Mapping of Language-and-Memory Networks in Patients With Temporal Lobe Epilepsy by Using the GE2REC Protocol

Abstract

Preoperative mapping of language and declarative memory functions in temporal lobe epilepsy (TLE) patients is essential since they frequently encounter deterioration of these functions and show variable degrees of cerebral reorganization. Due to growing evidence on language and declarative memory interdependence at a neural and neuropsychological level, we propose the GE2REC protocol for interactive language-and-memory network (LMN) mapping. GE2REC consists of three inter-related tasks, sentence generation with implicit encoding (GE) and two recollection (2REC) memory tasks: recognition and recall. This protocol has previously been validated in healthy participants, and in this study, we showed that it also maps the LMN in the left TLE (N = 18). Compared to healthy controls (N = 19), left TLE (LTLE) showed widespread inter- and intra-hemispheric reorganization of the LMN through reduced activity of regions engaged in the integration and the coordination of this meta-network. We also illustrated how this protocol could be implemented in clinical practice individually by presenting two case studies of LTLE patients who underwent efficient surgery and became seizure-free but showed different cognitive outcomes. This protocol can be advantageous for clinical practice because it (a) is short and easy to perform; (b) allows brain mapping of essential cognitive functions, even at an individual level; (c) engages language-and-memory interaction allowing to evaluate the integrative processes within the LMN; (d) provides a more comprehensive assessment by including both verbal and visual modalities, as well as various language and memory processes. Based on the available postsurgical data, we presented preliminary results obtained with this protocol in LTLE patients that could potentially inform the clinical practice. This implies the necessity to further validate the potential of GE2REC for neurosurgical planning, along with two directions, guiding resection and describing LMN neuroplasticity at an individual level.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy

“…In the picture retrieval task, the hyperconnectivity, especially in right TLE patients, originated from the LANG in the prefrontal cortex and extended from this region to the other areas. While other investigators have also reported the intervention of prefrontal areas in the memory network, 15,16,36,37 our results showed that this effect is initiated from the left hemispheric areas to the other intracranial regions. Also, our finding suggests an ipsilateral hypoconnectivity in PHIP.…”

“…Effective connectivity can be measured by fMRI and dynamic causal modeling (DCM) as one of the most common methods for calculating and modeling the effective neural communication between brain regions and networks. 12,13 Several studies have examined the effect of TLE on the performance of memory and language networks using fMRI [14][15][16] including encoding and retrieval of abstract images, visual scenes, faces, words, object location, and spatial navigation tasks. 14,15,[17][18][19] Several investigations have also used the DCM method to map the memory or language networks.…”

“…14,15,[17][18][19] Several investigations have also used the DCM method to map the memory or language networks. 15,16,20 Despite the promising results of these studies, fMRI activation analysis that examined the whole brain does not provide information about the connectivity of these regions. Based on previous research, the primary structures involved in episodic memory include the hippocampus, parahippocampus, entorhinal cortex, and perirhinal cortex, all of which are structures found in the temporal lobe of the cerebrum.…”

“…Several studies have examined the effect of TLE on the performance of memory and language networks using fMRI 14–16 including encoding and retrieval of abstract images, visual scenes, faces, words, object location, and spatial navigation tasks 14,15,17–19 . Several investigations have also used the DCM method to map the memory or language networks 15,16,20 …”