Disrupted activity flow during declarative memory states in pharmaco-resistant epilepsy
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
P2 (Plaza Level)
Poster No:
836
Submission Type:
Abstract Submission
Authors:
Donna Gift Cabalo1, Jordan DeKraker1, Ke Xie1, Jessica Royer1, Raúl Rodríguez-Cruces1, Alexander Ngo1, Ella Sahlas1, Thaera Arafat2, Raluca Pana2, Elizabeth Jefferies3, Alexander Barnett2, Neda Bernasconi2, Andrea Bernasconi2, Jonathan Smallwood4, Boris Bernhardt1
Institutions:
1McGill University, Montreal, Quebec, 2Montreal Neurological Institute, Montreal, Quebec, 3University of York, York, York, 4Queens University, Kingston, Ontario
First Author:
Co-Author(s):
Introduction:
Temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) are the two most common pharmaco-resistant epilepsies, both associated with significant yet distinct declarative memory deficits (Elger, 2002; Helmstaedter, 2002). TLE patients exhibit marked episodic and mild semantic memory difficulties (Barrett-Jones, 2022; Helmstaedter, 2002), while FLE patients may present with mild declarative memory impairment together with difficulties in semantic and overall language-related processes (Caciagli, 2023; Della Rocchetta, 1993). Functional imaging indicates differential patterns of activation and connectivity in the medial temporal and fronto-limbic networks in TLE and FLE, respectively. Yet, activation and connectivity differences have rarely been studied in the same analysis and have not been systematically assessed across these syndromes. Here, we used activity flow mapping (AFM;Cole, 2016) to explore the synergy between intrinsic connectivity and functional activation patterns during declarative states. We also assessed how local disruptions due to lesions relate to cognitive alterations.
Methods:
We studied 34 TLE patients (left/right=20/14, age=36土12years; F/M: 17/17), 20 FLE patients (left/right=10/10, 31土11years; F/M:11/9) with variable degrees of mesiotemporal and frontal pathology, respectively; and 87 healthy controls (HC; age=32土9 years, F/M: 40/47). Participants underwent fMRI during wakeful rest, and while performing episodic and semantic retrieval tasks (Figure 1A). Task-fMRI activation amplitudes were estimated using a general linear model, while rest-FC was estimated using regularized graphical lasso for more reliable estimates of unconfounded connectivity (Cole,2016; Peterson, 2023). Task activations of each brain regions were first predicted in a HC training subgroup, HC1 (n=45; Figure 1B). Activity flow predictions were benchmarked as the overlap between predicted and actual activations for both tasks (r=0.80, p<0.0001). We repeated this analysis in a second subgroup of healthy controls (HC2, n=42), and in the TLE and FLE patients. Notably, predictive models only had access to data from HC1 when predicting task activity in HC2, TLE, and FLE (Mill, 2020).
Results:
In all three groups, both tasks activated medio-lateral temporal and posterior-parietal regions, extending to larger fronto-parietal and default mode networks (Irish, 2020), with broader activation in patients. Activity flow predictions were significant for both tasks in all groups, albeit, lower in patients (Figure 2,left panel). When assessing prediction errors (Cole, 2016), higher errors were observed in unimodal compared to transmodal regions. This suggest that transmodal regions are involved in more distributed processing, whereas unimodal regions are characterized by more localize information flow (Ito, 2020). Additionally, hippocampal volume and longer seizure duration positively and negatively correlated with prediction accuracy in episodic, but not in semantic task, respectively (Figure 2,middle). Finally, higher prediction accuracies correlated with better behavioural performance,(Figure 2, right).
Conclusions:
AFM accurately predicts task-evoked activation during both episodic and semantic tasks in patients. Transmodal regions, involved in distributed processing, showed higher prediction accuracy than unimodal regions, suggesting their crucial role for the intregration of cognitive processes across different memory domains. Hippocampal volume was linked to prediction accuracy in the episodic task, highlighting its role in episodic processes, while longer seizure duration negatively impacted accuracy, suggesting that prolonged epileptic activity may worsen cognitive deficits through network disruptions. The correlation between prediction accuracy and behavioral performance suggest that the activity flow framework could be a valuable tool for assessing cognitive function in epilepsy patients, particularly for patients unable to complete traditional memory tasks.
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
Epilepsy
FUNCTIONAL MRI
Memory
MRI
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Caciagli, L. (2023). Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy. Brain, 146(3), 935-953.
Cole, M. W. (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature neuroscience, 19(12), 1718-1726.
Della Rocchetta, A. I., & Milner, B. (1993). Strategic search and retrieval inhibition: The role of the frontal lobes. Neuropsychologia, 31(6), 503-524.
Elger, C. E. (2002). Epilepsy: disease and model to study human brain function. Brain pathology, 12(2), 193.
Helmstaedter, C. (2002). Effects of chronic epilepsy on declarative memory systems. Progress in brain research, 135, 439-453.
Irish, M., & Vatansever, D. (2020). Rethinking the episodic-semantic distinction from a gradient perspective. Current Opinion in Behavioral Sciences, 32, 43-49.
Ito, T. (2020). A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales. Neuroimage, 221, 117141.
Mill, R. D. (2020). Predicting dysfunctional age-related task activations from resting-state network alterations. Neuroimage, 221, 117167.
Peterson, K. L. (2023). Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding. bioRxiv, 2023.2009. 2016.558065.