Effective connectivity in temporal lobe epilepsy with hippocampal sclerosis
HS is often accompanied by extrahippocampal abnormalities, especially along the circuit of Papez, which has been demonstrated by visual analysis and region‐of‐interest (ROI) quantification of manual volumetry.4 Recently, the advent of voxel‐based morphometry and cortical thickness measurements using automatic whole‐brain analysis has facilitated a more comprehensive assessment of the structural changes extending beyond the hippocampus in TLE patients with HS.7 These studies have suggested that TLE patients with HS have volume reduction within the temporal lobes and the thalamus.5 The involvement of the thalamus in TLE patients with HS has also been found in diffusion tensor imaging (DTI) studies with reduced fractional anisotropy.12
It is well known that limbic projections are composed of hippocampal efferent projecting to the mammillary body via the fornix, with subsequent projection to the thalamus.5 Thus, it is unsurprising that structural changes occur in the thalamus in TLE patients with HS. A postmortem study indicates that approximately 25% of TLE patients have neuronal loss and gliosis in the thalamus.13 In addition, an interictal positron emission tomography (PET) study has revealed thalamic hypometabolism in TLE patients.14 However, it is unclear whether the thalamus plays a significant role in TLE patients with HS compared to TLE patients without HS.
In recent years, the concept of focal seizures has changed, whereas once focal seizures were thought to originate in an anatomically isolated focus, now they are thought to occur within networks limited to one hemisphere.15 Based on this concept, TLE with HS is thought to be a network disease with widespread extratemporal effect,16 and several studies have suggested the alteration of connectivity in TLE patients with HS.17 However, the mechanism of altered functional connectivity in TLE patients with HS has not been clearly elucidated.
Unlike functional connectivity measures, which explore non‐directional statistical dependencies between brain regions, effective connectivity refers to patterns of directed causal influences between brain regions.19 In other words, functional connectivity relies on disproving the null hypothesis that separates brain areas function independently of one another, while effective connectivity seeks to model these relationships by adding weighted directionality to them.19 Causal modeling has further enhanced our understanding of neuroanatomy by modeling dynamical interactions within a network of ROI instead of activity in individual brain regions.20 The most prevalent approaches to effective connectivity analysis are dynamic causal modeling, Granger causality, and structural equation modeling (SEM).19 SEM is a statistical method that analyzes the effective connectivity between observed and latent variables to test hypotheses and confirm relationships.21 It has the advantage of allowing fast and robust computations and can be used for rather complicated models. SEM analysis is popular in the social sciences as well as medicine, because of its long history and accessibility; packaged computer programs allow researchers to obtain results without the inconvenience of understanding experimental design and control, effect and sample sizes, and numerous other factors that are part of good research design.19 SEM has been widely used to investigate effective connectivity in brain disorders such as Parkinson's disease, stroke, and schizophrenia.20 Those studies have revealed that these diseases involve alterations of effective connectivity in the brain. However, few studies have used SEM to investigate effective connectivity in TLE patients with HS.