FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response

Flow augmentation therapies preserve brain network integrity and hemodynamics in a canine permanent occlusion model

The acute phase of ischemic stroke presents a critical window for therapeutic intervention, where novel approaches such as hyper-acute cerebral flow augmentation offer promising avenues for neuroprotection. In this study, we investigated the effects of two such therapies, NEH (a combination of norepinephrine and hydralazine) and Sanguinate (pegylated bovine carboxyhemoglobin), on resting-state functional connectivity, global mean signal (GMS), and blood oxygen level-dependent (BOLD) time lag in a pre-clinical canine model of stroke via permanent occlusion of the middle cerebral artery (total of n = 40 IACUC-approved mongrel canines randomly split into control/natural history and two treatment groups). Utilizing group independent component analysis (ICA), we identified and examined the integrity of sensorimotor and visual networks both pre- and post-occlusion, across treatment and control groups. Our results demonstrated that while the control group exhibited significant disruptions in these networks following stroke, the treatment groups showed remarkable preservation of network integrity. Voxel-wise functional connectivity analysis revealed less pronounced alterations in the treatment groups, suggesting maintained neural connections. Notably, the treatments stabilized GMS, with only minimal reductions observed post-occlusion compared to significant decreases in the control group. Furthermore, BOLD time-lag unity plots indicated that NEH and Sanguinate maintained consistent hemodynamic response timing, as evidenced by tighter clustering around the line of unity, suggesting a potential neuroprotective effect. These findings were underscored by robust statistical analyses, including paired T-tests and Mann-Whitney U tests, which confirmed the significance of the connectivity changes observed. The correlation of BOLD time-lag variations with neuroimaging functional biomarkers highlighted the impact of stroke and the efficacy of early therapeutic interventions. Our study supports the further study of flow augmentation therapies such as NEH and Sanguinate in stroke treatment protocols and suggests flow augmentation therapies should be further explored in an effort to improve patient outcomes.

Effects of cognitive behavioural therapy and exposure-response prevention on brain activation in obsessive-compulsive disorder patients: systematic review and meta-analysis

Current psychotherapeutic treatments for OCD, while effective, have complex outcomes with mixed efficacy. Previous research has observed baseline brain activation patterns in OCD patients, elucidating some of the implications of this disorder. Observing the effects of evidence-based psychotherapeutics for OCD on brain activation (through MRI) may provide a more comprehensive outline of pathology. This systematic review and meta-analysis evaluated the effects of cognitive behavioural therapy (CBT) with exposure-response prevention (ERP) on brain activation in OCD patients. Academic databases were systematically searched, and the outcomes evaluated included changes in brain activation and symptom severity between baseline and post-treatment. Patients (n = 193) had confirmed OCD diagnosis and underwent protocolized CBT with ERP programs delivered by trained therapists. Participants in the CBT with ERP programs demonstrated significant improvements in symptom severity (Cohen's d = - 1.91). In general, CBT with ERP resulted in decreased activation post-treatment in the frontal (Cohen's d = 0.40), parietal (Cohen's d = 0.79), temporal (Cohen's d = 1.02), and occipital lobe (Cohen's d = 0.76), and cerebellum (Cohen's d = - 0.78). The findings support CBT with ERP's ability to improve brain activation abnormalities in OCD patients. By identifying regions that improved activation levels, psychotherapy programs may benefit from the addition of function-specific features that could improve treatment outcomes.

Hemodynamic response function description in patients with glioma

INTRODUCTION

Functional 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.

METHODS

Forty 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.

RESULTS

Differences 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 CONCLUSION

There 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.

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.

The confound of hemodynamic response function variability in human resting-state functional MRI studies

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.

