Uncovering the neural basis of resting functional MRI
Neurological and psychiatric diseases are increasingly thought of as network diseases, arising from the abnormal communication between multiple brain regions. As a result, the neuroimaging community is working to provide clinicians with a fast, paradigm-free, and universally accessible tool to aid in diagnosing and identifying the clinical and pre-clinical onset of a variety of diseases. Resting functional MRI represents a potentially powerful technique that meets these criteria. By examining the synchronized activity between different brain regions at rest, this technique can simultaneously localize the boundaries of functional and pathological networks. Indeed, resting fMRI has the potential to transform into a powerful complement to current clinical diagnostic tools. However, because the signals that are measured with this technique are extremely slow (10s of seconds) and indirect (measuring blood flow and oxygen consumption rather than neural activity), the neural basis of resting fMRI is not well understood. We have the opportunity to record from the brain's of epilepsy patients undergoing surgical evaluation to study the link between direct neural signals and resting fMRI networks.
First, we asked how electrically propagated potentials correspond to these spontaneous fluctuations of the BOLD signal. We demonstrated that the spatial distribution and magnitude of temporally correlated low-frequency BOLD fluctuations predict the pattern and magnitude of cortico-cortical evoked potentials elicited within 500ms after electrical stimulation. These findings were replicated across patients and functional subsystems. This work strengthened the notion that slow, spontaneous fluctuations are governed by similar architecture as fast electrically propagated activity.
Second, we investigated how resting fMRI relate to internally driven fluctuations of neuronal activity, specifically in regions exhibiting inversely correlated activity (‘anticorrelations’), thought to represent competitive interactions between functional systems. We demonstrate that positively and negatively correlated fluctuations of high gamma activity underlie positive and negative BOLD correlations, respectively. These results suggest that both resting BOLD interactions have neurophysiological origins in slow power modulations of fast frequency activity.
Neurophysiological Investigation of Spontaneous Correlated and Anticorrelated Fluctuations of the BOLD Signal
Analyses of intrinsic fMRI BOLD signal fluctuations reliably reveal correlated and anticorrelated functional networks in the brain. Because the BOLD signal is an indirect measure of neuronal activity and anticorrelations can be introduced by preprocessing steps, such as global signal regression, the neurophysiological significance of correlated and anticorrelated BOLD fluctuations is a source of debate. Here, we address this question by examining the correspondence between the spatial organization of correlated BOLD fluctuations and correlated fluctuations in electrophysiological high power signals recorded directly from the cortical surface of 5 patients. We demonstrate that both positive and negative BOLD correlations have neurophysiological correlates reflected in fluctuations of spontaneous neuronal activity. Although applying global signal regression to BOLD signals results in some BOLD anticorrelations that are not apparent in the ECoG data, it enhances the neuronal-hemodynamic correspondence overall. Together, these findings provide support for the neurophysiological fidelity of BOLD correlations and anticorrelations.
Intrinsic functional architecture predicts electrically evoked responses in the human brain
Adaptive brain function is characterized by dynamic interactions within and between neuronal circuits, often occurring at the time scale of milliseconds. These complex interactions between adjacent and noncontiguous brain areas depend on a functional architecture that is maintained even in the absence of input. Functional MRI studies carried out during rest (R-fMRI) suggest that this architecture is represented in low-frequency (<0.1 Hz) spontaneous fluc
tuations in the blood oxygen level-dependent signal that are correlated within spatially distributed networks of brain areas. These networks, collectively referred to as the brain’s intrinsic functional architecture, exhibit a remarkable correspondence with patterns of task-evoked coactivation as well as maps of anatomical connectivity. Despite this striking correspondence, there is no direct evidence that this intrinsic architecture forms the scaffold that gives rise to faster processes relevant to information processing and seizure spread. Here, we demonstrate that the spatial distribution and magnitude of temporally correlated low-frequency fluctuations observed with R-fMRI during rest predict the pattern and magnitude of corticocortical evoked potentials elicited within 500 ms after single-pulse electrical stimulation of the cerebral cortex with intracranial electrodes. Across individuals, this relationship was found to be independent of the specific regions and functional systems probed. Our findings bridge the immense divide between the temporal resolutions of these distinct measures of brain function and provide strong support for the idea that the low-frequency signal fluctuations observed with R-fMRI maintain and update the intrinsic architecture underlying the brain’s repertoire of functional responses.