bioRxivNeurosciencePreprint
Default Mode Network Ventral Hub Connectivity Associated with Memory Impairment in Temporal Lobe Epilepsy Surgery
Elliot G. NealiD, Long Di, You Jeong ParkiD, Austin Finch, Ferdinand KorneliiD, Stephanie MaciveriD, Yarema B. BezchlibnykiD, Mike R. SchoenbergiD, Fernando Ĺ. ValeiD
Abstract
In patients undergoing surgery for intractable temporal lobe epilepsy, the relationship between the default mode network and patients neurocognitive outcome remains unclear. The objective of this study is to employ non-invasive network mapping to identify the relationship between subdivisions of the default mode network and neurocognitive function before and after epilepsy surgery in patients with temporal lobe epilepsy. Twenty-seven medically patients with medically refractory temporal lobe epilepsy were prospectively enrolled and received resting state functional MRI and neuropsychological testing both pre- and post-operatively. Connectivity within the default mode network was modeled and average connectivity within the networks was calculated. Higher pre-operative connectivity in the ventral default mode network hub correlated with impaired baseline performance in a visual memory task. Post-operatively, a decrease in ventral but not dorsal default mode network connectivity was correlated with a deterioration of verbal and logical memory after surgery. Overall, higher connectivity in the ventral default mode network hub was associated with poor memory function in patients with temporal lobe epilepsy both before and after temporal lobe surgery. Pre-operatively, higher ventral connectivity was associated with worse visual function. Post-operatively, decreased connectivity of the ventral and dorsal default mode network was correlated with a greater decrease in logical and verbal memory when compared with the pre-operation baseline. An imbalance in default mode network connectivity towards the ventral stream and more widespread epilepsy networks may be used to predict memory impairments following surgical intervention and may lead to more tailored surgical decision making based on this non-invasive network modeling.
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