RESUMEN
The neurobiological underpinnings of stuttering, a speech disorder characterized by disrupted speech fluency, remain unclear. While recent developments in the field have afforded researchers the ability to pinpoint several genetic profiles associated with stuttering, how these specific genetic backgrounds impact neuronal circuits and how they generate or facilitate the emergence of stuttered speech remains unknown. In this study, we identified the large-scale cortical network that characterizes stuttering using functional connectivity MRI and graph theory. We performed a spatial similarity analysis that examines whether the topology of the stuttering cortical network intersects with genetic expression levels of previously reported genes for stuttering from the protein-coding transcriptome data of the Allen Human Brain Atlas. We found that GNPTG - a gene involved in the mannose-6-phosphate lysosomal targeting pathways - was significantly co-localized with the stuttering cortical network. An enrichment analysis demonstrated that the genes identified with the stuttering cortical network shared a significantly overrepresented biological functionality of Neurofilament Cytoskeleton Organization (NEFH, NEFL and INA). The relationship between lysosomal pathways, cytoskeleton organization, and stuttering, was investigated by comparing the genetic interactome between GNPTG and the neurofilament genes implicated in the current study. We found that genes of the interactome network, including CDK5, SNCA, and ACTB, act as functional links between lysosomal and neurofilament genes. These findings support the notion that stuttering is due to a lysosomal dysfunction, which has deleterious effects on the neurofilament organization of the speech neuronal circuits. They help to elucidate the intriguing, unsolved link between lysosomal mutations and the presence of stuttering.
Asunto(s)
Corteza Cerebral , Conectoma , Lisosomas/genética , Red Nerviosa , Proteínas de Neurofilamentos/genética , Tartamudeo , Transcriptoma , Atlas como Asunto , Corteza Cerebral/metabolismo , Corteza Cerebral/fisiopatología , Humanos , Imagen por Resonancia Magnética , Metaanálisis como Asunto , Red Nerviosa/metabolismo , Red Nerviosa/fisiopatología , Tartamudeo/genética , Tartamudeo/metabolismo , Tartamudeo/fisiopatología , Transcriptoma/genética , Transferasas (Grupos de Otros Fosfatos Sustitutos)/genética , Transferasas (Grupos de Otros Fosfatos Sustitutos)/metabolismoRESUMEN
Functional connectivity MRI (fcMRI) has become instrumental in facilitating research of human brain network organization in terms of coincident interactions between positive and negative synchronizations of large-scale neuronal systems. Although there is a common agreement concerning the interpretation of positive couplings between brain areas, a major debate has been made in disentangling the nature of negative connectivity patterns in terms of its emergence in several methodological approaches and its significance/meaning in specific neuropsychiatric diseases. It is still not clear what information the functional negative correlations or connectivity provides or how they relate to the positive connectivity. Through implementing stepwise functional connectivity (SFC) analysis and studying the causality of functional topological patterns, this study aims to shed light on the relationship between positive and negative connectivity in the human brain functional connectome. We found that the strength of negative correlations between voxel-pairs relates to their positive connectivity path-length. More importantly, our study describes how the spatio-temporal patterns of positive connectivity explain the evolving changes of negative connectivity over time, but not the other way around. This finding suggests that positive and negative connectivity do not display equivalent forces but shows that the positive connectivity has a dominant role in the overall human brain functional connectome. This phenomenon provides novel insights about the nature of positive and negative correlations in fcMRI and will potentially help new developments for neuroimaging biomarkers.