Parameter estimation in the magnitude-only and complex-valued fMRI data models

In functional magnetic resonance imaging, voxel time courses are complex-valued data but are traditionally converted to real magnitude-only data ones. At a large signal-to-noise ratio (SNR), the magnitude-only data Ricean distribution is approximated by a normal distribution that has been suggested as reasonable in magnitude-only data magnetic resonance images for an SNR of 5 and potentially as low as 3. A complex activation model has been recently introduced by Rowe and Logan [Rowe, D.B., and Logan, B.R. (2004). A complex way to compute fMRI activation. NeuroImage, 23 (3):1078-1092] that is valid for all SNRs. The properties of the parameter estimators and activation statistic for these two models and a more accurate Ricean approximation based on a Taylor series expansion are characterized in terms of bias, variance, and Cramer-Rao lower bound. It was found that the unbiased estimators in the complex model continued to be unbiased for lower SNRs while those of the normal magnitude-only data model became biased as the SNR decreased and at differing levels for the regression coefficients. The unbiased parameter estimators from the approximate magnitude-only Ricean Taylor model were unbiased to lower SNRs than the magnitude-only normal ones. Further, the variances of the parameter estimators achieved their minimum value in the complex data model regardless of SNR while the magnitude-only data normal model and Ricean approximation using a Taylor series did not as the SNR decreased. Finally, the mean activation statistic for the complex data model was higher and not SNR dependent while it decreased with SNR in the magnitude-only data models but less so for the approximate Ricean model. These results indicate that using the complex data model and not approximations to the true magnitude-only Ricean data model is more appropriate at lower SNRs. Therefore, since the computational cost is relatively low for the complex data model and since the SNR is not inherently known a priori for all voxels, the complex data model is recommended at all SNRs.     

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series

As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase.     

Dynamic Causal Modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.     

EEG signatures of auditory activity correlate with simultaneously recorded fMRI responses in humans

We recorded auditory-evoked potentials (AEPs) during simultaneous, continuous fMRI and identified trial-to-trial correlations between the amplitude of electrophysiological responses, characterised in the time domain and the time–frequency domain, and the hemodynamic BOLD response. Cortical AEPs were recorded from 30 EEG channels within the 3 T MRI scanner with and without the collection of simultaneous BOLD fMRI. Focussing on the Cz (vertex) EEG response, single-trial AEP responses were measured from time-domain waveforms. Furthermore, a novel method was used to characterise the single-trial AEP response within three regions of interest in the time–frequency domain (TF-ROIs). The latency and amplitude values of the N1 and P2 AEP peaks during fMRI scanning were not significantly different from the Control session (p > 0.16). BOLD fMRI responses to the auditory stimulation were observed in bilateral secondary auditory cortices as well as in the right precentral and postcentral gyri, anterior cingulate cortex (ACC) and supplementary motor cortex (SMC). Significant single-trial correlations were observed with a voxel-wise analysis, between (1) the magnitude of the EEG TF-ROI1 (70–800 ms post-stimulus, 1–5 Hz) and the BOLD response in right primary (Heschl's gyrus) and secondary (STG, planum temporale) auditory cortex; and (2) the amplitude of the P2 peak and the BOLD response in left pre- and postcentral gyri, the ACC and SMC. No correlation was observed with single-trial N1 amplitude on a voxel-wise basis. An fMRI-ROI analysis of functionally-identified auditory responsive regions identified further single-trial correlations of BOLD and EEG responses. The TF amplitudes in TF-ROI1 and TF-ROI2 (20–400 ms post-stimulus, 5–15 Hz) were significantly correlated with the BOLD response in all bilateral auditory areas investigated (planum temporale, superior temporal gyrus and Heschl's gyrus). However the N1 and P2 peak amplitudes, occurring at similar latencies did not show a correlation in these regions. N1 and P2 peak amplitude did correlate with the BOLD response in bilateral precentral and postcentral gyri and the SMC. Additionally P2 and TF-ROI1 both correlated with the ACC. TF-ROI3 (400–900 ms post-stimulus, 4–10 Hz) correlations were only observed in the ACC and SMC. Across the group, the subject-mean N1 peak amplitude correlated with the BOLD response amplitude in the primary and secondary auditory cortices bilaterally, as well as the right precentral gyrus and SMC. We confirm that auditory-evoked EEG responses can be recorded during continuous and simultaneous fMRI. We have presented further evidence of an empirical single-trial coupling between the EEG and BOLD fMRI responses, and show that a time–frequency decomposition of EEG signals can yield additional BOLD fMRI correlates, predominantly in auditory cortices, beyond those found using the evoked response amplitude alone.     

Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data

Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.     

Anatomically informed interpolation of fMRI data on the cortical surface

Analyzing functional magnetic resonance imaging (fMRI) data restricted to the cortical surface is of particular interest for two reasons: (1) to increase detection sensitivity using anatomical constraints and (2) to compare or use fMRI results in the context of source localization from magneto/electro-encephalography (MEEG) data, which requires data to be projected on the same spatial support. Designing an optimal scheme to interpolate fMRI raw data or resulting activation maps on the cortical surface relies on a trade-off between choosing large enough interpolation kernels, because of the distributed nature of the hemodynamic response, and avoiding mixing data issued from different anatomical structures. We propose an original method that automatically adjusts the level of such a trade-off, by defining interpolation kernels around each vertex of the cortical surface using a geodesic Vorono? diagram. This Vorono?-based interpolation method was evaluated using simulated fMRI activation maps, manually generated on an anatomical MRI, and compared with a more standard approach where interpolation kernels were defined as local spheres of radius r=3 or 5 mm. Several validation parameters were considered: the spatial resolution of the simulated activation map, the spatial resolution of the cortical mesh, the level of anatomical/functional data misregistration and the location of the vertices within the gray matter ribbon. Using an activation map at the spatial resolution of standard fMRI data, robustness to misregistration errors was observed for both methods, whereas only the Vorono?-based approach was insensitive to the position of the vertices within the gray matter ribbon.     

Influence of fMRI data sampling on the temporal characterization of the hemodynamic response

Experimental and modeling studies were used to estimate the effect of different sampling rates (repetition times, TR) and different sampling positions on the estimates of the temporal properties of the hemodynamic response function (HRF) derived from fMRI studies. Data were acquired at a TR of 250 ms and then subjected to various degrees of undersampling. Using a gaussian fitting function it is demonstrated that the accuracy of HRF peak time determination decreases with lower sampling rate (higher TR). The decrease in accuracy amounts to about 50 ms per second of TR increase. In addition, temporal shifts of the HRF peak time are found when reducing the influence of the more variable descending part of HRF curve by using a temporal cut-off after HRF peak time. The shift scales with TR, amounts up to 100 ms for a TR of 1500 ms and a cut-off of 3-4 s and depends on the sampling position. The use of the full HRF function does not lead to a shift but increases the influence of potential confounding factors as large veins and poststimulus undershoot. Since both accuracy and potential shifts of HRF peak determination scale with TR, it is important that temporal fMRI studies are carried out with high sampling rates.     

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on independent component analysis (ICA) for reducing gradient artifacts from spiral in–out and echo-planar pulse sequences at 3 T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen, P., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230–239.), principal component analysis (Niazy, R., Beckmann, C., Iannetti, G., Brady, J., Smith, S., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720–737.), Taylor series ( Wan, X., Iwata, K., Riera, J., Kitamura, M., Kawashima, R., 2006. Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts. Clin. Neurophysiol. 117, 681–692.) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (< 12.5 Hz) with high signal-to-noise ratio (SNR > 4). Our algorithms outperformed all of these methods on resting EEG in the theta and alpha bands (SNR > 4); however, for all methods, signal recovery was modest (SNR  1) in the beta band and poor (SNR < 0.3) in the gamma band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (< 0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3 T using spiral in–out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework.     

Search for patterns of functional specificity in the brain: a nonparametric hierarchical Bayesian model for group fMRI data

Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with previously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli.     

