Neuroimaging studies have provided a wealth of information on the cortical and subcortical regions of the human brain active during cognitive tasks. Recent studies have shown that regions that are co-activated during tasks maintain, even at rest, a high level of inter-regional correlation, or 'functional connectivity'. However, these interregional correlations are measured over long time scales (e.g. minutes). In contrast, little is known about the temporal dynamics and interactions of these brain regions over the short timescales (e.g. sec or ms) that are typical of most tasks. The analysis of temporal dynamics and interactions is strongly limited by the low temporal resolution of neuroimaging methods (functional magnetic resonance imaging, fMRI; Positron emission tomography, PET), and the low spatial resolution of methods for recording extracranial electro-magnetic activity (electroencephalography, EEG, magnetoencephalography, MEG). To solve these fundamental limitations, we have combined fMRI measures of blood-oxgyenation-level- dependent (BOLD) signals with electrocorticographic (ECoG) signals recorded from invasively monitored human subjects. We have developed methods to co-register functional networks localized with fMRI with intracranial electrodes that record surface cortical local field potentials (LFP). We have applied these novel methods to study the dynamics and interactions of cortical networks involved in spatial attention. Our preliminary results indicate that cortical networks observed with fMRI during a spatial attention task show multiple coherence modulations with ECoG. Maintenance of spatial attention correlates with sustained phase synchronization in the delta band (1-3 Hz) across multiple occipital, parietal, and frontal task-relevant regions, while shifts of spatial attention are associated with transient increases of phase synchronization in the theta band (3-7 Hz). We propose a series of experiments in which we first localize with fMRI cortical regions/networks specialized for spatial attention and then study their dynamics and interaction with ECoG on well- characterized cognitive tasks. Our first specific aim is to determine the role of delta/theta band phase synchronization in linking cortical regions during voluntary orienting of spatial attention. Our second specific aim studies how delta/theta band phase synchronization is affected by the temporal structure of a task. Our third specific aims focuses on the interaction between two different attention networks (dorsal, DAN; ventral, VAN) during stimulus-driven re-orienting.
There is growing evidence that the behavioral symptoms of neurological (stroke, TBI, AD, MS) and psychiatric (schizophrenia, depression) disorders reflect the disruption of brain networks. While functional magnetic resonance imaging (fMRI) can identify these networks and provide some information about whether they are damaged, fMRI unfortunately cannot track in detail how these networks function over short time periods (i.e. seconds). Yet it is these rapid timescales that are particularly salient for understanding behavior. Non-invasive methods such as EEG or MEG have traditionally been used to study human neural activity at fine time scales. Unfortunately, their usefulness is currently limited by the fact that these signals, unlike those measured in fMRI, cannot be unambiguously localized to specific parts of the brain. To fill this methodological gap, this project combines fMRI with directly measured cortical electrophysiology in human patients who require intracranial monitoring to identify the location of their seizures. By using functional imaging to precisely co-register regions of the brain with intracranial subdural electrode arrays (or electrocorticography, ECoG) the time course of neuronal activity of identified regions and networks of interest can be precisely determined. These novel methods are applied to understand the temporal dynamics and interactions of brain regions that are likely specialized in the control of spatial attention. The identification of neural mechanisms of attention in the normal brain and their temporal dynamics is a necessary first step for understanding the dysfunction of brain networks, a central feature of many neurological and psychiatric disorders. While the combination of fMRI and ECoG will remain a specialized application in human cognitive neuroscience, it also provides at the moment our best chance to establish some basic information about the relationship between fMRI signals and neuronal activity in human subjects during cognitive processing.
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| Corbetta, Maurizio; Siegel, Joshua S; Shulman, Gordon L (2018) On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury. Cortex 107:229-237 |
| Hacker, Carl D; Snyder, Abraham Z; Pahwa, Mrinal et al. (2017) Frequency-specific electrophysiologic correlates of resting state fMRI networks. Neuroimage 149:446-457 |
| Meehan, Timothy P; Bressler, Steven L; Tang, Wei et al. (2017) Top-down cortical interactions in visuospatial attention. Brain Struct Funct 222:3127-3145 |
| Capotosto, Paolo; Baldassarre, Antonello; Sestieri, Carlo et al. (2017) Task and Regions Specific Top-Down Modulation of Alpha Rhythms in Parietal Cortex. Cereb Cortex 27:4815-4822 |
| Carter, Alex R; McAvoy, Mark P; Siegel, Joshua S et al. (2017) Differential white matter involvement associated with distinct visuospatial deficits after right hemisphere stroke. Cortex 88:81-97 |
| Mitra, Anish; Snyder, Abraham Z; Hacker, Carl D et al. (2016) Human cortical-hippocampal dialogue in wake and slow-wave sleep. Proc Natl Acad Sci U S A 113:E6868-E6876 |
| de Pasquale, F; Della Penna, S; Sporns, O et al. (2016) A Dynamic Core Network and Global Efficiency in the Resting Human Brain. Cereb Cortex 26:4015-33 |
| Spadone, Sara; Della Penna, Stefania; Sestieri, Carlo et al. (2015) Dynamic reorganization of human resting-state networks during visuospatial attention. Proc Natl Acad Sci U S A 112:8112-7 |
| Guidotti, Roberto; Del Gratta, Cosimo; Baldassarre, Antonello et al. (2015) Visual Learning Induces Changes in Resting-State fMRI Multivariate Pattern of Information. J Neurosci 35:9786-98 |
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