Subject‐level reliability analysis of fast fMRI with application to epilepsy

The hemodynamic response function (HRF) links neuronal activity to functional magnetic resonance imaging (fMRI) signals. While most fMRI studies use a canonical HRF, increasing evidence from studies of healthy subjects suggests that the HRF depends on anatomical location and disease states. Here, we investigate how HRF variability relates to anatomical location and pathology in the epileptic brain, using a large simultaneous electroencephalogram and fMRI dataset. Applying HRF deconvolution and temporal decomposition, we built the first whole-brain HRF library specific to epilepsy, identifying four distinct shape groups. We mapped HRF features across parcellations of two atlases using novel Bayesian hierarchical models. In non-epileptogenic regions, HRF shape and spatial distributions align with findings from healthy subjects. Within pathological regions, they vary significantly according to pathology. Our results indicate that HRF variability is associated with pathology, in addition to its dependence on anatomical location, motivating region- and pathology-based HRF modulation in epilepsy studies.

Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI

Pain is a complex experience that is thought to emerge from the activity of multiple brain areas, some of which are inconsistently detected using traditional fMRI analysis. One hypothesis is that the traditional analysis of pain-related cerebral responses, by relying on the correlation of a predictor and the canonical hemodynamic response function (HRF)- the general linear model (GLM)- may under-detect the activity of those areas involved in stimulus processing that do not present a canonical HRF. In this study, we employed an innovative data-driven processing approach- an inter-run synchronization (IRS) analysis- that has the advantage of not establishing any pre-determined predictor definition. With this method we were able to evidence the involvement of several brain regions that are not usually found when using predictor-based analysis. These areas are synchronized during the administration of mechanical punctate stimuli and are characterized by a BOLD response different from the canonical HRF. This finding opens to new approaches in the study of pain imaging.

In an event-related functional MRI data analysis, an accurate and robust extraction of the hemodynamic response function (HRF) and its associated statistics (e.g., magnitude, width, and time to peak) is critical to infer quantitative information about the relative timing of the neuronal events in different brain regions. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) to accurately estimate HRFs pertaining to each stimulus sequence across all voxels. MASM explicitly accounts for both spatial and temporal smoothness information, while incorporating such information to adaptively estimate HRFs in the frequency domain. One simulation study and a real data set are used to demonstrate the methodology and examine its finite sample performance in HRF estimation, which confirms that MASM significantly outperforms the existing methods including the smooth finite impulse response model, the inverse logit model and the canonical HRF.

.SignificanceAdvances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF.AimsWe compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM.ApproachWe augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with “short-separation regression” designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests.ResultsWe found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment.ConclusionsProperly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.

BOLD fMRI has become a prevalent method to study cerebral sensory processing in rodent disease models, including pain and mechanical hypersensitivity. fMRI data analysis is frequently combined with a general-linear-model (GLM) -based analysis, which uses the convolution of a hemodynamic response function (HRF) with the stimulus paradigm. However, several studies indicated that the HRF differs across species, sexes, brain structures, and experimental factors, including stimulation modalities or anesthesia, and hence might strongly affect the outcome of BOLD analyzes. While considerable work has been done in humans and rats to understand the HRF, much less is known in mice. As a prerequisite to investigate mechano-sensory processing and BOLD fMRI data in male and female mice, we (1) designed a rotating stimulator that allows application of two different mechanical modalities, including innocuous von Frey and noxious pinprick stimuli and (2) determined and statistically compared HRFs across 30 brain structures and experimental conditions, including sex and, stimulus modalities. We found that mechanical stimulation lead to brain-wide BOLD signal changes thereby allowing extraction of HRFs from multiple brain structures. However, we did not find differences in HRFs across all brain structures and experimental conditions. Hence, we computed a whole-brain mouse HRF, which is based on 88 functional scans from 30 mice. A comparison of this mouse-specific HRF with our previously reported rat-derived HRF showed significantly slower kinetics in mice. Finally, we detected pronounced differences in cerebral BOLD activation between male and female mice with mechanical stimulation, thereby exposing divergent processing of noxious and innocuous stimuli in both sexes.

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.

Using patient-specific hemodynamic response function in epileptic spike analysis of human epilepsy: a study based on EEG–fNIRS

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.

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.

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG–fMRI acquisitions, especially when a reference paradigm is unavailable.

The hemodynamic response function (HRF) describes the dynamic process of the blood oxygen level dependent (BOLD) signal evoked by a very short stimulus of unit intensity. Understanding HRF is a key issue for exploring the underlying dynamics of nervous system activation. Some researches suggested that the shape of HRF varied with different brain regions and different subjects, even at different time point in the same brain region of a subject. In this work, epilepsy discharges were adopted as markers of short stimuli, and then we use EEG-fMRI method to investigate HRF in temporal lobe epilepsy with interictal regional slow wave. Five patients with idiopathic temporal lobe epilepsy were enrolled in this study, the peak delay and amplitude of the estimated HRF was compared with Gamma, Glover and the SPM canonical HRF, and the results show that the delay of the estimated HRF is different from either one of the three theoretical model, thus further study on both theoretical model and real data estimation is needed.

