Connective Core Structures in Cognitive Networks: the Role of Hubs

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

Graph Theory Measures and Their Application to Neurosurgical Eloquence

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.

Brain connectivity in patients with dystonia during motor tasks

OBJECTIVE

This study aims to investigate alterations of brain connectivity using multivariate electroencephalographic data to provide new insights of the brain connectivity dynamics of dystonia.

APPROACH

We recorded electroencephalography (EEG) of patients with right upper limb idiopathic focal dystonia and paired controls during resting state, writing-from-memory, and finger-tapping tasks. We applied power spectrum analyses considering the mu, beta and gamma rhythms of the motor cortex and analyzed brain connectivity networks and microstates (MS).

MAIN RESULTS

The power spectra results showed that patients had a loss of desynchronization of the beta rhythm during the writing task. We observed differences in the structure of the connective core in beta rhythm, as well as, in the intensity of the patient's hubs observed with basis in path length measures in mu and beta rhythms. Abnormalities were also identified in MS of default mode networks of patients associated with its performances during motor tasks.

SIGNIFICANCE

The EEG connectivity analyses provided interesting insights about the cortical electrophysiological patterns in dystonia, such as loss of event-related desynchronization, changes in the effective connectivity with similar signature to other neurological diseases, indications of alterations in the default-mode-network. Our findings are consistent with previously described connectivity abnormalities in neuroimaging studies confirming that dystonia is a network disorder.