Abstract
PurposePrevious imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics.Methods20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured.ResultsIn intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF.ConclusionsDiffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.
Highlights
Temporal lobe epilepsy (TLE) is one of the most frequent drug-resistant epilepsies, commonly associated with hippocampal sclerosis, a surgically-amenable lesion (Wiebe et al, 2001)
We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics
As increased diffusivity could be driven by reduced neurite density or tissue atrophy, we explored these relationships with linear regression models
Summary
Temporal lobe epilepsy (TLE) is one of the most frequent drug-resistant epilepsies, commonly associated with hippocampal sclerosis, a surgically-amenable lesion (Wiebe et al, 2001). Histopathological studies have identified widespread neuronal loss and gliosis (Cavanagh and Meyer, 1956; Falconer et al, 1964; Kuzniecky et al, 1987; Nishio et al, 2000) and altered myelination of temporal neocortex (Hardiman et al, 1988; Thom et al, 2000; Kasper et al, 2003; Eriksson et al, 2004) In line with these observations, imaging studies have shown extensive neocortical (Keller and Roberts, 2008; Bernhardt et al, 2010; Bernhardt et al, 2009; Blanc et al, 2011; Labate et al, 2011; Vaughan et al, 2016) and subcortical atrophy (Keller and Roberts, 2008; Bonilha et al, 2010; Bernhardt et al, 2012; Coan et al, 2014; Alvim et al, 2016) indicative of a system-level disorder (Keller et al, 2014; Bernhardt et al, 2015; Vaughan et al, 2016; de Campos et al, 2016). The tensor model makes the assumption of a single fibre population in each voxel (Jeurissen et al, 2013), even though a given voxel may contain multiple fibre populations with diverse orientations (Jones et al, 2013; Maier–Hein et al, 2017)
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