@article{MTMT:2520541,
	title = {Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal.},
	url = {https://m2.mtmt.hu/api/publication/2520541},
	author = {Keller, CJ and Bickel, S and Honey, CJ and Groppe, DM and Entz, László and Craddock, RC and Lado, FA and Kelly, C and Milham, M and Mehta, AD},
	doi = {10.1523/JNEUROSCI.4837-12.2013},
	journal-iso = {J NEUROSCI},
	journal = {JOURNAL OF NEUROSCIENCE},
	volume = {33},
	unique-id = {2520541},
	issn = {0270-6474},
	abstract = {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 gamma 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.},
	keywords = {Adult; Female; Male; Humans; Electrodes, Implanted; ROC Curve; Neural Pathways/physiology; Neurons/physiology; Image Interpretation, Computer-Assisted/methods; Cerebral Cortex/blood supply/*physiology; Electroencephalography/methods/statistics & numerical data; Neurophysiology/*methods; Magnetic Resonance Imaging/methods/statistics & numerical data; Brain Waves/physiology; Brain Mapping/methods/*statistics & numerical data},
	year = {2013},
	eissn = {1529-2401},
	pages = {6333-6342}
}

@article{MTMT:1606036,
	title = {Intrinsic functional architecture predicts electrically evoked responses in the human brain.},
	url = {https://m2.mtmt.hu/api/publication/1606036},
	author = {Keller, CJ and Bickel, S and Entz, László and Ulbert, István and Milham, MP and Kelly, C and Mehta, AD},
	doi = {10.1073/pnas.1019750108},
	journal-iso = {P NATL ACAD SCI USA},
	journal = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA},
	volume = {108},
	unique-id = {1606036},
	issn = {0027-8424},
	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.},
	year = {2011},
	eissn = {1091-6490},
	pages = {10308-10313},
	orcid-numbers = {Ulbert, István/0000-0001-9941-9159}
}