Hemodynamic Response Function Research Articles

A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS

Objective. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN). Approach. The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts. Main results. Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contain variants of shapes and amplitudes of HRF. Significance. The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.

Changes in neurovascular coupling with cerebral perfusion pressure indicate a link to cerebral autoregulation

Cerebral autoregulation ensures a stable average blood supply to brain tissue across steady state cerebral perfusion pressure (CPP) levels. Neurovascular coupling, in turn, relies on sufficient blood flow to meet neuronal demands during activation. These mechanisms break down in pathologies where extreme levels of CPP can cause dysregulation in cerebral blood flow. Here, we experimentally tested the influence of changes in CPP on neurovascular coupling in a hydrocephalus-type non-human primate model (n = 3). We recorded local neural and vascular evoked responses to a checkerboard visual stimulus, non-invasively, using electroencephalography and near-infrared spectroscopy respectively. The evoked signals showed changes in various waveform features in the visual evoked potentials and the hemodynamic responses, with CPP. We further used these signals to fit for a hemodynamic response function (HRF) to describe neurovascular coupling. We estimated n = 26 distinct HRFs at a subset of CPP values ranging from 40–120 mmHg across all subjects. The HRFs, when compared to a subject dependent healthy baseline (CPP 70–90 mmHg) HRF, showed significant changes in shape with increasing CPP (ρCPP = −0.55, p-valueCPP = 0.0049). Our study provides preliminary experimental evidence on the relationship between neurovascular coupling and CPP changes, especially when beyond the limits of static autoregulation.

Reappraisal-related downregulation of amygdala BOLD activation occurs only during the late trial window

During cognitive reappraisal, an individual reinterprets the meaning of an emotional stimulus to regulate the intensity of their emotional response. Prefrontal cortex activity has been found to support reappraisal and is putatively thought to downregulate the amygdala response to these stimuli. The timing of these regulation-related responses during the course of a trial, however, remains poorly understood. In the current fMRI study, participants were instructed to view or reappraise negative images and then rate how negative they felt following each image. The hemodynamic response function was estimated in 11 regions of interest for the entire time course of the trial including image viewing and rating. Notably, within the amygdala there was no evidence of downregulation in the early (picture viewing) window of the trial, only in the late (rating) window, which also correlated with a behavioral measure of reappraisal success. With respect to the prefrontal regions, some (e.g., inferior frontal gyrus) showed reappraisal-related activation in the early window, whereas others (e.g., middle frontal gyrus) showed increased activation primarily in the late window. These results highlight the temporal dynamics of different brain regions during emotion regulation and suggest that the amygdala response to negative images need not be immediately dampened to achieve successful cognitive reappraisal.

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.

Clinical Usefulness of Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging in Children With Focal Epilepsy.

Background and PurposeThe current study analyzed the interictal epileptiform discharge (IED)-related hemodynamic response and aimed to determine the clinical usefulness of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) in defining the epileptogenic zone (EZ) in children with focal epilepsy.MethodsPatients with focal epilepsy showing IEDs on conventional EEG were evaluated using EEG-fMRI. Statistical analyses were performed using the times of spike as events modeled with multiple hemodynamic response functions. The area showing the most significant t-value for blood-oxygen-level-dependent (BOLD) changes was compared with the presumed EZ. Moreover, BOLD responses between -9 and +9 s around the spike times were analyzed to track the hemodynamic response patterns over time.ResultsHalf (n=13) of 26 EEG-fMRI investigations of 19 patients were successful. Two patients showed 2 different types of spikes, resulting in 15 analyses. The maximum BOLD response was concordant with the EZ in 11 (73.3%) of the 15 analyses. In 10 (66.7%) analyses, the BOLD response localized the EZs more specifically. Focal BOLD responses in the EZs occurred before IEDs in 11 analyses and were often widespread after IEDs. Hemodynamic response patterns were consistent in the same epilepsy syndrome or when repeating the investigation in the same patients.ConclusionsEEG-fMRI can provide additional information for localizing the EZ in children with focal epilepsy, and also reveal the pathogenesis of pediatric epilepsy by evaluating the patterns in the hemodynamic response across time windows of IEDs.

Improved Activation and Hemodynamic Response Function of Olfactory fMRI Using Simultaneous Multislice with Reduced TR Acquisition.

