TY - JOUR
T1 - Multimodal neurocognitive markers of frontal lobe epilepsy
T2 - Insights from ecological text processing
AU - Moguilner, Sebastian
AU - Birba, Agustina
AU - Fino, Daniel
AU - Isoardi, Roberto
AU - Huetagoyena, Celeste
AU - Otoya, Raúl
AU - Tirapu, Viviana
AU - Cremaschi, Fabián
AU - Sedeño, Lucas
AU - Ibáñez, Agustín
AU - García, Adolfo M.
N1 - Publisher Copyright:
© 2021
PY - 2021/7/15
Y1 - 2021/7/15
N2 - The pressing call to detect sensitive cognitive markers of frontal lobe epilepsy (FLE) remains poorly addressed. Standard frameworks prove nosologically unspecific (as they reveal deficits that also emerge across other epilepsy subtypes), possess low ecological validity, and are rarely supported by multimodal neuroimaging assessments. To bridge these gaps, we examined naturalistic action and non-action text comprehension, combined with structural and functional connectivity measures, in 19 FLE patients, 19 healthy controls, and 20 posterior cortex epilepsy (PCE) patients. Our analyses integrated inferential statistics and data-driven machine-learning classifiers. FLE patients were selectively and specifically impaired in action comprehension, irrespective of their neuropsychological profile. These deficits selectively and specifically correlated with (a) reduced integrity of the anterior thalamic radiation, a subcortical structure underlying motoric and action-language processing as well as epileptic seizure spread in this subtype; and (b) hypoconnectivity between the primary motor cortex and the left-parietal/supramarginal regions, two putative substrates of action-language comprehension. Moreover, machine-learning classifiers based on the above neurocognitive measures yielded 75% accuracy rates in discriminating individual FLE patients from both controls and PCE patients. Briefly, action-text assessments, combined with structural and functional connectivity measures, seem to capture ecological cognitive deficits that are specific to FLE, opening new avenues for discriminatory characterizations among epilepsy types.
AB - The pressing call to detect sensitive cognitive markers of frontal lobe epilepsy (FLE) remains poorly addressed. Standard frameworks prove nosologically unspecific (as they reveal deficits that also emerge across other epilepsy subtypes), possess low ecological validity, and are rarely supported by multimodal neuroimaging assessments. To bridge these gaps, we examined naturalistic action and non-action text comprehension, combined with structural and functional connectivity measures, in 19 FLE patients, 19 healthy controls, and 20 posterior cortex epilepsy (PCE) patients. Our analyses integrated inferential statistics and data-driven machine-learning classifiers. FLE patients were selectively and specifically impaired in action comprehension, irrespective of their neuropsychological profile. These deficits selectively and specifically correlated with (a) reduced integrity of the anterior thalamic radiation, a subcortical structure underlying motoric and action-language processing as well as epileptic seizure spread in this subtype; and (b) hypoconnectivity between the primary motor cortex and the left-parietal/supramarginal regions, two putative substrates of action-language comprehension. Moreover, machine-learning classifiers based on the above neurocognitive measures yielded 75% accuracy rates in discriminating individual FLE patients from both controls and PCE patients. Briefly, action-text assessments, combined with structural and functional connectivity measures, seem to capture ecological cognitive deficits that are specific to FLE, opening new avenues for discriminatory characterizations among epilepsy types.
KW - Cognitive markers
KW - Frontal lobe epilepsy
KW - Multimodal neuroimaging, machine learning
KW - Naturalistic discourse
UR - http://www.scopus.com/inward/record.url?scp=85103762261&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2021.117998
DO - 10.1016/j.neuroimage.2021.117998
M3 - Article
C2 - 33789131
AN - SCOPUS:85103762261
SN - 1053-8119
VL - 235
JO - NeuroImage
JF - NeuroImage
M1 - 117998
ER -