Using electronically delivered therapy and brain imaging to understand OCD pathology: A pilot feasibility study

Background

Obsessive–compulsive disorder (OCD) is a debilitating mental health disorder with current psychotherapeutic treatments, while somewhat effective, yielding low accessibility and scalability. A lack of knowledge regarding the neural pathology of OCD may be hindering the development of innovative treatments. Previous research has observed baseline brain activation patterns in OCD patients, elucidating some understanding of the implications. However, by using neuroimaging to observe the effects of treatment on brain activation, a more complete picture of OCD can be drawn. Currently, the gold standard treatment is cognitive behavioral therapy (CBT). However, CBT is often inaccessible, time-consuming, and costly. Fortunately, it can be effectively delivered electronically (e-CBT).

Objectives

This pilot study implemented an e-CBT program for OCD and observed its effects on cortical activation levels during a symptom provocation task. It was hypothesized that abnormal activations could be attenuated following treatment.

Methods

OCD patients completed a 16-week e-CBT program administered through an online platform, mirroring in-person content. Treatment efficacy was evaluated using behavioral questionnaires and neuroimaging. Activation levels were assessed at the resting state and during the symptom provocation task.

Results

In this pilot, seven participants completed the program, with significant improvements (p < 0.05) observed between baseline and post-treatment for symptom severity and levels of functioning. No statistically significant (p = 0.07) improvement was observed in the quality of life. Participants had mostly positive qualitative feedback, citing accessibility benefits, comprehensive formatting, and relatable content. No significant changes in cortical activation were observed between baseline and post-treatment.

Conclusion

This project sheds light on the application of e-CBT as a tool to evaluate the effects of treatment on cortical activation, setting the stage for a larger-scale study. The program showed great promise in feasibility and effectiveness. While there were no significant findings regarding changes in cortical activation, the trends were in agreeance with previous literature, suggesting future work could provide insight into whether e-CBT offers comparable cortical effects to in-person psychotherapy. Applying a greater knowledge of the neural mechanisms of action in OCD can help develop novel treatment plans in the future.

Characterization of Hemodynamic Alteration in Parkinson's Disease and Effect on Resting-State Connectivity

Functional magnetic resonance imaging (fMRI) is a convolution of latent neural activity and the hemodynamic response function (HRF). According to prior studies, the neurodegenerative process in idiopathic Parkinson's Disease (PD) interacts significantly with neuromuscular abnormalities. Although these underlying neuromuscular changes might influence the temporal characteristics of HRF and fMRI signals, relatively few studies have explored this possibility. We hypothesized that such alterations would engender changes in estimated functional connectivity (FC) in fMRI space compared to latent neural space. To test these theories, we calculated voxel-level HRFs by deconvolving resting-state fMRI data from PD patients (n = 61) and healthy controls (HC) (n = 47). Significant group differences in HRF (P < 0.05, Gaussian random field-corrected) were observed in several regions previously associated with PD. Subsequently, we focused on putamen-seed-based FC differences between the PD and HC groups using fMRI and latent neural signals. The results suggested that neglecting HRF variability may cultivate false-positive and false-negative FC group differences. Furthermore, HRF was related to dopamine receptor type 2 (DRD2) gene expression (P < 0.001, t = -7.06, false discover rate-corrected). Taken together, these findings reveal HRF variation and its possible underlying molecular mechanism in PD, and suggest that deconvolution could reduce the impact of HRF variation on FC group differences.

Modeling the dynamics of cerebrovascular reactivity to carbon dioxide in fMRI under task and resting-state conditions

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO₂). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO₂ response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO₂ changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCA_opt) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCA_opt and IL methods. Of the three, the CCA_opt method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

autohrf-an R package for generating data-informed event models for general linear modeling of task-based fMRI data

