enhance our understanding of the relationship between spontaneous and stimulus-driven activity in the AC and support the development of more time-efficient paradigms for studying its functional organization.

neuroscience↗

bioRxiv2025

Functional MRI Signatures of Autonomic Physiology in Aging

While traditionally regarded as "noise", blood-oxygenation-level-dependent (BOLD) fMRI fluctuations coupled to systemic physiology--such as heart rate and respiratory changes--also hold valuable information about brain vascular properties and autonomic function. In this study, we leverage these physiological BOLD signals to characterize age-related changes in brain physiology. Using a large dataset from the Lifespan Human Connectome Project Aging study, we investigated how the spatiotemporal BOLD-fMRI signatures of autonomic physiology, specifically heart rate and respiratory variation, change with advancing age. Our findings reveal that aging is associated with globally slower respiratory fMRI responses, alongside faster cardiac fMRI responses and enhanced brain-cardiac signal coupling. Moreover, we show that the impact of age on physiological fMRI signals exhibits a notable turning point after age 60, suggesting a critical role of declining vascular health and autonomic function in aging. The potential impact of age-related changes in brain structure, tissue perfusion, and in-scan arousal states on the identified physiological fMRI patterns is also tested and discussed. Altogether, our results underscore significant age effects in the fMRI signatures of systemic physiology, emphasizing the pivotal role of altered vascular properties and autonomic function in aging. Methodologically, this study also demonstrates the utility of resting-state fMRI for extracting multi-parametric information about brain physiology, offering new biomarker opportunities that complement established functional connectivity metrics.

physiology↗

bioRxiv2025

Quantifying brain-wide cerebrospinal fluid flow dynamics using slow-flow-sensitized phase-contrast MRI

Cerebrospinal fluid (CSF) flow is a key component of the brains waste clearance system. However, our understanding of CSF flow in the human brain, particularly within the brain-wide subarachnoid space (SAS), is limited due to a lack of non-invasive tools for measuring slow flow. Here, we propose a CSF flowmetry technique using phase-contrast MRI combined with a slow-flow-sensitized acquisition. It achieves high sensitivity in measuring slow CSF flow (e.g., 100 m/s), and enables quantitative measurement of the velocity and direction with whole-brain coverage, spanning from ventricles to SAS. Our proof-of-concept results demonstrate repeatable flow measurements and show that cardiac pulsation induces coherent CSF flow changes within the SAS. Our data also suggest that cardiac pulsation has a stronger driving effect on brain-wide CSF flow compared to respiration. This technique provides a valuable tool for investigating CSF dynamics and pathways to advance a holistic understanding of brain-wide CSF flow. TeaserA novel MR technique enables noninvasive, quantitative mapping of slow cerebrospinal fluid flow in the subarachnoid space across the human brain.

bioengineering↗

bioRxiv2025

T1-weighted fMRI in mouse visual cortex using an Iron Oxide Nanoparticle contrast agent and Ultrashort Echo Time (UTE) imaging at 9.4 T

PurposeThis study aims to investigate the feasibility of using T1-weighted fMRI with an iron oxide nanoparticle contrast agent and Ultrashort Echo Time (UTE) imaging at 9.4T to measure functional hyperaemia in the mouse visual cortex. The goal is to capture positive signal changes in both the parenchyma and pial surface, and to test whether surface vessels respond during neuronal activation. MethodsThe study involved scanning of nine mice after administration of iron oxide-based superparamagnetic contrast agent (Molday ION) via the tail vein. Two functional imaging experiments were conducted: one to investigate the effect of echo time on the functional response, and another to characterize the impact of higher resolution on UTE functional contrast. Regions of interest (ROIs) were defined in the parenchyma and pial surface of the visual cortex. ResultsThe administration of the contrast agent produced a bright-blood signal in the vasculature in structural MRI when using a UTE acquisition. Positive signal changes were observed at the shortest echo time (0.164 ms) in both the parenchyma (0.2% {+/-} 0.08) and pial surface (0.2% {+/-} 0.1 %), providing evidence that UTE fMRI experiments can detect changes in both pial and parenchymal vessels. Measurements using longer echo times ([≥]1 ms) showed negative signal changes. Higher spatial resolution resulted in increased percent signal change at the pial surface, suggesting less partial volume effects and better delineation of surface vessels. ConclusionThe findings demonstrate that T1-weighted fMRI with UTE imaging and iron oxide nanoparticles captures positive signal changes across all vascular compartments, providing additional insights into the involvement of surface vessels during functional hyperemia.

neuroscience↗

bioRxiv2024

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal lag (CortiLag) patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo (ME) ICA, except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed CortiLag-ICA framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.

neuroscience↗

bioRxiv2024

On the analysis of functional PET (fPET)-FDG: baseline mischaracterization can introduce artifactual metabolic (de)activations

Functional Positron Emission Tomography (fPET) with (bolus plus) constant infusion of [18F]-fluorodeoxyglucose FDG), known as fPET-FDG, is a recently introduced technique in human neuroimaging, enabling the detection of dynamic glucose metabolism changes within a single scan. However, the statistical analysis of fPET-FDG data remains challenging because its signal and noise characteristics differ from both classic bolus-administration FDG PET and from functional Magnetic Resonance Imaging (fMRI), which together compose the primary sources of inspiration for analytical methods used by fPET-FDG researchers. In this study, we present an investigate of how inaccuracies in modeling baseline FDG uptake can introduce artifactual patterns to detrended TAC residuals, potentially introducing spurious (de)activations to general linear model (GLM) analyses. By combining simulations and empirical data from both constant infusion and bolus-plus-constant infusion protocols, we evaluate the effects of various baseline modeling methods, including polynomial detrending, regression against the global mean time-activity curve, and two analytical methods based on tissue compartment model kinetics. Our findings indicate that improper baseline removal can introduce statistically significant artifactual effects, although these effects characterized in this study ([~]2-8%) are generally smaller than those reported by previous literature employing robust sensory stimulation ([~]10-30%). We discuss potential strategies to mitigate this issue, including informed baseline modeling, optimized tracer administration protocols, and careful experimental design. These insights aim to enhance the reliability of fPET-FDG in capturing true metabolic dynamics in neuroimaging research.

neuroscience↗