Canonical Hemodynamic Response Function Research Articles

Hemodynamic response function description in patients with glioma

IntroductionFunctional magnetic resonance imaging is a powerful tool that has provided many insights into cognitive sciences. Yet, as its analysis is mostly based on the knowledge of an a priori canonical hemodynamic response function (HRF), its reliability in patients’ applications has been questioned. There have been reports of neurovascular uncoupling in patients with glioma, but no specific description of the Hemodynamic Response Function (HRF) in glioma has been reported so far. The aim of this work is to describe the HRF in patients with glioma. MethodsForty patients were included. MR images were acquired on a 1.5T scanner. Activated clusters were identified using a fuzzy general linear model; HRFs were adjusted with a double-gamma function. Analyses were undertaken considering the tumor grade, age, sex, tumor location, and activated location. ResultsDifferences are found in the occipital, limbic, insular, and sub-lobar areas, but not in the frontal, temporal, and parietal lobes. The presence of a glioma slows the time-to-peak and onset times by 5.2 and 3.8 % respectively; high-grade gliomas present 8.1 % smaller HRF widths than low-grade gliomas. Discussion and conclusionThere is significant HRF variation due to the presence of glioma, but the magnitudes of the observed differences are small. Most processing pipelines should be robust enough for this magnitude of variation and little if any impact should be visible on functional maps. The differences that have been observed in the literature between functional mapping obtained with magnetic resonance vs. that obtained with direct electrostimulation during awake surgery are more probably due to the intrinsic difference in the mapping process: fMRI mapping detects all recruited areas while intra-surgical mapping indicates only the areas indispensable for the realization of a certain task. Surgical mapping might not be the gold standard to use when trying to validate the fMRI mapping process.

Pupil size reflects activation of subcortical ascending arousal system nuclei during rest.

Neuromodulatory nuclei that are part of the ascending arousal system (AAS) play a crucial role in regulating cortical state and optimizing task performance. Pupil diameter, under constant luminance conditions, is increasingly used as an index of activity of these AAS nuclei. Indeed, task-based functional imaging studies in humans have begun to provide evidence of stimulus-driven pupil-AAS coupling. However, whether there is such a tight pupil-AAS coupling during rest is not clear. To address this question, we examined simultaneously acquired resting-state fMRI and pupil-size data from 74 participants, focusing on six AAS nuclei: the locus coeruleus, ventral tegmental area, substantia nigra, dorsal and median raphe nuclei, and cholinergic basal forebrain. Activation in all six AAS nuclei was optimally correlated with pupil size at 0-2 s lags, suggesting that spontaneous pupil changes were almost immediately followed by corresponding BOLD-signal changes in the AAS. These results suggest that spontaneous changes in pupil size that occur during states of rest can be used as a noninvasive general index of activity in AAS nuclei. Importantly, the nature of pupil-AAS coupling during rest appears to be vastly different from the relatively slow canonical hemodynamic response function that has been used to characterize task-related pupil-AAS coupling.

BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles

Typical fMRI analyses often assume a canonical hemodynamic response function (HRF) that primarily focuses on the peak height of the overshoot, neglecting other morphological aspects. Consequently, reported analyses often reduce the overall response curve to a single scalar value. In this study, we take a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. We then employ a roughness penalty at the population level to estimate the response curve, aiming to enhance predictive accuracy, inferential efficiency, and cross-study reproducibility. By examining a fast event-related FMRI dataset, we demonstrate the shortcomings and information loss associated with adopting the canonical approach. Furthermore, we address the following key questions: 1) To what extent does the HRF shape vary across different regions, conditions, and participant groups? 2) Does the data-driven approach improve detection sensitivity compared to the canonical approach? 3) Can analyzing the HRF shape help validate the presence of an effect in conjunction with statistical evidence? 4) Does analyzing the HRF shape offer evidence for whole-brain response during a simple task?

Perspective: Disentangling the effects of tES on neurovascular unit.

