Brain Organoid and Systems Neuroscience Journal最新文献

Traumatic brain injuries (TBIs) are characterized by widespread complications that exert a debilitating effect on the well-being of the affected individual. TBIs are associated with a multitude of psychiatric and medical comorbidities over the long term. Furthermore, no medications prevent secondary injuries associated with a primary insult. In this perspective article, we propose applying graph theory via the construction of disease comorbidity networks to identify high-risk patient groups, offer preventive care to affected populations, and reduce the disease burden. We describe the challenges associated with monitoring the development of comorbidities in TBI subjects and explain how disease comorbidity networks can reduce disease burden by preventing disease-related complications. We further discuss the various methods used to construct disease comorbidity networks and explain how features derived from a network can help identify subjects who might be at risk of developing post-traumatic comorbidities. Lastly, we address the potential challenges of using graph theory to successfully manage comorbidities following a TBI.

In the big data era, with a lack of comparable functional neuroimaging data, researchers try to combine heterogeneous data of different lengths, trimming those to the same number of timepoints (NTPs). However, the effects of trimming blood-oxygen-level dependent (BOLD) signal data on functional connectivity (FC) are still poorly understood.

Resting-state functional magnetic resonance imaging data from thirty healthy subjects were pre-processed for five different NTPs, from which FC matrices were computed. These BOLD signal correlation matrices were binarized for several thresholds, excluding weak correlations. Graph metrics were computed to study FC differences between different NTPs. The study included node degree analysis for each brain region and assessment of small-worldness coefficients ($\sigma$ and $\omega$), whereas in small-world networks, characteristic values are $\sigma$ > 1 and $\omega$ $\approx$ 0, indicating a balance between high clustering coefficients and short characteristic path lengths.

A tendency of decreasing global network degrees for higher NTPs was observed, translating the loss of stronger correlations with longer BOLD signals. Trimming such data affects brain regions differently, probably due to brain network dynamics. Regarding small-worldness, we observed that $\sigma$ was greater than 1 for all the different NTPs, showing an increasing trend for higher NTPs (median value: ${\sigma }_{\mathrm{BRAIN}}=$ 3.05). In addition, $\omega$ consistently remained greater than 0 for all NTPs, gradually approaching 0 as the NTPs increased (median value ${\omega }_{\mathrm{BRAIN}}=$ 0.20). As such, the results suggest a tendency for an increase of small-world properties for increasing NTPs. Nonetheless, the overall properties of brain networks almost remain constant. In conclusion, trimming BOLD signal data leads to small differences in FC.

Epilepsy is a disease of altered brain networks. The monitoring and analysis of functional connectivity and network properties can yield a better understanding of the underlying pathology, and improve treatment and prognostics. Identifying hub network regions has been in the spotlight of network neuroscience studies in epilepsy, as monitoring these areas can provide a perspective of the network’s local and global organization. Functional network analysis can be especially useful in Medically Refractory Epilepsy (MRE) cases, where surgical intervention is necessary for seizure relief. In such cases, the delineation of the epileptogenic zone, which represents the surgical target, is a very crucial procedure, which can be enhanced by understanding the underlying network topology. In this review, we will explore the expanding body of literature on functional connectivity of interictal intracranial electrophysiologic data, focusing on the interpretation of network properties, global or local, for identifying epileptogenic tissue. We will emphasize functional connectivity at high frequencies (above 80 Hz), as during the past decade High-Frequency Oscillations (HFOs) have been increasingly recognized as a promising biomarker of the seizure onset zone. We will conclude the review with an assessment of current limitations and a discussion of future research paths.