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Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks.

ABSTRACT: Frequency-specific oscillations and phase-coupling of neuronal populations are essential mechanisms for the coordination of activity between brain areas during cognitive tasks. Therefore, the ongoing activity ascribed to the different functional brain networks should also be able to reorganise and coordinate via similar mechanisms. We develop a novel method for identifying large-scale phase-coupled network dynamics and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct patterns of oscillatory power and coherence in specific frequency bands. Brain states are identified for sensory, motor networks and higher-order cognitive networks. The cognitive networks include a posterior alpha (8-12?Hz) and an anterior delta/theta range (1-7?Hz) network, both exhibiting high power and coherence in areas that correspond to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks have characteristic signatures in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

SUBMITTER: Vidaurre D

PROVIDER: S-EPMC6065434 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks.

Vidaurre Diego D Hunt Laurence T LT Quinn Andrew J AJ Hunt Benjamin A E BAE Brookes Matthew J MJ Nobre Anna C AC Woolrich Mark W MW

Nature communications 20180730 1

Frequency-specific oscillations and phase-coupling of neuronal populations are essential mechanisms for the coordination of activity between brain areas during cognitive tasks. Therefore, the ongoing activity ascribed to the different functional brain networks should also be able to reorganise and coordinate via similar mechanisms. We develop a novel method for identifying large-scale phase-coupled network dynamics and show that resting networks in magnetoencephalography are well characterised b ...[more]

PMID: 30061566

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Project description:OBJECTIVE:We hypothesized that the modulation index (MI), a summary measure of the strength of phase-amplitude coupling between high-frequency activity (>150 Hz) and the phase of slow waves (3-4 Hz), would serve as a useful interictal biomarker for epilepsy presurgical evaluation. METHODS:We investigated 123 patients who underwent focal cortical resection following extraoperative electrocorticography recording and had at least 1 year of postoperative follow-up. We examined whether consideration of MI would improve the prediction of postoperative seizure outcome. MI was measured at each intracranial electrode site during interictal slow-wave sleep. We compared the accuracy of prediction of patients achieving International League Against Epilepsy class 1 outcome between the full multivariate logistic regression model incorporating MI in addition to conventional clinical, seizure onset zone (SOZ), and neuroimaging variables, and the reduced logistic regression model incorporating all variables other than MI. RESULTS:Ninety patients had class 1 outcome at the time of most recent follow-up (mean follow-up = 5.7 years). The full model had a noteworthy outcome predictive ability, as reflected by regression model fit R2 of 0.409 and area under the curve (AUC) of receiver operating characteristic plot of 0.838. Incomplete resection of SOZ (P < 0.001), larger number of antiepileptic drugs at the time of surgery (P = 0.007), and larger MI in nonresected tissues relative to that in resected tissue (P = 0.020) were independently associated with a reduced probability of class 1 outcome. The reduced model had a lower predictive ability as reflected by R2 of 0.266 and AUC of 0.767. Anatomical variability in MI existed among nonepileptic electrode sites, defined as those unaffected by magnetic resonance imaging lesion, SOZ, or interictal spike discharges. With MI adjusted for anatomical variability, the full model yielded the outcome predictive ability of R2 of 0.422, AUC of 0.844, and sensitivity/specificity of 0.86/0.76. SIGNIFICANCE:MI during interictal recording may provide useful information for the prediction of postoperative seizure outcome.

Dataset Information

Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks.

Publications

Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks.

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