Objectives The respiration could decrease the time synchronization between odor stimulation and data acquisition, consequently deteriorating the functional activation and hemodynamic response function (HRF) in olfactory functional magnetic resonance imaging (fMRI) with a conventional repetition time (TR). In this study, we aimed to investigate whether simultaneous multislice (SMS) technology with reduced TR could improve the blood oxygen level-dependent (BOLD) activation and optimize HRF modeling in olfactory fMRI. Methods Sixteen young healthy subjects with normal olfaction underwent olfactory fMRI on a 3T MRI scanner using a 64 channel head coil. FMRI data were acquired using SMS acceleration at three different TRs: 3000 ms, 1000 ms, and 500 ms. Both metrics of BOLD activation (activated voxels, mean, and maximum t-scores) and the HRF modeling (response height and time to peak) were calculated in the bilateral amygdalae, hippocampi, and insulae. Results The 500 ms and 1000 ms TRs both significantly improved the number of activated voxels, mean, and maximum t-score in the amygdalae and insulae, compared with a 3000 ms TR (all P < 0.05). But the increase of these metrics in the hippocampi did not reach a statistical significance (all P > 0.05). No significant difference in any BOLD activation metrics between TR 500 ms and 1000 ms was observed in all regions of interest (ROIs) (all P > 0.05). The HRF curves showed that higher response height and shorter time to peak in all ROIs were obtained at 500 ms and 1000 ms TRs compared to 3000 ms TR. TR 500 ms had a more significant effect on response height than TR 1000 ms in the amygdalae (P = 0.017), and there was no significant difference in time to peak between TR 500 ms and 1000 ms in all ROIs (all P > 0.05). Conclusions The fast image acquisition technique of SMS with reduced TR could significantly improve the functional activation and HRF curve in olfactory fMRI.

Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications

Functional Near-Infrared Spectroscopy (fNIRS) captures activations and inhibitions of cortical areas and implements a viable approach to neuromonitoring in clinical research. Compared to more advanced methods, continuous wave fNIRS (CW-fNIRS) is currently used in clinics for its simplicity in mapping the whole sub-cranial cortex. Conversely, it often lacks hardware reduction of confounding factors, stressing the importance of a correct signal processing. The proposed pipeline includes movement artifact reduction (MAR), bandpass filtering (BPF), and principal component analysis (PCA). Eight MAR algorithms were compared among 23 young adult volunteers under motor-grasping task. Single-subject examples are shown followed by the percentage in energy reduction (ERD%) statistics by single steps and cumulative values. The block average of the hemodynamic response function was compared with generalized linear model fitting. Maps of significant activation/inhibition were illustrated. The mean ERD% of pre-processed signals concerning the initial raw signal energy reached 4%. A tested multichannel MAR variant showed overcorrection on 4-fold more expansive windows. All of the MAR algorithms found similar activations in the contralateral motor area. In conclusion, single channel MAR algorithms are suggested followed by BPF and PCA. The importance of whole cortex mapping for fNIRS integration in clinical applications was also confirmed by our results.

Characterization of Hemodynamic Alterations in Schizophrenia and Bipolar Disorder and Their Effect on Resting-State fMRI Functional Connectivity.

Common and distinct neural bases of Schizophrenia (SZ) and bipolar disorder (BP) have been explored using resting-state fMRI (rs-fMRI) functional connectivity (FC). However, fMRI is an indirect measure of neural activity, which is a convolution of the hemodynamic response function (HRF) and latent neural activity. The HRF, which models neurovascular coupling, varies across the brain within and across individuals, and is altered in many psychiatric disorders. Given this background, this study had three aims: quantifying HRF aberrations in SZ and BP, measuring the impact of such HRF aberrations on FC group differences, and exploring the genetic basis of HRF aberrations. We estimated voxel-level HRFs by deconvolving rs-fMRI data obtained from SZ (N = 38), BP (N = 19), and matched healthy controls (N = 35). We identified HRF group differences (P < .05, FDR corrected) in many regions previously implicated in SZ/BP, with mediodorsal, habenular, and central lateral nuclei of the thalamus exhibiting HRF differences in all pairwise group comparisons. Thalamus seed-based FC analysis revealed that ignoring HRF variability results in false-positive and false-negative FC group differences, especially in insula, superior frontal, and lingual gyri. HRF was associated with DRD2 gene expression (P < .05, 1.62 < |Z| < 2.0), as well as with medication dose (P < .05, 1.75 < |Z| < 3.25). In this first study to report HRF aberrations in SZ and BP, we report the possible modulatory effect of dopaminergic signalling on HRF, and the impact that HRF variability can have on FC studies in clinical samples. To mitigate the impact of HRF variability on FC group differences, we suggest deconvolution during data preprocessing.

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.