Transcranial electrical stimulation (tES) can modulate the neurovascular unit, including the perivascular space morphology, but the mechanisms are unclear. In this perspective article, we used an open-source "rsHRF toolbox" and an open-source functional magnetic resonance imaging (fMRI) transcranial direct current stimulation (tDCS) data set to show the effects of tDCS on the temporal profile of the haemodynamic response function (HRF). We investigated the effects of tDCS in the gray matter and at three regions of interest in the gray matter, namely, the anodal electrode (FC5), cathodal electrode (FP2), and an independent site remote from the electrodes (PZ). A "canonical HRF" with time and dispersion derivatives and a finite impulse response (FIR) model with three parameters captured the effects of anodal tDCS on the temporal profile of the HRF. The FIR model showed tDCS onset effects on the temporal profile of HRF for verum and sham tDCS conditions that were different from the no tDCS condition, which questions the validity of the sham tDCS (placebo). Here, we postulated that the effects of tDCS onset on the temporal profile of HRF are subserved by the effects on neurovascular coupling. We provide our perspective based on previous work on tES effects on the neurovascular unit, including mechanistic grey-box modeling of the effects of tES on the vasculature that can facilitate model predictive control (MPC). Future studies need to investigate grey-box modeling of online effects of tES on the neurovascular unit, including perivascular space, neurometabolic coupling, and neurovascular coupling, that can facilitate MPC of the tES dose-response to address the momentary ("state") and phenotypic ("trait") factors.

Effect of Sleep on Motor Learning and Neural Activation Among Individuals with Traumatic Brain Injury

<h3>Research Objectives</h3> To examine the effect of a 45-minute nap on motor sequence learning and neural activation compared with a 45-minute period of resting wakefulness among individuals with traumatic brain injury (TBI). <h3>Design</h3> Cross-sectional. <h3>Setting</h3> Northeastern Rehabilitation Research Facility. <h3>Participants</h3> 23 individuals with a history of TBI between the ages of 21-63 (M=45.1, SD=12.9). Females comprised 13.6 % of the sample. <h3>Interventions</h3> Not Applicable. <h3>Main Outcome Measures</h3> E*Prime was used to deliver stimuli and capture the number of correct finger-tapping sequences over 12 learning trials and 6 retest trials after a 45-minute period of either sleep or relaxed wakefulness. A Siemens 3T Skyra MRI scanner was used to collect functional brain data while they completed the tapping sequence. Regressors corresponding to the time period of tapping sequence after the 45-minute Nap or Wake intervention were convolved with a canonical hemodynamic response function. The beta weights associated with the conditions of interest (tap vs non-tap) were entered in random-effects group analysis and corrected for multiple comparisons (z>1.6). <h3>Results</h3> Analysis of covariance demonstrated a significantly greater gain in the number of correct sequences in the Nap group (M=1.8, SD=1.7) compared with the Wake group (M=-.05, SD=2.9), controlling for age and learning capacity across training trials, F(1,18)=5.1, p=0.037. fMRI data revealed significant differences in activation between the Wake vs. Nap group in regions of the default mode network (DMN) (the precuneus and medial temporal lobe) as well as in the right ventrolateral prefrontal cortex (vlPFC). <h3>Conclusions</h3> The sleep-dependent enhancement of motor sequence learning is consistent with prior research after TBI. The fMRI findings suggest that this effect may be associated with a better ability in the Nap group to suppress DMN activation. At the same time, vlPFC activation might suggest greater reliance of the Wake group on working memory compared to the Nap group. <h3>Author(s) Disclosures</h3> Research was funded by the New Jersey Commission on Brain Injury Research. Authors have no conflicts of interest to disclose.

Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping

A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.

Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition.

The relation between electrophysiology and BOLD‐fMRI requires further elucidation. One approach for studying this relation is to find time‐frequency features from electrophysiology that explain the variance of BOLD time‐series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD‐fMRI data. We propose a framework for extracting the spatial distribution of these time‐frequency features while also estimating more flexible, region‐specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD‐fMRI and can be used to construct estimates of BOLD time‐series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task‐based and resting‐state EEG‐fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter‐subject variability with regards to EEG‐to‐BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.

