Assessing linear time-invariance in human primary somatosensory cortex with BOLD fMRI using vibrotactile stimuli

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Hemodynamic response varies across tactile stimuli with different temporal structures

Tactile stimuli can be distinguished based on their temporal features (e.g., duration, local frequency, and number of pulses), which are fundamental for vibrotactile frequency perception. Characterizing how the hemodynamic response changes in shape across experimental conditions is important for designing and interpreting fMRI studies on tactile information processing. In this study, we focused on periodic tactile stimuli with different temporal structures and explored the hemodynamic response function (HRF) induced by these stimuli. We found that HRFs were stimulus‐dependent in tactile‐related brain areas. Continuous stimuli induced a greater area of activation and a stronger and narrower hemodynamic response than intermittent stimuli with the same duration. The magnitude of the HRF increased with increasing stimulus duration. By normalizing the characteristics into topographic matrix, nonlinearity was obvious. These results suggested that stimulation patterns and duration within a cycle may be key characters for distinguishing different stimuli. We conclude that different temporal structures of tactile stimuli induced different HRFs, which are essential for vibrotactile perception and should be considered in fMRI experimental designs and analyses.

Transient and sustained components of the sensorimotor BOLD response in fMRI

Blood oxygenation level-dependent (BOLD) signal time courses in functional magnetic resonance imaging are estimated within the framework of general linear modeling by convolving an input function, that represents neural activity, with a canonical hemodynamic response function (HRF). Here we investigate the performance of different neural input functions and latency-optimized HRFs for modeling BOLD signals in response to vibrotactile somatosensory stimuli of variable durations (0.5, 1, 4, 7 s) in 14 young, healthy adults who were required to make button press responses at each stimulus cessation. Informed by electrophysiology and the behavioral task, three nested models with an increasing number of parameters were considered: a boxcar; boxcar and offset transient; and onset transient, boxcar and offset transient (TBT). The TBT model provided the best fit of the group-averaged BOLD time courses based on χ(2) and F statistics. Only the TBT model was capable of fitting the bimodal shape of the BOLD response to the 7-s stimulus and the relative peak amplitudes for all stimulus lengths in key somatosensory and motor areas. This suggests that the TBT model provides a more comprehensive description of brain sensorimotor responses in this experiment than provided by the simple boxcar model. Work comparing the activation maps obtained with the TBT model with magnetoencephalography data is under way.

Oscillatory response function: towards a parametric model of rhythmic brain activity

Rhythmic brain activity, measured by magnetoencephalography (MEG), is modulated during stimulation and task performance. Here, we introduce an oscillatory response function (ORF) to predict the dynamic suppression-rebound modulation of brain rhythms during a stimulus sequence. We derived a class of parametric models for the ORF in a generalized convolution framework. The model parameters were estimated from MEG data acquired from 10 subjects during bilateral tactile stimulation of fingers (stimulus rates of 4 Hz and 10 Hz in blocks of 0.5, 1, 2, and 4 s). The envelopes of the 17-23 Hz rhythmic activity, computed for sensors above the rolandic region, correlated 25%-43% better with the envelopes predicted by the models than by the stimulus time course (boxcar). A linear model with separate convolution kernels for onset and offset responses gave the best prediction. We studied the generalizability of this model with data from 5 different subjects during a separate bilateral tactile sequence by first identifying neural sources of the 17-23 Hz activity using cortically constrained minimum norm estimates. Both the model and the boxcar predicted strongest modulation in the primary motor cortex. For short-duration stimulus blocks, the model predicted the envelope of the cortical currents 20% better than the boxcar did. These results suggest that ORFs could concisely describe brain rhythms during different stimuli, tasks, and pathologies.

Predicting stimulus-rate sensitivity of human somatosensory fMRI signals with MEG

With increasing stimulus rate (SR), cortical EEG and MEG responses typically decrease in amplitude whereas BOLD fMRI signals increase. To address this discrepancy, we predicted BOLD responses with squared MEG waveforms using a recently proposed energy-density model. Tactile stimuli were delivered to finger tips at SRs of 1, 4, or 10 Hz in successive 25-s blocks, and brain signals were detected from area 3b of the primary somatosensory cortex of nine healthy adults using a 306-channel whole-scalp neuromagnetometer and a 3-T fMRI magnet. The main MEG deflections decreased in amplitude as a function of SR, whereas the BOLD signals increased from 1- to 4-Hz SR, with no further change at 10 Hz. MEG energy densities, obtained over the whole stimulus train and convolved with different hemodynamic response functions, predicted both the shape and amplitude of the BOLD signals well, and incorporation of nonlinear terms into the model did not offer any further advantage. Thus, squared MEG waveforms obtained over the entire stimulus train provided an appropriate estimate of area 3b neuronal activity associated with the BOLD signal.

A novel method for integrating MEG and BOLD fMRI signals with the linear convolution model in human primary somatosensory cortex

Characterizing the neurovascular coupling between hemodynamic signals and their neural origins is crucial to functional neuroimaging research, even more so as new methods become available for integrating results from different functional neuroimaging modalities. We present a novel method to relate magnetoencephalography (MEG) and BOLD fMRI data from primary somatosensory cortex within the context of the linear convolution model. This model, which relates neural activity to BOLD signal change, has been widely used to predict BOLD signals but typically lacks experimentally derived measurements of neural activity. In this study, an fMRI experiment is performed using variable-duration (< or =1 s) vibrotactile stimuli applied at 22 Hz, analogous to a previously published MEG study (Nangini et al., [2006]: Neuroimage 33:252-262), testing whether MEG source waveforms from the previous study can inform the convolution model and improve BOLD signal estimates across all stimulus durations. The typical formulation of the convolution model in which the input is given by the stimulus profile is referred to as Model 1. Model 2 is based on an energy argument relating metabolic demand to the postsynaptic currents largely responsible for the MEG current dipoles, and uses the energy density of the estimated MEG source waveforms as input to the convolution model. It is shown that Model 2 improves the BOLD signal estimates compared to Model 1 under the experimental conditions implemented, suggesting that MEG energy density can be a useful index of hemodynamic activity.