Intrinsic functional architecture predicts electrically evoked responses in the human brain

Abstract

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 fluctuations 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.

Mapping human brain networks with cortico-cortical evoked potentials

“…Brain regions with temporally correlated BOLD fluctuations also exhibited larger CCEP amplitude during the N1 and N2 time periods (figure 2d). These findings, which were replicated across patients and functional networks, suggest that temporal correlations of slow, spontaneous haemodynamics reflect similar functional interactions to those arising from fast electrically propagated activity [52].…”

“…This procedure is repeated 10-50 times for signal averaging of the evoked response. CCEPs typically consist of an early (10-30 ms) negative surface deflection termed the N1 and a later (80-250 ms) slow wave termed the N2 (figure 2a) [50,52,[61][62][63]. Considerable waveform heterogeneity of the N1 and N2 components of the CCEP exists across spatially diverse recording sites following stimulation (figure 2b).…”

“…provide a neuronal basis of neuroimaging signals that are used widely to study brain connectivity and function. For example, the N1 of the CCEP partially reflects both structural [61] and functional [52] connections between cortical regions, whereas the N2 partially reflects functional connections [52]. The N1 potential occurs between 10 and 30 ms and is thought to reflect excitation of local cortex ( [62]; see Information in Box 1).…”

Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases

“…This sensitivity allows the identification of distant and complex network interactions that match well with data suggesting that the effects of brain stimulation are also polysynaptic (4,10,(26)(27)(28)(29)(30)(31)(32)(33). From the practical standpoint, prior work has used rs-fcMRI to predict the propagation of brain stimulation (93)(94)(95), link sites of invasive and noninvasive stimulation in depression (43), and identify biomarkers of the response to therapeutic brain stimulation (36). Moving forward, rs-fcMRI has potential as a clinical tool (42) and is robust enough to identify reproducible, individualized stimulation targets (44).…”

“…First, although both rs-fcMRI and brain stimulation are polysynaptic, they do not necessarily reflect the same polysynaptic phenomena, and discrepancies exist (93)(94)(95). More advanced rs-fcMRI processing techniques designed to predict the influence of one region on another may prove better for identifying stimulation-based brain networks (96)(97)(98).…”