Across the adult lifespan the ipsilateral sensorimotor cortex negative BOLD response exhibits decreases in magnitude and spatial extent suggesting declining inhibitory control

Ipsilateral sensorimotor (iSM1) cortex negative BOLD responses (NBR) are observed to unilateral tasks and are thought to reflect a functionally relevant component of sensorimotor inhibition. Evidence suggests that sensorimotor inhibitory mechanisms degrade with age, along with aspects of motor ability and dexterity. However, understanding of age-related changes to NBR is restricted by limited comparisons between young vs old adults groups with relatively small samples sizes. Here we analysed a BOLD fMRI dataset (obtained from the CamCAN repository) of 581 healthy subjects, gender-balanced, sampled from the whole adult lifespan performing a motor response task to an audiovisual stimulus. We aimed to investigate how sensorimotor and default-mode NBR characteristics of magnitude, spatial extent and response shape alter at every decade of the aging process. We observed a linear decrease in iSM1 NBR magnitude across the whole lifespan, whereas the contralateral sensorimotor (cSM1) PBR magnitude was unchanged. An age-related decrease in the spatial extent of NBR and an increase in the ipsilateral positive BOLD response (PBR) was observed. This occurred alongside an increasing negative correlation between subject's iSM1 NBR and cSM1 PBR magnitude, reflecting a change in the balance between cortical excitation and inhibition. Conventional GLM analysis, using a canonical haemodynamic response (HR) function, showed disappearance of iSM1 NBR in subjects over 50 years of age. However, a deconvolution analysis showed that the shape of the iSM1 HR altered throughout the lifespan, with significantly delayed time-to-peak and decreased magnitude. The most significant decreases in iSM1 HR magnitude occurred in older age (>60 years) but the first changes in HR shape and timing occurred as early as 30 years, suggesting the possibility of separate mechanisms underlying these alterations. Reanalysis using data-driven HRs for each decade detected significant sensorimotor NBR into late older age, showing the importance of taking changes in HR morphology into account in fMRI aging studies. These results may reflect fMRI measures of the age-related decreases in transcollosal inhibition exerted upon ipsilateral sensorimotor cortex and alterations to the excitatory-inhibitory balance in the sensorimotor network.

Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data.

.SignificanceAlthough emerging evidence suggests that the hemodynamic response function (HRF) can vary by brain region and species, a single, canonical, human-based HRF is widely used in animal studies. Therefore, the development of flexible, accessible, brain-region specific HRF calculation approaches is paramount as hemodynamic animal studies become increasingly popular.AimTo establish an fMRI-compatible, spectral, fiber-photometry platform for HRF calculation and validation in any rat brain region.ApproachWe used our platform to simultaneously measure (a) neuronal activity via genetically encoded calcium indicators (GCaMP6f), (b) local cerebral blood volume (CBV) from intravenous Rhodamine B dye, and (c) whole brain CBV via fMRI with the Feraheme contrast agent. Empirical HRFs were calculated with GCaMP6f and Rhodamine B recordings from rat brain regions during resting-state and task-based paradigms.ResultsWe calculated empirical HRFs for the rat primary somatosensory, anterior cingulate, prelimbic, retrosplenial, and anterior insular cortical areas. Each HRF was faster and narrower than the canonical HRF and no significant difference was observed between these cortical regions. When used in general linear model analyses of corresponding fMRI data, the empirical HRFs showed better detection performance than the canonical HRF.ConclusionsOur findings demonstrate the viability and utility of fiber-photometry-based HRF calculations. This platform is readily scalable to multiple simultaneous recording sites, and adaptable to study transfer functions between stimulation events, neuronal activity, neurotransmitter release, and hemodynamic responses.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response.

