Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study

Signal fluctuations in functional magnetic resonance imaging (fMRI) can result from a number of sources that may have a neuronal, physiologic or instrumental origin. To determine the relative contribution of these sources, we recorded physiological (respiration and cardiac) signals simultaneously with fMRI in human volunteers at rest with their eyes closed. State-of-the-art technology was used including high magnetic field (7 T), a multichannel detector array and high-resolution (3 mm³) echo-planar imaging. We investigated the relative contribution of thermal noise and other sources of variance to the observed fMRI signal fluctuations both in the visual cortex and in the whole brain gray matter. The following sources of variance were evaluated separately: low-frequency drifts due to scanner instability, effects correlated with respiratory and cardiac cycles, effects due to variability in the respiratory flow rate and cardiac rate, and other sources, tentatively attributed to spontaneous neuronal activity. We found that low-frequency drifts are the most significant source of fMRI signal fluctuations (3.0% signal change in the visual cortex, TE=32 ms), followed by spontaneous neuronal activity (2.9%), thermal noise (2.1%), effects due to variability in physiological rates (respiration 0.9%, heartbeat 0.9%), and correlated with physiological cycles (0.6%). We suggest the selection and use of four lagged physiological noise regressors as an effective model to explain the variance related to fluctuations in the rates of respiration volume change and cardiac pulsation. Our results also indicate that, compared to the whole brain gray matter, the visual cortex has higher sensitivity to changes in both the rate of respiration and the spontaneous resting-state activity. Under the conditions of this study, spontaneous neuronal activity is one of the major contributors to the measured fMRI signal fluctuations, increasing almost twofold relative to earlier experiments under similar conditions at 3 T.     

Functional MRI Using Spin Lock Editing Preparation Pulses

A novel approach for detecting blood oxygenation level-dependent (BOLD) signals in the brain is investigated using spin locking (SL) pulses to selectively edit the effects of extravascular diffusion in field gradients from different sized vascular structures. We show that BOLD effects from diffusion amongst susceptibility gradients will contribute significantly not only to transverse relaxation rates (R₂* and R₂) but also to R_1ρ, the rate of longitudinal relaxation in the rotating frame. Similar to the ability of 180-degree pulses to refocus static dephasing effects in a spin echo, moderately strong SL pulses can also reduce contributions of diffusion in large-scale gradients and the choice of SL amplitude can be used to selectively emphasize smaller scale inhomogeneities (such as microvasculature) and to drastically reduce the influence of larger structures (such as veins). Moreover, measurements over a range of locking fields can be used to derive estimates of the spatial scales of intrinsic gradients. The method was used to detect BOLD activation in human visual cortex. Eight healthy young adults were imaged at 3 T using a single-slice, SL-prepped turbo spin echo (TSE) sequence with spin-lock amplitudes ω₁ = 80 Hz and 400 Hz, along with conventional T₂*-weighted and T₂-prepped sequences. The BOLD signal varied from 1.1 ± 0.4 % (ω₁ = 80 Hz) to 0.7 ± 0.2 % (at 400 Hz), whereas the T₂-weighted sequence measured 1.3 ± 0.3 % and the T₂* sequence measured 1.9 ± 0.3 %. This new R_1ρ functional contrast can be made selectively sensitive to intrinsic gradients of different spatial scales, thereby increasing the spatial specificity of the evoked response.     

On the use of information theory for the analysis of the relationship between neural and imaging signals

Functional magnetic resonance imaging (fMRI) is a widely used method for studying the neural basis of cognition and of sensory function. A potential problem in the interpretation of fMRI data is that fMRI measures neural activity only indirectly, as a local change of deoxyhemoglobin concentration due to the metabolic demands of neural function. To build correct sensory and cognitive maps in the human brain, it is thus crucial to understand whether fMRI and neural activity convey the same type of information about external correlates. While a substantial experimental effort has been devoted to the simultaneous recordings of hemodynamic and neural signals, so far, the development of analysis methods that elucidate how neural and hemodynamic signals represent sensory information has received less attention. In this article, we critically review why the analytical framework of information theory, the mathematical theory of communication, is ideally suited to this purpose. We review the principles of information theory and explain how they could be applied to the analysis of fMRI and neural signals. We show that a critical advantage of information theory over more traditional analysis paradigms commonly used in the fMRI literature is that it can elucidate, within a single framework, whether an empirically observed correlation between neural and fMRI signals reflects either a similar stimulus tuning or a common source of variability unrelated to the external stimuli. In addition, information theory determines the extent to which these shared sources of stimulus signal and of variability lead fMRI and neural signals to convey similar information about external correlates. We then illustrate the formalism by applying it to the analysis of the information carried by different bands of the local field potential. We conclude by discussing the current methodological challenges that need to be addressed to make the information-theoretic approach more robustly applicable to the simultaneous recordings of neural and imaging data.     