General and specific contributions of the medial prefrontal cortex to knowledge about mental states

Recent neuroimaging research (Mitchell, J.P., Heatherton, T.F., Macrae, C.N., 2002. Distinct neural systems subserve person and object knowledge. Proc. Natl. Acad. Sci. U. S. A. 99, 15238-15243.) has suggested that semantic knowledge about the psychological aspects of other people draws on a pattern of neural activity that differentiates social from nonsocial semantics. Although the medial prefrontal cortex (mPFC) clearly plays a central role in a range of such social-cognitive tasks, little is known about the precise contributions made by this region to social semantics. The current study addressed two outstanding questions regarding mPFC function. First, do mPFC contributions to processing words that refer to psychological states extend to other, nonhuman targets or are they specific to understanding the psychological experience of conspecifics? Second, does the mPFC respond generally to tasks that require processing another person, or is its activity specific to understanding psychological characteristics? To address these questions, participants were scanned using fMRI while judging the applicability of words to one of two types of targets: people or dogs. For each target, participants made one of two types of semantic judgment: does this word describe a potential psychological state of the target or does this word refer to a physical part of the target? Results demonstrated that greater mPFC activation accompanied judgments of psychological states than of body parts regardless of whether the target was a person or a dog, indicating that mPFC contributions to social semantics are specific for understanding psychological states--directly countering recent suggestions that mPFC responds generally to any judgment about another person--and that mPFC activity extends to targets other than conspecifics.     

EEG-fMRI of idiopathic and secondarily generalized epilepsies

We used simultaneous EEG and functional MRI (EEG-fMRI) to study generalized spike wave activity (GSW) in idiopathic and secondary generalized epilepsy (SGE). Recent studies have demonstrated thalamic and cortical fMRI signal changes in association with GSW in idiopathic generalized epilepsy (IGE). We report on a large cohort of patients that included both IGE and SGE, and give a functional interpretation of our findings. Forty-six patients with GSW were studied with EEG-fMRI; 30 with IGE and 16 with SGE. GSW-related BOLD signal changes were seen in 25 of 36 individual patients who had GSW during EEG-fMRI. This was seen in thalamus (60%) and symmetrically in frontal cortex (92%), parietal cortex (76%), and posterior cingulate cortex/precuneus (80%). Thalamic BOLD changes were predominantly positive and cortical changes predominantly negative. Group analysis showed a negative BOLD response in the cortex in the IGE group and to a lesser extent a positive response in thalamus. Thalamic activation was consistent with its known role in GSW, and its detection in individual cases with EEG-fMRI may in part be related to the number and duration of GSW epochs recorded. The spatial distribution of the cortical fMRI response to GSW in both IGE and SGE involved areas of association cortex that are most active during conscious rest. Reduction of activity in these regions during GSW is consistent with the clinical manifestation of absence seizures.     

fMRI探讨针刺足三里穴和下巨虚穴的大脑功能区分布

目的采用磁共振脑功能成像(fMRI)技术与针刺经络穴位相结合的方法来观察足阳明胃经上的足三里穴、下巨虚穴在大脑皮层功能区的分布位置。方法 64名健康志愿者,分为实验组和对照组,均按先后顺序针刺右侧足三里穴、上巨虚穴。使实验组处于得气状态,对照组处于不得气状态。针刺同时进行fMRI扫描。t检验分析得出刺激状态与静息状态信号对比的脑功能图像。结果 得气状态下fMRI图像显示两穴位的脑功能活动区定位无明显差别(P>0.05),主要位于双侧扣带回、岛叶、大脑外侧沟上壁及中央后回等部位。不得气状态下,fMRI图像显示两穴位的脑功能活动区定位亦无明显差别(P>0.05),主要位于左侧中央后回。同一穴位在得气和不得气两种状态下,脑功能活动区定位差别明显(P<0.01)。结论 针刺足三里、下巨虚两穴位治疗胃肠疾病有其科学基础;经络与中枢神经系统密切相关,针刺效应可能通过经络-大脑皮层-内脏这一途径完成。     

