Relating BOLD fMRI and neural oscillations through convolution and optimal linear weighting
Introduction
The exact relationship between neuronal events and haemodynamic changes measured with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is still unknown, although many recent studies, including those described below, have provided a qualitative correspondence. BOLD fMRI lacks a direct relationship to neural properties, although a correspondence to neural amplitude or duration has been found in specific paradigms (Bellgowan et al., 2003).
The local neuromagnetic fields generated by current flow in the dendrites of cortical pyramidal cells can be measured noninvasively above the scalp with magnetoencephalography (MEG). The neural current flow generating these fields is related to the local field potentials (LFPs) measured invasively (Hamalainen et al., 1993) and reflects input and intracortical processing. Local neural potentials/fields have also been shown to overlap spatially with gradient-echo (GE) BOLD data (Brookes et al., 2005, Martuzzi et al., 2009, Singh et al., 2002, Winterer et al., 2007) and to be the cause of the majority of metabolic demand which drives the BOLD response (Attwell and Laughlin, 2001). Logothetis et al. (2001) convolved LFPs and single- and multiunit spikes recorded in monkeys with a haemodynamic response function (HRF) and found LFPs to correlate with BOLD contrast better than spike output. Using invasive neural recordings in humans and comparing to fMRI obtained in different subjects, Mukamel et al. (2005) band pass-filtered the LFPs at multiple frequency bands and showed a negative correlation of BOLD with low-frequency LFPs and a positive correlation with higher frequencies.
BOLD responses are often modelled as a convolution of neural events with an HRF; however, the neural events are usually approximated by the stimulus timing rather than being directly measured. The shape of the HRF is often approximated by a single or sum of two Gamma functions. Deconvolving the fMRI response with the HRF is important in understanding the dynamics of the underlying neural activity (Glover, 1999, Gitelman et al., 2003). However, large variability has been shown across individual subjects' measured HRFs and this variability can affect parameter estimation of significant changes and detection of significant voxels (Handwerker et al., 2004, Lu et al., 2006).
Previous studies have compared MEG or electroencephalography (EEG) data to fMRI in a variety of ways. Studies have used EEG sensor-level data (Mart et al., 2004, Parkes et al., 2006, Tyvaert et al., 2008) or MEG broadband power from dipole fits (Nangini et al., 2008) to convolve with the HRF to improve detection of BOLD changes. In experiments to study the resting state in which no stimulus was presented, EEG data recorded concurrently with fMRI were the only available information with which to investigate BOLD correlates (Gonçalves et al., 2006). De Munck et al. (2007) recorded simultaneous EEG and fMRI and deconvolved the BOLD with the EEG alpha power to obtain an ‘alpha response function’ rather than assuming the traditional HRF. They found a negative correlation in visual cortex and positive correlation in thalamus, as reported previously (Laufs et al., 2003, Gonçalves et al., 2006).
Here, we extend previous work by applying a time–frequency beam former (Dalal et al., 2008) on MEG data to extract a time–frequency spectrogram at every voxel location in the brain and compare with BOLD fMRI data. Analysis was focussed on voxels within a visual cortex region of interest. Use of the subject-specific HRF was compared to the double-Gamma HRF (provided in SPM, www.fil.ion.ucl.ac.uk/spm). Two spatial–temporal–spectral comparisons were made: (1) MEG convolved with the HRF to predict BOLD and (2) fMRI deconvolved with the HRF to predict MEG. We show excellent agreement across the frequency spectrum between BOLD correlated with MEG, and BOLD correlated with invasive neuroelectrical recordings (Mukamel et al., 2005).
Section snippets
Acquisition
Six healthy subjects gave informed consent and participated in the study. Ethical approval was obtained prior to data collection from the University of Nottingham Medical School Ethics Committee. A sinusoidal drifting grating (drifting at 8 Hz) was presented in a circular window with a radius of 2.5° of visual angle, and with centre of the circular window at 4° from fixation, in the lower left quadrant of the visual field. Three Michelson contrasts (0.25, 0.5 and 1) were presented
Results
Both MEG and fMRI results showed responses in visual cortex consistent with previous findings for this stimulus (Stevenson et al., 2008). Changes in neural oscillatory power in motor cortex were observed due to the subjects' button press, but as the fMRI slice coverage did not include motor cortex, these results were not further analysed.
Discussion
Both MEG and fMRI activity is observed in primary visual cortex as well as surrounding areas, indicating a general correspondence of underlying activity between the methods. Lower frequencies in MEG source estimates are negatively temporally correlated with fMRI whilst higher frequencies are positively correlated. These results are consistent whether using forward convolution or deconvolution and whether using a subject-specific HRF or standard SPM HRF. Furthermore, MEG time–frequency beam
Conclusion
In conclusion, the results here support the trend that lower neural oscillation frequencies negatively correlate with BOLD fMRI and higher frequencies positively correlate. Both convolution and deconvolution can be used to assess this correlation. Using a time–frequency beam former on MEG data helped elucidate these findings. However, the robustness of these findings remains to be tested on different paradigms.
Acknowledgments
The authors would like to thank Dr. Sarang S. Dalal for help with NUTMEG and the time–frequency beam former. Funding was provided by the Medical Research Council, the Engineering and Physical Sciences Research Council, the Wellcome Trust and the University of Nottingham. JMZ is funded by the Whitaker International Scholar program.
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