The analysis of task-related fMRI data at the level of individual participants is commonly based on general linear modeling (GLM), which allows us to estimate the extent to which the BOLD signal can be explained by the task response predictors specified in the event model. The predictors are constructed by convolving the hypothesized time course of neural activity with an assumed hemodynamic response function (HRF). However, our assumptions about the components of brain activity, including their onset and duration, may be incorrect. Their timing may also differ across brain regions or from person to person, leading to inappropriate or suboptimal models, poor fit of the model to actual data, and invalid estimates of brain activity. Here, we present an approach that uses theoretically driven models of task response to define constraints on which the final model is computationally derived using actual fMRI data. Specifically, we developed autohrf-an R package that enables the evaluation and data-driven estimation of event models for GLM analysis. The highlight of the package is the automated parameter search that uses genetic algorithms to find the onset and duration of task predictors that result in the highest fitness of GLM based on the fMRI signal under predefined constraints. We evaluated the usefulness of the autohrf package on two original datasets of task-related fMRI activity, a slow event-related spatial working memory study and a mixed state-item study using the flanker task, and on a simulated slow event-related working memory data. Our results suggest that autohrf can be used to efficiently construct and evaluate better task-related brain activity models to gain a deeper understanding of BOLD task response and improve the validity of model estimates. Our study also highlights the sensitivity of fMRI analysis with GLM to precise event model specification and the need for model evaluation, especially in complex and overlapping event designs.

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within the mouse brain's lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources.

Improved localization of language areas using single voxel signal analysis of unprocessed fMRI data

Activated brain regions can be visualized and localized with the use of fMRI (functional magnetic imaging). This is based on changes in the blood flow in activated regions, or more precisely on the hemodynamic response function (HRF) and the Blood-Oxygen-Level-Dependent (BOLD) effect. This study used a task-based fMRI examination with language paradigms in order to stimulate the language areas. The measured fMRI data are frequently altered by different preprocessing steps for the analysis and the display of activations. These changes can lead to discrepancies between the displayed and the truly measured location of the activations. Simple t-maps were created with unprocessed fMRI data, to provide a more realistic representation of the language areas. HRF-dependent single-voxel fMRI signal analysis was performed to improve the analyzability of these activation maps.

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

Significance: Although 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. Aim: To establish an fMRI-compatible, spectral, fiber-photometry platform for HRF calculation and validation in any rat brain region. Approach: We 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. Results: We 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. Conclusions: Our 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.

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.

Neurobiological outcomes of cognitive behavioral therapy for obsessive-compulsive disorder: A systematic review

INTRODUCTION

Obsessive-compulsive disorder (OCD) is characterized by recurrent distressing thoughts and repetitive behaviors, or mental rituals performed to reduce anxiety. Recent neurobiological techniques have been particularly convincing in suggesting that cortico-striatal-thalamic-cortico (CSTC) circuits, including orbitofrontal cortex (OFC) and striatum regions (caudate nucleus and putamen), are responsible for mediation of OCD symptoms. However, it is still unclear how these regions are affected by OCD treatments in adult patients. To address this yet open question, we conducted a systematic review of all studies examining neurobiological changes before and after first-line psychological OCD treatment, i.e., cognitive-behavioral therapy (CBT).

METHODS

Studies were included if they were conducted in adults with OCD and they assessed the neurobiological effects of CBT before and after treatment. Two databases were searched: PsycINFO and PubMed for the time frame up to May 2022.

RESULTS

We obtained 26 pre-post CBT treatment studies performed using different neurobiological techniques, namely functional magnetic resonance imaging (fMRI), Positron emission tomography (PET), regional cerebral blood flow (rCBF), 5-HT concentration, magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), Electroencephalography (EEG). Neurobiological data show the following after CBT intervention: (i) reduced activations in OFC across fMRI, EEG, and rCBF; (ii) decreased activity in striatum regions across fMRI, rCBF, PET, and MRI; (iii) increased activations in cerebellum (CER) across fMRI and MRI; (iv) enhanced neurochemical concentrations in MRS studies in OFC, anterior cingulate cortex (ACC) and striatum regions. Most of these neurobiological changes are also accompanied by an improvement in symptom severity as assessed by a reduction in the Y-BOCS scores.

CONCLUSION

Cognitive-behavioral therapy seems to be able to restructure, modify, and transform the neurobiological component of OCD, in addition to the clinical symptoms. Nevertheless, further studies are necessary to frame the OCD spectrum in a dimensional way.