Brief neuronal afterdischarges in the rat hippocampus lead to transient changes in oscillatory activity and to a very long-lasting decline in BOLD signals without inducing a hypoxic state

The effects of hippocampal neuronal afterdischarges (nAD) on hemodynamic parameters, such as blood-oxygen-level-dependent (BOLD) signals) and local cerebral blood volume (CBV) changes, as well as neuronal activity and metabolic parameters in the dentate gyrus, was investigated in rats by combining in vivo electrophysiology with functional magnetic resonance imaging (fMRI) or 1H-nuclear magnetic resonance spectroscopy (1H-NMRS). Brief electrical high-frequency pulse-burst stimulation of the right perforant pathway triggered nAD, a seizure-like activity, in the right dentate gyrus with a high incidence, a phenomenon that in turn caused a sustained decrease in BOLD signals for more than 30 min. The decrease was associated with a reduction in CBV but not with signs of hypoxic metabolism. nAD also triggered transient changes mainly in the low gamma frequency band that recovered within 20 min, so that the longer-lasting altered hemodynamics reflected a switch in blood supply rather than transient changes in ongoing neuronal activity. Even in the presence of reduced baseline BOLD signals, neurovascular coupling mechanisms remained intact, making long-lasting vasospasm unlikely. Subsequently generated nAD did not further alter the baseline BOLD signals. Similarly, nAD did not alter baseline BOLD signals when acetaminophen was previously administered, because acetaminophen alone had already caused a similar decrease in baseline BOLD signals as observed after the first nAD. Thus, at least two different blood supply states exist for the hippocampus, one low and one high, with both states allowing similar neuronal activity. Both acetaminophen and nAD switch from the high to the low blood supply state. As a result, the hemodynamic response function to an identical stimulus differed after nAD or acetaminophen, although the triggered neuronal activity was similar.

Imaging functional neuroplasticity in human white matter tracts

Magnetic resonance imaging (MRI) studies are sensitive to biological mechanisms of neuroplasticity in white matter (WM). In particular, diffusion tensor imaging (DTI) has been used to investigate structural changes. Historically, functional MRI (fMRI) neuroplasticity studies have been restricted to gray matter, as fMRI studies have only recently expanded to WM. The current study evaluated WM neuroplasticity pre–post motor training in healthy adults, focusing on motor learning in the non-dominant hand. Neuroplasticity changes were evaluated in two established WM regions-of-interest: the internal capsule and the corpus callosum. Behavioral improvements following training were greater for the non-dominant hand, which corresponded with MRI-based neuroplasticity changes in the internal capsule for DTI fractional anisotropy, fMRI hemodynamic response functions, and low-frequency oscillations (LFOs). In the corpus callosum, MRI-based neuroplasticity changes were detected in LFOs, DTI, and functional correlation tensors (FCT). Taken together, the LFO results converged as significant amplitude reductions, implicating a common underlying mechanism of optimized transmission through altered myelination. The structural and functional neuroplasticity findings open new avenues for direct WM investigations into mapping connectomes and advancing MRI clinical applications.

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis.

Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.

Enhanced design matrix for task-related fMRI data analysis

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.

Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey

Diffuse optical tomography (DOT), as a functional near-infrared spectroscopy (fNIRS) technique, can estimate three-dimensional (3D) images of the functional hemodynamic response in brain volume from measured optical signals. In this study, we applied DOT algorithms to the fNIRS data recorded from the surface of macaque monkeys’ skulls when the animals performed food retrieval tasks using either the left- or right-hand under head-free conditions. The hemodynamic response images, reconstructed by DOT with a high sampling rate and fine voxel size, demonstrated significant activations at the upper limb regions of the primary motor area in the central sulcus and premotor, and parietal areas contralateral to the hands used in the tasks. The results were also reliable in terms of consistency across different recording dates. Time-series analyses of each brain area revealed preceding activity of premotor area to primary motor area consistent with previous physiological studies. Therefore, the fNIRS–DOT protocol demonstrated in this study provides reliable 3D functional brain images over a period of days under head-free conditions for region-of-interest–based time-series analysis.

Effective Connectivity during an Avoidance-Based Pavlovian-to-Instrumental Transfer Task.

Pavlovian-to-instrumental transfer (PIT) refers to a phenomenon whereby a classically conditioned stimulus (CS) impacts the motivational salience of instrumental behavior. We examined behavioral response patterns and functional magnetic resonance imaging (fMRI) based effective connectivity during an avoidance-based PIT task. Eleven participants (8 females; Mage = 28.2, SD = 2.8, range = 25–32 years) completed the task. Effective connectivity between a priori brain regions engaged during the task was determined using hemodynamic response function group iterative multiple model estimation (HRF-GIMME). Participants exhibited behavior that was suggestive of specific PIT, a CS previously associated with a reinforcing outcome increased instrumental responding directed at the same outcome. We did not find evidence for general PIT; a CS did not significantly increase instrumental responding towards a different but related outcome. Using HRF-GIMME, we recovered effective connectivity maps among corticostriatal circuits engaged during the task. Group-level paths revealed directional effects from left putamen to right insula and from right putamen to right cingulate. Importantly, a direct effect of specific PIT stimuli on blood–oxygen-level-dependent (BOLD) activity in the left putamen was found. Results provide initial evidence of effective connectivity in key brain regions in an avoidance-based PIT task network. This study adds to the literature studying PIT effects in humans and employing GIMME models to understand how psychological phenomena are supported in the brain.

