Authors :
Presenting Author: Sam Javidi, PhD – Farber Institute for Neuroscience, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
Qirui Zhang, MD – Farber Institute for Neuroscience, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
Ankeeta Ankeeta, PhD – Farber Institute for Neuroscience, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
Michael Sperling, MD – Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Joseph Tracy, PhD – Farber Institute for Neuroscience, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
Rationale: Prior control theory studies in temporal lobe epilepsy (TLE) have investigated the networks that support high or low energy state transitions in the human brain using predefined topologies (i.e., the well-known canonical intrinsic brain networks[1].
In this study, we sought to determine the topology of high and low energy state transitions (also referred to as hard and easy, respectively) unique to focal TLE without placing constraints on or pre-selecting any regional network topology. Our aim was to identify the regional networks unique to TLE that are highly disposed to drive these distinct and extreme state transitions under the assumption that: (1) the energy costs of state transitions are related to the underlying integrity of the regions and pathways involved, (2) the easy and hard state transitions unique to TLE provide clues to two different, but equally important epileptiform processes and connectivities in the brain.
Methods: Participants were 25 ATL-treated focal TLE patients (right=9; left=16) and age/sex matched healthy participants (HP, n=25). All participants underwent multi-shell diffusion-weighted MRI (NODDI) imaging (in 64 directions; b-values: 1000, 2000 and 3000 < ![if !msEquation] >< ![if !vml] >
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< ![endif] >< ![endif] >). The Brainnetome atlas was used to parcellate the brain (256 regions). The right TLE connectivity matrices were flipped. Genetic algorithm was used to find the minimum energy required for transitioning between two distinct brain states (network control theory).
Results: Fig1-A displays the initial (green) and target (red) regional brain maps significantly correlated with the global neurocognitive index (hard transition); Fig1-B displays the maps related to surgical outcome grade (easy transition).
Table1 lists the specific regions that produced TLE/HP differences in total energy. A transition initiated from the ipsilateral rostral hippocampus to frontal, temporal lobes and cingulate indicates cognitive dysfunction emerges from difficulty making transitions involving the ictal temporal lobe that require higher energy. The data also suggested that good outcome in TLE is supported by easy transition involving specific sources (frontal lobe, basal ganglia, left thalamus) to select target (left/right medial amygdala, left caudal hippocampus, right rostral hippocampus, cingulate and temporal lobe).
Conclusions: Utilizing network control theory and a regionally unconstrained method we found global cognitive functioning may be driven by difficulty making certain high energy transitions, and that good surgical outcome may be driven by select easy transitions involving the ictal temporal lobe.
Our findings demonstrate the clinical value of using unconstrained network methodologies involving state transition energy to find the network organization and drivers of brain states implementing neurocognitive status and surgical outcomes.
1. H, X., et al.,
Uncovering the biological basis of control energy. Sci Adv, 2022.
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NIH toolbox for assessment of neurological and behavioral function. Neurology, 2013
3. J Jr, E.,
Outocme with respect to epileptic seizures. Funding: Joseph Tracy (PI), NIH/NINDS, R01 NS112816-01