Using fast fMRI (whole-brain 10 Hz sampling), we revealed the left hemisphere’s BOLD signal is more delayed by 500 ms when engaging a lexical discrimination task than a non-lexical discrimination task. EEG study on the same subjects suggested such BOLD signal delay is related to the oscillatory power in the beta band.
Hemodynamic responses can exhibit fine timing modulations by the stimulus and behaviors. Previously it was shown that inter-regional BOLD signal is closely related to reaction time1 in mental chronometry tasks and to neuronal oscillatory activities in choice reaction-time tasks2. In the human visual cortex, it has been shown that the elicited BOLD signals are temporally linear up to 0.5 Hz3. Sustained visual stimulation can generate oscillatory BOLD signal up to 0.75 Hz4. Together, these results suggested that the existence of fine timing information in fMRI signal. However, fine temporal characteristics of the BOLD signal in human auditory cortex are less explored, particularly in cases with different top-down modulations.
Here we used ultra-fast fMRI with a 10-Hz sampling rate5 and EEG to measure the BOLD and electrophysiological signals at the human auditory cortex in response to speech sounds. In particular, our design delivered the same speech sounds in two conditions with and without a lexical context. We hypothesized that BOLD signal at the auditory cortex exhibits different temporal characteristics because it receives different context-dependent feedback information. The EEG data were used to probe the neural correlates of BOLD temporal characteristics.
Subjects (n=14; 6 female; age: 22.7+/-1.8) participated in either a lexical tone or a color discrimination task after giving their written informed consents approved by the Institute Review Board. For “lexical” trials, subjects were asked to respond if the visual cue (digit) matched the tone of the presented Chinese speech sound. For “non-lexical” trials, subjects were asked to press a button after hearing the same sound stimulus if the visual cue was colored in red. The same experiment design was used for both fMRI and EEG data collection.
Data were collected on a 3T MRI scanner (Skyra, Siemens) using a 32-channel head coil array and the inverse imaging method (TR=0.1 s; TE=30 s; flip angle=30o)5. Two runs of data were collected for each task condition including about 90 trials in each run. Each run lasted 380 s. EEG were collected using a 64-channel cap and amplifier (Compumedics Neuroscan, USA) with the sampling rate of 1000 Hz. The vertical and horizontal eye movements were measured. The impedances of all electrodes were kept below 10kΩ during the experiment.
Functional MRI
were reconstructed with the minimum-norm estimates5. They were further analyzed by the General Linear Model, where hemodynamic
responses were modeled by finite impulse response functions. We chose the
time-to-half (TTH), the time reaching 50% of the maximal response as the fMRI
timing indices. EEG data were first re-referenced to the average of all
electrodes and then high-pass filtered (0.1 Hz). Time-frequency representation
(TFR) of the EEG signals were calculated using the Morlet wavelets (between 4
Hz and 80 Hz). The power of TFR was linearly detrended at each EEG electrode
and then summed across frequencies. The correlation between fMRI timing and EEG
power was estimated by bootstrap, where subjects were sampled with replacement
500 times to generate estimates of fMRI timing and EEG power for lexical and
non-lexical conditions separately. Linear regression was done over these bootstrap
samples to test the significance of the correlation.
1 Menon R. S., Luknowsky D. C. & Gati J. S.Proc Natl Acad Sci U S A.1998; 95:10902-10907.
2 Lin F.
H., Witzel T., Raij T. et al.Neuroimage.2013; 78:372-384.
3 Dale A. & Buckner R.Hum Brain Mapp.1997; 5:329-340.
4 Lewis L. D., Setsompop K., Rosen B. R. et al.Proc Natl Acad Sci U S A.2016; 113:E6679-E6685. 5 Lin F. H., Witzel T., Mandeville J. B. et al.Neuroimage.2008; 42:230-247.
6 Laufs H., Krakow K., Sterzer P. et al.Proc Natl Acad Sci U S A.2003; 100:11053-11058.
7 Fontolan L., Morillon B., Liegeois-Chauvel C. et al.Nat Commun.2014; 5:4694.