Differential Patterns of Change in Brain Connectivity Resulting from Severe Traumatic Brain Injury

A Graph-Based Statistical Approach to Identifying Functional Connectivity Networks in Patients with Traumatic Brain Injury

Objectives

Traumatic brain injury (TBI) is one of the most common types of brain injuries associated with cognitive impairments. Functional magnetic resonance imaging (fMRI) studies can provide a unique opportunity to examine brain connectivity patterns and understand the neural substrates of cognitive outcomes following traumatic injury. Therefore, this study aims to determine changes in functional connectivity patterns in patients with TBI compared to healthy individuals using two graph models, adaptive dense subgraph discovery (ADSD) and variance component.

Materials & Methods

This study used fMRI data downloaded from https://openneuro.org. These data included 14 patients with TBI aged between 18 and 36 and 12 healthy individuals (female: N=6, male: N=6) aged between 19 and 52. Out of the 74 regions examined, a cluster of 18 regions related to TBI was identified using the ADSD model. Subsequently, these identified regions were used as input for the variance component model to investigate changes in connectivity patterns.

Results

Functional connectivity between an 18-brain region cluster, such as the Rectus (Left, Right), Supp_Motor_Area (Left, Right), and Middle Cingulum (Left, Right), differed between the patient and healthy groups. Based on the analysis of functional connectivity between pairs of brain regions, 153 connections between pairs of brain regions were compared in the two groups, out of which 63 connections showed significant differences between the two groups. Compared to other regions, Supp_Motor_Area_Right and Rectus_Left had more connections.

Conclusion

The study's results indicate that the functional connectivity between the Cingulum, Hippocampus, Fusiform, Supp_Motor_Area, and Precentral regions differs between the two groups. Since these regions are involved in processes such as memory, learning, spatial orientation, face recognition, coordination, and motor control, changes in their functional connectivity may lead to impairments in these areas.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Predicting the Path: How Machine Learning Can Identify Subtypes of Epilepsy and Predict Disease Progression

Identification of Four Biotypes in Temporal Lobe Epilepsy via Machine Learning on Brain Images Jiang Y, Li W, Li J, Li X, Zhang H, Sima X, Li L, Wang K, Li Q, Fang J, Jin L, Gong Q, Yao D, Zhou D, Luo C, An D. Nat Commun . 2024;15(1):2221. PMCID: PMC10933450 Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional magnetic resonance imaging from 296 individuals with focal epilepsy originating from the temporal lobe epilepsy (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify 2 hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These 4 subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression, and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields stratification and prognostication—a necessary step for precise medicine.

Brain functional connectivity in hyperthyroid patients: systematic review

INTRODUCTION

Functional connectivity (FC) is the correlation between brain regions' activities, studied through neuroimaging techniques like fMRI. It helps researchers understand brain function, organization, and dysfunction. Hyperthyroidism, characterized by high serum levels of free thyroxin and suppressed thyroid stimulating hormone, can lead to mood disturbance, cognitive impairment, and psychiatric symptoms. Excessive thyroid hormone exposure can enhance neuronal death and decrease brain volume, affecting memory, attention, emotion, vision, and motor planning.

METHODS

We conducted thorough searches across Google Scholar, PubMed, Hinari, and Science Direct to locate pertinent articles containing original data investigating FC measures in individuals diagnosed with hyperthyroidism.

RESULTS

The systematic review identified 762 articles, excluding duplicates and non-matching titles and abstracts. Four full-text articles were included in this review. In conclusion, a strong bilateral hippocampal connection in hyperthyroid individuals suggests a possible neurobiological influence on brain networks that may affect cognitive and emotional processing.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42024516216.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.