Cargando…

Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing

The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still d...

Descripción completa

Detalles Bibliográficos
Autores principales: Ciullo, Valentina, Vecchio, Daniela, Gili, Tommaso, Spalletta, Gianfranco, Piras, Federica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978278/
https://www.ncbi.nlm.nih.gov/pubmed/29881338
http://dx.doi.org/10.3389/fnhum.2018.00212
_version_ 1783327509023031296
author Ciullo, Valentina
Vecchio, Daniela
Gili, Tommaso
Spalletta, Gianfranco
Piras, Federica
author_facet Ciullo, Valentina
Vecchio, Daniela
Gili, Tommaso
Spalletta, Gianfranco
Piras, Federica
author_sort Ciullo, Valentina
collection PubMed
description The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures indexing the process of attentional re-orienting. These results represent a crucial step forward the ordinary predictive processing description, as we identified the patterns of connectivity characterizing the brain organization associated with the ability to generate and update temporal expectancies in case of contextual violations.
format Online
Article
Text
id pubmed-5978278
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-59782782018-06-07 Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing Ciullo, Valentina Vecchio, Daniela Gili, Tommaso Spalletta, Gianfranco Piras, Federica Front Hum Neurosci Neuroscience The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures indexing the process of attentional re-orienting. These results represent a crucial step forward the ordinary predictive processing description, as we identified the patterns of connectivity characterizing the brain organization associated with the ability to generate and update temporal expectancies in case of contextual violations. Frontiers Media S.A. 2018-05-24 /pmc/articles/PMC5978278/ /pubmed/29881338 http://dx.doi.org/10.3389/fnhum.2018.00212 Text en Copyright © 2018 Ciullo, Vecchio, Gili, Spalletta and Piras. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Ciullo, Valentina
Vecchio, Daniela
Gili, Tommaso
Spalletta, Gianfranco
Piras, Federica
Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing
title Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing
title_full Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing
title_fullStr Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing
title_full_unstemmed Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing
title_short Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing
title_sort segregation of brain structural networks supports spatio-temporal predictive processing
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978278/
https://www.ncbi.nlm.nih.gov/pubmed/29881338
http://dx.doi.org/10.3389/fnhum.2018.00212
work_keys_str_mv AT ciullovalentina segregationofbrainstructuralnetworkssupportsspatiotemporalpredictiveprocessing
AT vecchiodaniela segregationofbrainstructuralnetworkssupportsspatiotemporalpredictiveprocessing
AT gilitommaso segregationofbrainstructuralnetworkssupportsspatiotemporalpredictiveprocessing
AT spallettagianfranco segregationofbrainstructuralnetworkssupportsspatiotemporalpredictiveprocessing
AT pirasfederica segregationofbrainstructuralnetworkssupportsspatiotemporalpredictiveprocessing