A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Lemaréchal, Jean-Didier; Jedynak, Maciej; Trébaul, L; Boyer, Anthony; Tadel, François; Bhattacharjee, Manik; Deman, Pierre; Tuyisenge, Viateur; Ayoubian, Leila; Hugues, Etienne; Chanteloup-Forêt, Blandine; Saubat, Carole; Zouglech, Raouf; Mejia, Gina Catalina Reyes; Tourbier, Sébastien; Hagmann, Patric; Adam, Claude; Barba, Carmen; Bartolomei, Fabricē; Blauwblomme, Thomas; Curot, Jonathan; Dubeau, François; Garcés, Mercedes; Hirsch, Édouard; Landré, Elizabeth; Liu, Sinclair; Maillard, Louis; Metsähonkala, Eeva-Liisa; Mı̂ndruță, Ioana; Nica, Anca; Pail, Martin; Petrescu, Ana Maria Roxana; Rheims, Sylvain; Rocamora, Rodrigo; Schulze‐Bonhage, Andreas; Szurhaj, William; Taussig, Delphine; Wang, Haixiang; Kahane, Philippe; George, Nathalie; David, Olivier; Navarro, Vincent; Biraben, Arnaud; Menard, D.; Brázdil, Milan; Kuba, Robert; Kočvarová, Jitka; Doležalová, Irena; Gotman, Jean; Ryvlin, Philippe; Isnard, Jean; Catenoix, Hélène; Montavont, Alexandra; Trébuchon, Agnès; McGonigal, Aileen; Zhou, Wenjing; Wang, Haixiang; Zhang, Wei; Zhu, Da‐Yuan; Qiang, Guo; Xiangshu, Hu; Li, Hua; Hua, Gang; Wang, Wensheng; Xi, Mian; Feng, Yigang; Nabbout, Rima; Bourgeois, Marie; Kamińska, Anna; Valentı́n, Antonio; Singh, Rinki; Metsähonkala, Liisa; Gaily, Eija; Lauronen, Leena; Peltola, Maria; Chassoux, Francine; Derambure, Philippe; Chochois, Maxime; Valenti, Maria Paola; Scholly, Julia; Valton, Luc; Denuelle, Marie; Príncipe, Alessandro; Ley, Miguel; Barborică, Andrei; Francione, Stefano; Mai, Roberto; Nobili, Lino; Sartori, Ivana; Tassi, Laura; Vignal, Jean–Pierre; Jonas, Jacques; Tyvaert, Louise; Chipaux, Mathilde; Minotti, Lorella; Job, Anne‐Sophie; Michel, Véronique; Montaudoin, Marie de; Aupy, Jérôme; Bouilleret, Viviane; Masnou, P.; Dussaule, Claire; Quirins, M.; Guerrini, Renzo; Lenge, Matteo; Nacci, Elisa

doi:10.1093/brain/awab362

Cited by 40 publications

(40 citation statements)

References 61 publications

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“…This framework provides evidence that the IHTT estimates are the result of activity elicited by the visual stimulus. The estimated IHTT values are in the reported range of electrode-based and intracranial EEG studies (Saron and Davidson, 1989;Friedrich et al, 2017;Chaumillon et al, 2018;Lemarechal et al, 2022). The MRI data and EEG-based measures of the IHTT were used to estimate morphological properties of white matter axons.…”

Section: Discussionmentioning

confidence: 99%

Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology

Oliveira

Lucia

Lutti

2022

Preprint

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Assessing axonal morphology in-vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate the morphology of axonal fibers from the combination of MRI data and EEG measures of the interhemispheric transfer time (IHTT). In the original study, the IHTT measures were computed from EEG data averaged across a group, leading to bias of the axonal morphology estimates. Here, we seek to estimate axonal morphology from individual measures of IHTT, obtained from EEG data acquired in a visual evoked potential experiment. Subject-specific IHTTs are computed in a data-driven framework with minimal a priori constraints, based on the maximal peak of neural responses to visual stimuli within periods of statistically significant evoked activity in the inverse solution space. The subject-specific IHTT estimates ranged from 8 to 29 ms except for one participant and the between-session variability was comparable to the differences in IHTT between subjects. The scale parameter of the axonal radius distribution, computed from the IHTT estimates and the MRI data, ranged from 0 to 0.79 μm. The change in axonal g-ratio with axonal radius ranged from 0.62 to 0.81 μm−α. The single-subject measurement of the IHTT yields estimates of axonal morphology that are consistent with histological values. However, improvement of the repeatability of the IHTT estimates is required to improve the specificity of the single-subject axonal morphology estimates.

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Section: Discussionmentioning

confidence: 99%

Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology

Oliveira

Lucia

Lutti

2022

Preprint

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“…First, SEEG signals were preprocessed to identify stimulation events, filter bad channels [72], and correct for stimulation artifacts [73]. Recordings with high likelihood of reflecting pathological activity were identified and discarded using Delphos software to mitigate the impact of epileptogenic processes in further analyses [39, 74]. CCEPs on each electrode contact were baseline corrected and z -scored based on the [−400, −10] ms time window before stimulation.…”

Section: Methodsmentioning

confidence: 99%

“…Note that P and A are asymmetric, such that P ij may differ to P ji . Data from the F-TRACT project is openly available at https://f-tract.eu/ and further details on data acquisition and processing are provided in [37][38][39].…”

Section: F-tract Datasetmentioning

confidence: 99%

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

Seguin

Jedynak

David

et al. 2022

Preprint

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Communication between gray matter regions underpins all facets of brain function. To date, progress in understanding large-scale neural communication has been hampered by the inability of current neuroimaging techniques to track signaling at whole-brain, high-spatiotemporal resolution. Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations in a total of 550 individuals, to study inter-areal communication in the human brain. We found that network communication models---computed on structural connectivity inferred from diffusion MRI---can explain the propagation of direct, focal electrical stimulation through white matter, measured at millisecond time scales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (out-of-sample R2=54%). Our work contributes towards the biological validation of concepts in network neuroscience and provides insight into how white matter connectivity shapes inter-areal signaling. We anticipate that our findings will have implications for research on macroscale neural information processing and the design of brain stimulation paradigms.

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“…Some EEG studies have detected small changes during adolescence (7,8). After adolescence there may be little further maturation, as also indicated by one study of cortico-cortical evoked potentials (CCEPs), which reported a decrease in conduction delay of only one millisecond from before to after the age of 15 (9). Anatomical studies, however, indicate that the human connectome may follow a much longer developmental trajectory: postmortem studies have shown that myelination starts in the late prenatal period and continues into late adolescence (10).…”

Section: Introductionmentioning

confidence: 99%

The development of efficient communication in the human connectome

Blooijs

Aar

Huiskamp

et al. 2022

Preprint

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The structure of the human connectome develops from childhood throughout adolescence to middle age, but how this affects the speed of neuronal signaling is not well described. To understand the maturation process of transmission speed across the connectome, we stimulated electrocorticographic (ECoG) electrode pairs in 75 human subjects of 4 to 51 years old. We then measured the latency of cortico-cortical evoked responses in other electrodes. Each year, responses got faster, until transmission speed reached a minimum latency at the age of 29-44 years old. Responses during middle age were about two times faster compared to childhood. These increases in speed were observed in both long- and short-range connections between and within frontal, central, parietal, and temporal areas. These findings indicate that long-range and short-range communication between brain regions increase in efficiency well throughout adolescence.

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A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Cited by 40 publications

References 61 publications

Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology

Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

The development of efficient communication in the human connectome

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