A systems-level analysis highlights microglial activation as a modifying factor in common forms of human epilepsy

Abstract

The common human epilepsies are associated with distinct patterns of reduced cortical thickness, detectable on neuroimaging, with important clinical consequences. To explore underlying mechanisms, we layered MRI-based cortical structural maps from a large-scale epilepsy neuroimaging study onto highly spatially-resolved human brain gene expression data, identifying >2,500 genes overexpressed in regions of reduced cortical thickness, compared to relatively-protected regions. The resulting set of differentially-expressed genes shows enrichment for microglial markers, and in particular, activated microglial states.Parallel analyses of cell-specific eQTLs show enrichment in human genetic signatures of epilepsy severity, but not epilepsy causation. Post mortem brain tissue from humans with epilepsy shows excess activated microglia. In an experimental model, depletion of activated microglia prevents cortical thinning, but not the development of chronic seizures. These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Connectome and microcircuit models implicate atypical subcortico-cortical interactions in autism pathophysiology

“…Spatial decoding of macroscale distortion patterns with post-mortem gene expression maps provided potential etiological substrates of our findings. Recent studies in healthy brain organization 96,97 , development 37 , and disease 98,99 have shown that how such analyses can help understanding the relationship between macroscopic neuroimaging phenotypes and spatial variations at the molecular scale 100 . In a prior study, similar approaches were used to identify genetic factors whose expression correlated to maps of cortical morphological variations in autism, and pointed to transcriptionally downregulated genes implicated in autism 101 .…”

An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

“…These findings have already been applied in the study of healthy adults (Hawrylycz et al, 2015; Park et al, 2020b; Q. Xu et al, 2020) and typically developing adolescents (Mascarell Maričić et al, 2020; Padmanabhan and Luna, 2014; Paquola et al, 2019a; Vértes et al, 2016; Whitaker et al, 2016), as well as individuals suffering from prevalent brain disorders (Altmann et al, 2018; Hashimoto et al, 2015; Klein et al, 2017; Park et al, 2020a; Patel et al, 2021; Romero-Garcia et al, 2019). The gene sets that co-vary with in vivo findings can furthermore be subjected to gene set enrichment analyses to discover potentially implicated molecular, cellular, and pathological processes (Ashburner et al, 2000; Carbon et al, 2019; Chen et al, 2013; Dougherty et al, 2010; Kuleshov et al, 2016; Morgan et al, 2019; Romero-Garcia et al, 2018; Subramanian et al, 2005).…”

“…The burgeoning imaging genetics field, bolstered by open databases of human transcriptomics [52][53][54][55] , has illustrated that macroscopic neuroimaging phenotypes can be related to spatial variations at the molecular scale 57 in contexts of healthy brain organization 56,57 , brain development 21 , and disease 51,58,59 . Combined with a method highlighting developmental time windows 51,55 , these approaches provide new insights into spatio-temporal shifts in gene expression that underly imaging and connectome findings.…”

An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

“…Recently established resources, such as the Allen Human Brain Atlas ( Arnatkeviciute et al, 2019 ; Hawrylycz et al, 2015 ), can be utilized to spatially associate macroscale imaging/connectome data with the expression patterns of thousands of genes. These findings have already been applied in the study of healthy adults ( Hawrylycz et al, 2015 ; Park et al, 2020 ) and typically developing adolescents ( Mascarell Maričić et al, 2020 ; Padmanabhan and Luna, 2014 ; Paquola et al, 2019a ; Vértes et al, 2016 ; Whitaker et al, 2016 ), as well as individuals suffering from prevalent brain disorders ( Altmann et al, 2018 ; Hashimoto et al, 2015 ; Klein et al, 2017 ; Park et al, 2021a ; Patel et al, 2021 ; Romero-Garcia et al, 2019 ). The gene sets that co-vary with in vivo findings can furthermore be subjected to gene set enrichment analyses to discover potentially implicated molecular, cellular, and pathological processes ( Ashburner et al, 2000 ; Carbon et al, 2019 ; Chen et al, 2013 ; Dougherty et al, 2010 ; Kuleshov et al, 2016 ; Morgan et al, 2019 ; Romero-Garcia et al, 2018 ; Subramanian et al, 2005 ).…”