Gender differences in brain activity evoked by muscle and cutaneous pain: a retrospective study of single-trial fMRI data

Gender greatly influences pain processing. Not only do females display greater pain sensitivity, many chronic pain conditions affect females more than males. Although gender-based differences in pain sensitivity may be related to cultural and social factors, animal studies also reveal gender differences in pain sensitivity, suggesting that physiological factors may contribute to differences in the processing of pain in males and females. It has been recently reported that noxious cutaneous heat stimuli evoke gender-based differences in activity in some brain regions. Given that most chronic pain conditions, including those with gender bias are of "deep" origin (e.g. arising in muscle, joints or viscera), we investigated whether gender differences also exist in the central processing of muscle pain. In 24 healthy adults we used functional magnetic resonance imaging (fMRI) to measure signal intensity changes during muscle and cutaneous pain induced by intramuscular and subcutaneous injections of hypertonic saline, respectively. In addition to activating the "pain neuromatrix", i.e. cingulate, insular, somatosensory and cerebellar cortices, both muscle pain and cutaneous pain evoked gender-based differences in the mid-cingulate cortex, dorsolateral prefrontal cortex, hippocampus and cerebellar cortex. These differences may reflect differences in emotional processing of noxious information in men and women and may underlie the gender bias that exists in many chronic pain conditions.     

The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data

In the present study, we compared the effects of temporal compression (averaging across multiple scans) and space selection (i.e. selection of "regions of interest" from the whole brain) on single-subject and multi-subject classification of fMRI data using the support vector machine (SVM). Our aim was to investigate various data transformations that could be applied before training the SVM to retain task discriminatory variance while suppressing irrelevant components of variance. The data were acquired during a blocked experiment design: viewing unpleasant (Class 1), neutral (Class 2) and pleasant pictures (Class 3). In the multi-subject level analysis, we used a "leave-one-subject-out" approach, i.e. in each iteration, we trained the SVM using data from all but one subject and tested its performance in predicting the class label of the this last subject's data. In the single-subject level analysis, we used a "leave-one-block-out" approach, i.e. for each subject, we selected randomly one block per condition to be the test block and trained the SVM using data from the remaining blocks. Our results showed that in a single-subject level both temporal compression and space selection improved the SVM accuracy. However, in a multi-subject level, the temporal compression improved the performance of the SVM, but the space selection had no effect on the classification accuracy.     

On the use of continuous relative phase: Review of current approaches and outline for a new standard

Background

In this paper we review applications of continuous relative phase and commonly reported methods for calculating the phase angle. Signals with known properties as well as empirical data were used to compare methods for calculating the phase angle.

Findings

Our results suggest that the most valid, robust and intuitive results are obtained from the following steps: 1) centering the amplitude of the original signals around zero, 2) creating analytic signals from the original signals using the Hilbert transform, 3) calculating the phase angle using the analytic signal and 4) calculating the continuous relative phase.

Interpretations

The resulting continuous relative phase values are free of frequency artifacts, a problem associated with most normalization techniques, and the interpretation remains intuitive. We propose these methods for future research using continuous relative phase in studies and analyses of human movement coordination.     

fMRI investigation of the effect of local and systemic lidocaine on noxious electrical stimulation-induced activation in spinal cord

Spinal cord fMRI offers an excellent opportunity to quantify nociception using neuronal activation induced by painful stimuli. Measurement of the magnitude of stimulation-induced activation, and its suppression with analgesics can provide objective measures of pain and efficacy of analgesics. This study investigates the feasibility of using spinal cord fMRI in anesthetized rats as a pain assay to test the analgesic effect of locally and systemically administered lidocaine. Blood volume (BV)-weighted fMRI signal acquired after intravenous injection of ultrasmall superparamagnetic iron oxide (USPIO) particles was used as an indirect readout of the neuronal activity. Transcutaneous noxious electrical stimulation was used as the pain model. BV-weighted fMRI signal could be robustly quantified on a run-by-run basis, opening the possibility of measuring pharmacodynamics (PD) of the analgesics with a temporal resolution of 2 min. Local administration of lidocaine was shown to ablate all stimulation-induced fMRI signals by the total blockage of peripheral nerve transmission, while the analgesic effect of systemically administered lidocaine was robustly detected after intravenous infusion of 3 mg/kg, which is similar to clinical dosage for human. This study establishes spinal cord fMRI as a viable assay for analgesics. With respect to the mode of action of lidocaine, this study suggests that systemic lidocaine, which is clinically used for the treatment of neuropathic pain, and believed to only block the peripheral nerve transmission of abnormal neural activity (ectopic discharge) originating from the damaged peripheral nerves, also blocks the peripheral nerve transmission of normal neural activity induced by transcutaneous noxious electrical stimulation.     

