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Laiou, P.

Publications and source records attributed to Laiou, P..

2 recordsLinked to original sources

Zero-phase-delay connectivity increases the reliability, concordance with structure, and prognostic ability of functional connectivity metrics

Neural populations synchronise their activity with either zero-phase-delay (activity in interacting regions occurs simultaneously) or a phase-delay (activity in one region follows the other). In electroencephalography and magnetoencephalography functional connectivity analyses, artefactual connectivity can also occur with zero-phase-delay. To minimise artefact, contemporary analyses typically exclude all zero-phase-delay interactions. However, the extent to which true interactions are resultingly lost - and the impact this has on the performance of functional connectivity metrics as biomarkers - remains unknown. Here, we show that most cortico-cortical functional connectivity occurs with zero- or near-zero phase-delay, even where such connectivity is unlikely to be artefactual. Including, rather than excluding, zero-phase-delay connectivity increases the reliability, convergence with neurobiology (structure-function concordance, homotopic interhemispheric connectivity, and age-related connectivity changes), and prognostic ability of functional connectivity metrics. We find that excluding zero-phase-delay connections penalises functional connectivity strength between the strongest structurally connected regions: stronger structural connections lead to functional connections with phase-delays closer to zero, mediated by a shorter signal propagation time. Our findings challenge generally accepted assumptions that zero-phase-exclusive methods are superior to zero-phase-inclusive methods.

neuroscience↗

Quantification and selection of ictogenic zones in epilepsy surgery

Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy.\n\nHere, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we develop a global optimisation approach to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy.

neuroscience↗