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Monitoring the haemodynamic response to visual stimulation in glaucoma patients

In this paper, we used time-domain functional near infrared spectroscopy (TD-fNIRS) to evaluate the haemodynamic response function (HRF) in the occipital cortex following visual stimulation in glaucomatous eyes as compared to healthy eyes. A total of 98 subjects were enrolled in the study and clinically classified as healthy subjects, glaucoma patients (primary open-angle glaucoma) and mixed subjects (i.e. with a different classification for the two eyes). After quality check data were used from HRF of 73 healthy and 62 glaucomatous eyes. The amplitudes of the oxygenated and deoxygenated haemoglobin concentrations, together with their latencies with respect to the stimulus onset, were estimated by fitting their time course with a canonical HRF. Statistical analysis showed that the amplitudes of both haemodynamic parameters show a significant association with the pathology and a significant discriminating ability, while no significant result was found for latencies. Overall, our findings together with the ease of use and noninvasiveness of TD-NIRS, make this technique a promising candidate as a supporting tool for a better evaluation of the glaucoma pathology.

Simultaneous Measurement of the BOLD Effect and Metabolic Changes in Response to Visual Stimulation Using the MEGA-PRESS Sequence at 3 T.

The blood oxygen level dependent (BOLD) effect that provides the contrast in functional magnetic resonance imaging (fMRI) has been demonstrated to affect the linewidth of spectral peaks as measured with magnetic resonance spectroscopy (MRS) and through this, may be used as an indirect measure of cerebral blood flow related to neural activity. By acquiring MR-spectra interleaved with frames without water suppression, it may be possible to image the BOLD effect and associated metabolic changes simultaneously through changes in the linewidth of the unsuppressed water peak. The purpose of this study was to implement this approach with the MEGA-PRESS sequence, widely considered to be the standard sequence for quantitative measurement of GABA at field strengths of 3 T and lower, to observe how changes in both glutamate (measured as Glx) and GABA levels may relate to changes due to the BOLD effect. MR-spectra and fMRI were acquired from the occipital cortex (OCC) of 20 healthy participants whilst undergoing intrascanner visual stimulation in the form of a red and black radial checkerboard, alternating at 8 Hz, in 90 s blocks comprising 30 s of visual stimulation followed by 60 s of rest. Results show very strong agreement between the changes in the linewidth of the unsuppressed water signal and the canonical haemodynamic response function as well as a strong, negative, but not statistically significant, correlation with the Glx signal as measured from the OFF spectra in MEGA-PRESS pairs. Findings from this experiment suggest that the unsuppressed water signal provides a reliable measure of the BOLD effect and that correlations with associated changes in GABA and Glx levels may also be measured. However, discrepancies between metabolite levels as measured from the difference and OFF spectra raise questions regarding the reliability of the respective methods.

Hemodynamic matrix factorization for functional magnetic resonance imaging

The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs.Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing.Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation.In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks.The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.

Hemodynamic Scaling of Task-Induced Signal Changes in Tumor Subjects.

Background: FMRI signal amplitude can change during stimulus presentation due to underlying neural function and hemodynamic responses limiting the accuracy of fMRI in pre-surgical planning. To account for these changes in fMRI activation signal, we used breath-hold tasks to mimic hemodynamic changes in brain tumor subjects and scaled the activation response.Methods: Motor and/or language fMRI was performed in 21 subjects with brain tumor. A breath-hold task was also performed in these subjects to obtain the hemodynamic response changes independent of neural changes. The task activation signals were calibrated on a voxel wise basis for all the subjects. Direct cortical stimulation was used to verify the scaled results of task-based fMRI.Results: After scaling for the hemodynamic response function (HRF) on a voxel wise basis, the spatial extent of the scaled activation was more clustered together and appeared to minimize false positives. Similarly, accounting for the underlying canonical HRF, the percentage increase of active voxels after scaling had lower standard non-deviation suggesting that the activation response across voxels were more similar.Conclusion: Although preliminary in nature, this study suggests that the variation in hemodynamic changes can be calibrated using breath-hold in brain tumor subjects and can also be used for other clinical cases where the underlying HRF has been altered.