Contributions of Parasympathetic Arousal–Related Activity to Cognitive Performance in Patients With First-Episode Psychosis and Control Subjects

BackgroundCognitive impairment is integral to the pathophysiology of psychosis. Recent findings implicate autonomic arousal–related activity in both momentary fluctuations and individual differences in cognitive performance. Although altered autonomic arousal is common in patients with first-episode psychosis (FEP), its contribution to cognitive performance is unknown. MethodsA total of 24 patients with FEP (46% male, age = 24.31 [SD 4.27] years) and 24 control subjects (42% male, age = 27.06 [3.44] years) performed the Multi-Source Interference Task in-scanner with simultaneous pulse oximetry. First-level models included the cardiac-blood oxygen level–dependent regressor, in addition to task (congruent, interference, and error) and nuisance (motion and CompCor physiology) regressors. The cardiac-blood oxygen level–dependent regressor reflected parasympathetic arousal–related activity and was created by convolving the interbeat interval at each heartbeat with the hemodynamic response function. Group models examined the effect of group or cognitive performance (reaction times × error rate) on arousal-related and task activity, while controlling for sex, age, and framewise displacement. ResultsParasympathetic arousal–related activity was robust in both groups but localized to different regions for patients with FEP and healthy control subjects. Within both groups, arousal-related activity was significantly associated with cognitive performance across occipital and temporal cortical regions. Greater arousal-related activity in the bilateral prefrontal cortex (Brodmann area 9) was related to better performance in healthy control subjects but not patients with FEP. ConclusionsAutonomic arousal circuits contribute to cognitive performance and the pathophysiology of FEP. Arousal-related functional activity is a novel indicator of cognitive ability and should be incorporated into neurobiological models of cognition in psychosis.

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

Recent studies have demonstrated that fast fMRI can track neural activity well above the temporal limit predicted by the canonical hemodynamic response model. While these findings are promising, the biophysical mechanisms underlying these fast fMRI phenomena remain underexplored. In this study, we discuss two aspects of the hemodynamic response, complementary to several existing hypotheses, that can accommodate faster fMRI dynamics beyond those predicted by the canonical model. First, we demonstrate, using both visual and somatosensory paradigms, that the timing and shape of hemodynamic response functions (HRFs) vary across graded levels of stimulus intensity—with lower-intensity stimulation eliciting faster and narrower HRFs. Second, we show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF. Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. It is further noteworthy that, while the present investigations focus on fast neural responses, our findings suggest that a revised hemodynamic model may benefit the many fMRI studies using paradigms with wide ranges of contrast levels (e.g., resting or naturalistic conditions) or with modern, high-resolution MR acquisitions.

Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models.

Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.

RsHRF: A toolbox for resting-state HRF estimation and deconvolution

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.

Evaluation of hemodynamic response function during mental arithmetic task in fNIRS data using GLM method

To understand the complex structure of neural networks in the brain, which is a critical concept in Neuroscience, it is essential to identify different areas of the brain. Studies on the performance of various brain regions and their processing techniques have helped collect a large amount of data about the functions of these areas, which can provide deeper insights into the mechanism of this system. In line with this, the present study seeks to evaluate the hemodynamic response function (HRF) of the cerebral cortex in 8 healthy subjects (3 males and 5 females) during mental arithmetic task (Subtraction of single-digit numbers by two-digit numbers). This is conducted by data recording using functional near-infrared spectroscopy (fNIRS) with 52 channels to identify the active parts of their brain. Following the task (3 or 4 runs), we recorded the brain channel signals of the subjects, removing the noise from the extracted signals using wavelet transform technique. Then, the location of the channels and the active areas of the brain were identified in each individual and the group levels using e general linear model (GLM) method. The findings suggest that at the individual level, dorsolateral prefrontal cortex (DLPFC), supramarginal gyrus (SMG), left superior parietal lobule (left SPL), and right inferior frontal gyrus are activated during the mental arithmetic task (subtraction). Also, at the group level, the frontal lobe (dorsolateral prefrontal cortex (DLPFC)) and the parietal lobe (left superior parietal lobule (left SPL) are activated, which is compatible with brain activation during mental arithmetic task.