Towards linking diffusion MRI based macro- and microstructure measures with cortico-cortical transmission in brain tumor patients.
Adult
Aged
Brain Neoplasms
/ surgery
Cerebral Cortex
/ diagnostic imaging
Diffusion Magnetic Resonance Imaging
Diffusion Tensor Imaging
Electric Stimulation
Electrocorticography
Evoked Potentials
Female
Glioma
/ surgery
Hemangioma, Cavernous, Central Nervous System
/ surgery
Humans
Male
Middle Aged
Neural Pathways
/ diagnostic imaging
Neurosurgical Procedures
Pilot Projects
Wakefulness
White Matter
/ diagnostic imaging
Young Adult
Brain white matter microstructure
Cortico-cortical evoked potentials
Direct electrical stimulation
Effective connectivity
Structural connectivity
Tractography
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 02 2021
01 02 2021
Historique:
received:
20
08
2020
revised:
29
09
2020
accepted:
16
11
2020
pubmed:
23
11
2020
medline:
10
3
2021
entrez:
22
11
2020
Statut:
ppublish
Résumé
We aimed to link macro- and microstructure measures of brain white matter obtained from diffusion MRI with effective connectivity measures based on a propagation of cortico-cortical evoked potentials induced with intrasurgical direct electrical stimulation. For this, we compared streamline lengths and log-transformed ratios of streamlines computed from presurgical diffusion-weighted images, and the delays and amplitudes of N1 peaks recorded intrasurgically with electrocorticography electrodes in a pilot study of 9 brain tumor patients. Our results showed positive correlation between these two modalities in the vicinity of the stimulation sites (Pearson coefficient 0.54±0.13 for N1 delays, and 0.47±0.23 for N1 amplitudes), which could correspond to the neural propagation via U-fibers. In addition, we reached high sensitivities (0.78±0.07) and very high specificities (0.93±0.03) in a binary variant of our comparison. Finally, we used the structural connectivity measures to predict the effective connectivity using a multiple linear regression model, and showed a significant role of brain microstructure-related indices in this relation.
Identifiants
pubmed: 33221443
pii: S1053-8119(20)31052-1
doi: 10.1016/j.neuroimage.2020.117567
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
117567Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.