Tailored haemodynamic response function increases detection power of fMRI in awake dogs (Canis familiaris)

Functional magnetic resonance imaging (fMRI) of awake and unrestrained dogs (Canis familiaris) has been established as a novel opportunity for comparative neuroimaging, promising important insights into the evolutionary roots of human brain function and cognition. However, data processing and analysis pipelines are often derivatives of methodological standards developed for human neuroimaging, which may be problematic due to profound neurophysiological and anatomical differences between humans and dogs. Here, we explore whether dog fMRI studies would benefit from a tailored dog haemodynamic response function (HRF). In two independent experiments, dogs were presented with different visual stimuli. BOLD signal changes in the visual cortex during these experiments were used for (a) the identification and estimation of a tailored dog HRF, and (b) the independent validation of the resulting dog HRF estimate. Time course analyses revealed that the BOLD signal in the primary visual cortex peaked significantly earlier in dogs compared to humans, while being comparable in shape. Deriving a tailored dog HRF significantly improved the model fit in both experiments, compared to the canonical HRF used in human fMRI. Using the dog HRF yielded significantly increased activation during visual stimulation, extending from the occipital lobe to the caudal parietal cortex, the bilateral temporal cortex, into bilateral hippocampal and thalamic regions. In sum, our findings provide robust evidence for an earlier onset of the dog HRF in two visual stimulation paradigms, and suggest that using such an HRF will be important to increase fMRI detection power in canine neuroimaging. By providing the parameters of the tailored dog HRF and related code, we encourage and enable other researchers to validate whether our findings generalize to other sensory modalities and experimental paradigms.

Changes in Hemodynamic Response Function Resulting From Chronic Alcohol Consumption.

Functional MRI (fMRI) task-related analyses rely on an estimate of the brain's hemodynamic response function (HRF) to model the brain's response to events. Although changes in the HRF have been found after acute alcohol administration, the effects of heavy chronic alcohol consumption on the HRF have not been explored, and the potential benefits or pitfalls of estimating each individual's HRF on fMRI analyses of chronic alcohol use disorder (AUD) are not known. Participants with AUD and controls (CTL) received structural, functional, and vascular scans. During fMRI, participants were cued to tap their fingers, and averaged responses were extracted from the motor cortex. Curve fitting on these HRFs modeled them as a difference between 2 gamma distributions, and the temporal occurrence of the main peak and undershoot of the HRF was computed from the mean of the first and second gamma distributions, respectively. ANOVA and regression analyses found that the timing of the HRF undershoot increased significantly as a function of total lifetime drinking. Although gray matter volume in the motor cortex decreased with lifetime drinking, this was not sufficient to explain undershoot timing shifts, and vascular factors measured in the motor cortex did not differ among groups. Comparison of random-effects analyses using custom-fitted and canonical HRFs for CTL and AUD groups showed better results throughout the brain for custom-fitted versus canonical HRFs for CTL subjects. For AUD subjects, the same was true except for the basal ganglia. These findings suggest that excessive alcohol consumption is associated with changes in the HRF undershoot. HRF changes could provide a possible biomarker for the effects of lifetime drinking on brain function. Changes in HRF topography affect fMRI activation measures, and subject-specific HRFs generally improve fMRI activation results.

Optimization of wavelet coherence analysis as a measure of neural synchrony during hyperscanning using functional near-infrared spectroscopy.

.Significance: The expanding field of human social interaction is enabled by functional near-infrared spectroscopy (fNIRS) that acquires hemodynamic signals during live two-person interactions. These advances call for development of methods to quantify interactive processes.Aim: Wavelet coherence analysis has been applied to cross-brain neural coupling. However, fNIRS-specific computations have not been explored. This investigation determines the effects of global mean removal, wavelet equation, and choice of oxyhemoglobin versus deoxyhemoglobin signals.Approach: We compare signals with a known coherence with acquired signals to determine optimal computational approaches. The known coherence was calculated using three visual stimulation sequences of a contrast-reversing checkerboard convolved with the canonical hemodynamic response function. This standard was compared with acquired human fNIRS responses within visual cortex using the same sequences.Results: Observed coherence was consistent with known coherence with highest correlations within the wavelength range between 10 and 20 s. Removal of the global mean improved the correlation irrespective of the specific equation for wavelet coherence, and the oxyhemoglobin signal was associated with a marginal correlation advantage.Conclusions: These findings provide both methodological and computational guidance that enhances the validity and interpretability of wavelet coherence analysis for fNIRS signals acquired during live social interactions.

