Epilepsy, ataxia, sensorineural deafness, tubulopathy (EAST) syndrome is an autosomal disease related to mutations in the K+ channel gene KCNJ10 (also referred to as the Kir4.1 gene). Most previous publications and reviews have focused on the renal salt-wasting pathology of the disease.[2, 3] Little on epilepsy treatment or the neurological or radiological phenotype exists in the current literature.
There has been some discussion regarding the cause of the seizures in EAST syndrome, given the findings of de/dys-myelination in KCNJ10 knockout mice. As the authors of that study (Neusch et al.) have intimated in previous correspondence there are significant differences between these mice models and humans, with mice dying soon after birth. In addition, at the time of the initial description of the syndrome it was noted that KCNJ10 knockout mice showed evidence of extensive abnormalities of myelination, whereas routine magnetic resonance imaging (MRI) of people with EAST syndrome failed at that time to identify a specific imaging abnormality. This current series by Cross et al. confirms these imaging findings with no radiological evidence for a demyelinatory condition. This study goes further, however, showing that there is some loss of volume in the corpus callosum which may relate to an abnormality in myelin production rather than a demyelinatory abnormality per se. For these reasons the working hypothesis has remained that the epilepsy in this condition is related to a decreased potassium buffering capacity in areas of high extracellular concentration with resulting increased neuronal excitability.
The work presented here is the largest series describing the neurological presentation, early treatment, and clinical and imaging assessment in EAST syndrome. This has several interesting and important clinical implications namely an early-onset epilepsy readily controlled with antiepileptic drugs (AED) in the majority; followed by a period of remission with later re-emergence of focal seizures again controllable with AED; where there is evidence of AED resistance no specific age relationship exists. Secondly, the motor disorder in the syndrome, whilst appearing to be cerebellar in origin, both clinically and neuro-radiologically, does (as described in the series) exhibit a wide degree of phenotypic variability independent of clinical findings in other family members or age.
From an imaging perspective this work excitingly points towards the future process of quantitative radiological phenotyping. There have been previous examples where characterization of a specific radiological phenotype (e.g. ‘Band-like intracranial calcification with simplified gyration and polymicrogyria’) has led to the investigation and identification of the underlying genetic abnormality. In this paper the authors have undertaken a systematic and detailed imaging analysis of a cohort with a known and proven genetic abnormality to develop an imaging phenotype for KCNJ10. This imaging phenotype includes appearances on standard MRI sequences such as dentate nucleus signal abnormality as well as cerebellar hemispheric/vermian hypoplasia, callosal, and spinal changes. Imaging and identification of these features should be achievable in any neurological/neuroradiology unit undertaking standard sequence MRI for the investigation of EAST syndome.
What is most exciting, however, is the quantitative volumetric analysis using a multi-atlas segmentation process. The specifics of the technique are not for discussion here and other methodologies for regional brain volume assessment exist; nonetheless the importance lies in the addition of quantitative parametric imaging to the traditional ‘describe what you see’ approach that radiology has used for many years. This type of neuroradiological phenotypic modeling combining anatomical, metabolic (magnetic resonance spectroscopy), physiological (diffusion imaging and perfusion imaging), as well as functional MRI represents a paradigm shift in our ability to describe, model, and characterize the neuroradiological phenotype of disease quantitatively.
The challenges ahead lie in how we take complex imaging data sets (such as the one presented here) and combine and analyse them with standardization across centers. We are moving from an age of image pattern recognition towards a time of quantitative multi-parametric imaging with significant import not only of diagnosis, but also in disease tracking, assessment of possible therapeutic interventions and, as seen here, the identification of subtle but definable imaging phenotypes associated with a specific genetic abnormality.