Continuous EEG-fMRI in patients with partial epilepsy and focal interictal slow-wave discharges on EEG

Purpose

To verify whether in patients with partial epilepsy and routine electroenecephalogram (EEG) showing focal interictal slow-wave discharges without spikes combined EEG–functional magnetic resonance imaging (fMRI) would localize the corresponding epileptogenic focus, thus providing reliable information on the epileptic source.

Methods

Eight patients with partial epileptic seizures whose routine scalp EEG recordings on presentation showed focal interictal slow-wave activity underwent EEG–fMRI. EEG data were continuously recorded for 24 min (four concatenated sessions) from 18 scalp electrodes, while fMRI scans were simultaneously acquired with a 1.5-Tesla magnetic resonance imaging (MRI) scanner. After recording sessions and MRI artefact removal, EEG data were analyzed offline. We compared blood oxygen level-dependent (BOLD) signal changes on fMRI with EEG recordings obtained at rest and during activation (with and without focal interictal slow-wave discharges).

Results

In all patients, when the EEG tracing showed the onset of focal slow-wave discharges on a few lateralized electrodes, BOLD-fMRI activation in the corresponding brain area significantly increased. We detected significant concordance between focal EEG interictal slow-wave discharges and focal BOLD activation on fMRI. In patients with lesional epilepsy, the epileptogenic area corresponded to the sites of increased focal BOLD signal.

Conclusions

Even in patients with partial epilepsy whose standard EEGs show focal interictal slow-wave discharges without spikes, EEG–fMRI can visualize related focal BOLD activation thus providing useful information for pre-surgical planning.     

An investigation of the relationship between BOLD and perfusion signal changes during epileptic generalised spike wave activity

In pathological conditions interpretation of functional magnetic resonance imaging (fMRI) results can be difficult. This is due to a reliance on the assumed coupling between neuronal activity and changes in cerebral blood flow (CBF) and oxygenation. We wanted to investigate the coupling between blood oxygen level dependant contrast (BOLD) and CBF time courses in epilepsy patients with generalised spike wave activity (GSW) to better understand the underlying mechanisms behind the EEG-fMRI signal changes observed, especially in regions of negative BOLD response (NBR). Four patients with frequent GSW were scanned with simultaneous electroencephalographic (EEG)-fMRI with BOLD and arterial spin labeling (ASL) sequences. We examined the relationship between simultaneous CBF and BOLD measurements by looking at the correlation of the two signals in terms of percentage signal change on a voxel-by-voxel basis. This method is not reliant on coincident activation. BOLD and CBF were positively correlated in patients with epilepsy during background EEG activity and GSW. The subject average value of the Delta CBF/Delta BOLD slope lay between +19 and +36 and also showed spatial variation which could indicate areas with altered vascular response. There was not a significant difference between Delta CBF/Delta BOLD during GSW, suggesting that neurovascular coupling to BOLD signal is generally maintained between states and, in particular, within areas of NBR.     

Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA

Functional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the large scale. Most studies assume a simple relationship between neural and BOLD activity, in spite of the fact that it is important to elucidate how the “when” and “what” components of neural activity are correlated to the “where” of fMRI data. Here we conducted simultaneous recordings of neural and BOLD signal fluctuations in primary visual (V1) cortex of anesthetized monkeys. We explored the neurovascular relationship during periods of spontaneous activity by using temporal kernel canonical correlation analysis (tkCCA). tkCCA is a multivariate method that can take into account any features in the signals that univariate analysis cannot. The method detects filters in voxel space (for fMRI data) and in frequency–time space (for neural data) that maximize the neurovascular correlation without any assumption of a hemodynamic response function (HRF). Our results showed a positive neurovascular coupling with a lag of 4–5 s and a larger contribution from local field potentials (LFPs) in the γ range than from low-frequency LFPs or spiking activity. The method also detected a higher correlation around the recording site in the concurrent spatial map, even though the pattern covered most of the occipital part of V1. These results are consistent with those of previous studies and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI.