Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy

Abstract

Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.

Fig. 1 |

The classic paradigm of neurobehavioural comorbidities of epilepsy. The outer ring depicts five major factors (and associated exemplars) that have long been considered, alone or in combination, to exert direct and/or indirect influences on the causes and course of neurobehavioural comorbidities in epilepsy.

Fig. 2 |

Subcortical, cortical and diffusion findings in ENIGMA-Epilepsy. a Subcortical volume (left) and cortical thickness (right) abnormalities shared across all epilepsy syndromes in the ENIGMA-Epilepsy meta-analysis. Coloured bar represents Cohen’s d effect size estimates for case–control differences in each subcortical or cortical region. Red and yellow shading depicts regions with greater volume loss or thinning in patients relative to controls, whereas blue shading represents regions with higher volume relative to controls. Patients with epilepsy had lower volumes of the bilateral thalami and hippocampi and right pallidum relative to controls, and increased volume of the lateral ventricles. The patients also showed cortical thinning in the precentral and paracentral gyri bilaterally and in the left prefrontal, superior parietal and cuneus. b White matter microstructural differences across 38 fibre tracts for the ‘all epilepsies’ cohort compared with controls. All values represent Cohen’s d effect size estimates for differences in fractional anisotropy and mean diffusivity between each patient group and healthy controls. Positive effect sizes reflect diffusion values greater than controls and negative effect sizes represent values lower than controls. The y and z values represent the slice number for the coronal and axial planes, respectively. Across all epilepsies, the greatest effects on fractional anisotropy were observed in the body and genu of the corpus callosum, external capsule, cingulum and corona radiata. The greatest effects on mean diffusivity were observed in the external capsule, anterior corona radiata and superior longitudinal fasciculus. Part a reprinted with permission from ref.. Part b adapted with permission from ref..

Fig. 3 |

Cognitive phenotypes and their distribution. a | The Epilepsy Connectome Project identified three cognitive phenotypes in patients with temporal lobe epilepsy (TLE): intact or minimally impaired, comparable to healthy controls; generalized impairment, with abnormal scores across all administered cognitive metrics; and focal impairment, predominantly affecting memory, language and/or executive function. The z-scores represent performance of the epilepsy groups compared with controls, with negative values indicating worse performance. b | Distribution of cognitive phenotypes across seven investigations in individuals with TLE^,,,,,,.

Fig. 4 |

Diffusion and network findings across discrete cognitive phenotypes of TLE. a | Differences in superficial white matter (SWM) fractional anisotropy and mean diffusivity across cognitive phenotypes in individuals with temporal lobe epilepsy (TLE) relative to healthy controls. Blue and cyan represent lower values and red and yellow represent higher values than controls. b | Local efficiency differences between healthy controls and each cognitive phenotype within perisylvian regions (depicted in red), including the pars triangularis (pTRI)/pars opercularis (pOPC), superior temporal gyrus (STG) and supramarginal gyrus (SMG). Significant differences between patients with TLE and healthy controls are depicted in grey and blue. The line graphs demonstrate differences in local efficiency within the left and right STG between healthy controls and each cognitive phenotype across different network densities. Shaded areas represent the upper and lower boundaries of local efficiency for healthy controls. In both panels, patients with single-domain memory or language impairments demonstrate findings distinct from patients with multiple domain impairments. Patients with both language and memory impairment showed widespread SWM abnormalities, whereas patients with memory impairments alone showed SWM abnormalities predominantly in the bilateral temporal lobes and cingulum. Patients with language impairments alone showed distinct abnormalities in perisylvian network structure that were not apparent at the regional SWM level. Adapted with permission from ref..

Fig. 5 |

A next-generation paradigm for neurobehavioural phenotypes of epilepsy. The outer ring depicts six major factors (and associated exemplars) that alone or in combination are proposed to exert direct and/or indirect influences on the causes and course of neurobehavioural phenotypes in epilepsy.