Supervised Brain Network Learning Based on Deep Recurrent Neural Networks

Task-based functional magnetic resonance imaging (tfMRI) is a widely used neuroimaging technique in exploring brain networks and functions associated with cognitive behaviors. Traditionally, the general linear model (GLM) is the most popular method in tfMRI data analysis due to its simpleness and robustness. This model-driven method adopts a canonical hemodynamic response function (HRF) and its various derivatives to construct regressors in the design matrix and estimate changes in the tfMRI data. However, a possible limitation of current model-driven methods is that the HRF is fixed and non-adaptive which may overlook other diverse and concurrent brain networks. In order to overcome these limitations, we proposed a novel hybrid framework, supervised brain network learning based on deep recurrent neural networks (SUDRNN), to reconstruct the diverse and concurrent functional brain networks. Specifically, this hybrid framework first takes advantage of the great capacity of deep recurrent neural networks (DRNN) in modeling sequential data to learn the diverse regressors from real tfMRI data. After that, it utilizes the effective supervised dictionary learning (SDL) method to reconstruct both the task-related functional brain networks and other latent brain networks simultaneously. Extensive experiment results on different tfMRI datasets from Human connectome project (HCP) demonstrated the superiority of the proposed framework.

Approach to optimize 3-dimensional brain functional activation image with high resolution: a study on functional near-infrared spectroscopy.

In this study, 3-dimensional (3-D) enhanced brain-function-map generation and estimation methodology is presented. Optical signals were modelled in the form of numerical optimization problem to infer the best existing waveform of canonical hemodynamic response function. Inter-channel activity patterns were also estimated. The estimation of activation of inter-channel gap depends on the minimization of generalized cross-validation. 3-D brain activation maps were produced through inverse discrete cosine transform. The proposed algorithm acquired significant results for 3-D functional maps with high resolution, in comparison with that of 2-D functional t-maps. A comprehensive analysis by exhibiting images corresponding to several layers has also been appended.

Hemodynamic Response Function Modeling to Determine the Areas with High Blood Supply in Block-Design fMRI Experiments

Background: Determining the activated brain areas due to different activities is one of the most common targets in functional magnetic resonance imaging (fMRI) data analysis, which could be carried out by hemodynamic response function (HRF) evaluation. The HR functions reflect changes of cerebral blood flow (CBF) in response to neural activity. Objectives: In this study, five models of HRF estimation were evaluated based on a simulated dataset. Models with higher accuracy were used to determine HRF parameters of the block-design fMRI data. Methods: The fMRI data were acquired in a 3 Tesla scanner. For block-design fMR imaging, CO2 gas was administered using a face-mask under physiological monitoring. Three patients with brain tumors were scanned. The fMRI data analysis was performed using the SPM 12 MATLAB toolbox. Akaike’s information criterion (AIC), Schwarz’ Bayesian (SBC), and mean square error (MSE) criteria were used to select the best HRF estimation model. Results: In simulation studies, the estimated HRFs by the canonical HRF plus its temporal derivative (TD), finite impulse response (FIR), and inverse logistic (IL) models were almost equal to the standard HRF. Mean square error, AIC, and SBC indices were ignorable for TD, FIR, and IL models (MSE/AIC/SBC magnitudes for TD, FIR, and IL models were 0.052/-1235.1/-1223.9, 0.055/-1206.4/-1194.9, and 0.068/-1091.5/-1049.2, respectively), which indicates that these models could accurately estimate HRF in block design fMRI studies. Conclusions: The HRF models could non-invasively evaluate the change of MR signal intensity under cerebrovascular reactivity (CVR) conditions and they might be helpful to investigate changes in human cerebral blood flow.