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The procedure to estimate brain activity based on fMR signals is a process based on many assumptions. Some of the methods such as GLM (General Linear Model) and ICA(Independent Component Analysis) used for this purpose contain several restrictions. In GLM, it is assumed that each active voxel responds similarly and linearly towards a given sitmulus. In ICA, an unsurmountable number of independent time series are produced, one of which is assumed to reflect the activity pattern. In this study, we used minimal number of assumptions to estimate an underlying HRF (hemodynamic response function) from a given fMR time series, and then used the estimated HRFs to classify voxels as active or passive. We have investigated results from simulations and real fMR experiments.

Magnetic resonance imaging is a popular non-invasive technique for investigating soft tissue structures within the human body. Many design methods now exist for their principle hardware components, such as the radio frequency (RF) coils. A popular RF coil type is the RF phased array, comprised of many closely spaced coils covering a large volume. A time harmonic inverse method is presented for the theoretical design of RF phased arrays. The method allows any array size to be considered where the focus is on optimal coil geometry and position of individual coils. An ill-conditioned integral equation is solved using a regularisation strategy in which the error between induced and target magnetic fields is minimised along with an additional constraint related to the curvature of the coil windings. The method is demonstrated for a number of design considerations and includes the ability to focus the RF field to arbitrary locations within the coil volume. The effect of the choice of magnetic field polarisation direction is also investigated using the model. References Jin J. 1999. Electromagnetic Analysis and Design in Magnetic Resonance Imaging, Florida: CRC Press. Lawrence B. G., Crozier S., Yau D. D. and Doddrell D. M. 2002. A time-harmonic inverse methodology for the design of rf coils in mri, IEEE Transactions on Biomedical Engineering 49(1), 64--71. doi:10.1109/10.972841 Li B. K., Liu F. and Crozier S. 2005. Focused, eight-element transceive phased array coil for parallel magnetic resonance imaging of the chest --- theoretical considerations, Magnetic Resonance in Medicine 53, 1251--1257. doi:10.1002/mrm.20505 Ramo S., Whinnery J. R. and van Duzer T. 1967. Fields and waves in communication electronics, New York: John Wiley and Sons, Inc. Roemer P. B., Edelstein W. A., Hayes C. E., Souza S. P. and Mueller O. M. 1990. The nmr phased array, Magnetic Resonance in Medicine 16(2), 192--225. doi:10.1002/mrm.1910160203 Sodickson D. K., McKenzie C. A., Ohliger M. A., Yeh E. N. and Price M. D. 2002. Recent advances in image reconstruction, coil sensitivity calibration, and coil array design for SMASH and generalized parallel mri, Magnetic Resonance Materials in Physics, Biology and Medicine 13, 158--163. Turner R. 1986. A target field approach to optimal coil design, Journal of Physics D: Applied Physics 19, L147--L151. doi:10.1088/0022-3727/19/8/001 While P. T., Forbes L. K. and Crozier S. 2005a. A time-harmonic target-field method for designing unshielded rf coils in mri, Measurement Science and Technology 16, 997--1006. doi:10.1088/0957-0233/16/4/012 While P. T., Forbes L. K. and Crozier S. 2005b. A time-harmonic target-field method for designing shielded rf coils in mri, Measurement Science and Technology 16, 1381--1393. doi:10.1088/0957-0233/16/6/021 While P. T., Forbes L. K. and Crozier S. 2006. An inverse method for designing loaded rf coils in mri, Measurement Science and Technology 17, 2506--2518. doi:10.1088/0957-0233/17/9/019 Brideson M. A., Forbes L. K. and Crozier S. 2002. Determining complicated winding patterns for shim coils using streamfunctions and the target-field method, Concepts in Magnetic Resonance 14(1), 9--18. doi:10.1002/cmr.10000 While P. T., Forbes L. K. and Crozier S. 2007. An inverse method for designing rf phased array coils in mri --- theoretical considerations, Measurement Science and Technology 18, 245--259. doi:10.1088/0957-0233/18/1/031 Delves L. M. and Mohamed J. L. 1985. Computational methods for integral equations, Cambridge: Cambridge University Press. Forbes L. K. and Crozier S. 2002. A novel target-field method for finite-length magnetic resonance shim coils: II. tesseral shims, Journal of Physics D: Applied Physics 35, 839--849. doi:10.1088/0022-3727/35/9/303 Hardy C. J., Cline H. E., Giaquinto R. O., Niendorf T., Grant A. K. and Sodickson D. K. 2006. 32-element receiver-coil array for cardiac imaging, Magnetic Resonance in Medicine 55, 1142--1149. doi:10.1002/mrm.20870