Jesús Alanís and the First Recording of the His Bundle: The Scientist and the Man

The anatomy and physiology of the specialized conduction system has intrigued investigators since the 19th century and is still not fully understood. Dr. Wilhelm His Jr. is well known because he discovered the A‐V bundle, and Dr. Sunao Tawara is rightly credited with the discovery of the atrioventricular (AV) node, but who was the first to record the electrical activity of the His bundle? This paper reviews the historical background and scientific contributions made by Dr. Jesús Alanís in the middle of the 20th century working at the National Institute of Cardiology in Mexico City. Collaborating with outstanding investigators such as Arturo Rosenblueth, Dr. Alanís recorded for the first time the electrical activity of the His bundle in the isolate canine heart. That the recorded electrogram was indeed the His bundle and not the AV node was confirmed by detailed studies that set the basis for modern clinical electrophysiology. The life and research contributions of this extraordinary man are reviewed in the context of a unique group of investigators who made significant advances in cardiac electrophysiology.     

Differential alteration of fMRI signal variability in the ascending trigeminal somatosensory and pain modulatory pathways in migraine

BackgroundThe moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain. Here, we investigated the regional blood-oxygen-level-dependent signal variability (BOLD_SV) and inter-regional dynamic functional connectivity (dFC) in the interictal phase of migraine and its relationship with the attack severity.MethodsWe acquired resting-state functional magnetic resonance imaging from 20 migraine patients and 26 healthy controls (HC). We calculated the standard deviation (SD) of the BOLD time-series at each voxel as a measure of the BOLD signal variability (BOLD_SV) and performed a whole-brain voxel-wise group comparison. The brain regions showing significant group differences in BOLD_SV were used to define the regions of interest (ROIs). The SD and mean of the dynamic conditional correlation between those ROIs were calculated to measure the variability and strength of the dFC. Furthermore, patients’ experimental pain thresholds and headache pain area/intensity levels during the migraine ictal-phase were assessed for clinical correlations.ResultsWe found that migraineurs, compared to HCs, displayed greater BOLD_SV in the ascending trigeminal spinal-thalamo-cortical pathways, including the spinal trigeminal nucleus, pulvinar/ventral posteromedial (VPM) nuclei of the thalamus, primary somatosensory cortex (S1), and posterior insula. Conversely, migraine patients exhibited lower BOLD_SV in the top-down modulatory pathways, including the dorsolateral prefrontal (dlPFC) and inferior parietal (IPC) cortices compared to HCs. Importantly, abnormal interictal BOLD_SV in the ascending trigeminal spinal-thalamo-cortical and frontoparietal pathways were associated with the patient’s headache severity and thermal pain sensitivity during the migraine attack. Migraineurs also had significantly lower variability and greater strength of dFC within the thalamo-cortical pathway (VPM-S1) than HCs. In contrast, migraine patients showed greater variability and lower strength of dFC within the frontoparietal pathway (dlPFC-IPC).ConclusionsMigraine is associated with alterations in temporal signal variability in the ascending trigeminal somatosensory and top-down modulatory pathways, which may explain migraine-related pain and allodynia. Contrasting patterns of time-varying connectivity within the thalamo-cortical and frontoparietal pathways could be linked to abnormal network integrity and instability for pain transmission and modulation.