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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857081 | PMC |
| http://dx.doi.org/10.3390/cancers15020556 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction.
J Cheminform
January 2025
Department of Mathematics, University of Tennessee, Knoxville, TN, 37996, USA.
Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)-based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory.
View Article and Find Full Text PDFReorganization of gray matter networks in patients with Moyamoya disease.
Sci Rep
January 2025
Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed.
View Article and Find Full Text PDFAttention-Based Interpretable Multiscale Graph Neural Network for MOFs.
J Chem Theory Comput
January 2025
The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Metal-organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective in exploring structure-property relationships and discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity and patterns.
View Article and Find Full Text PDFNew Algorithms to Generate Permutationally Invariant Polynomials and Fundamental Invariants for Potential Energy Surface Fitting.
J Chem Theory Comput
January 2025
State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, People's Republic of China.
Symmetric functions, such as Permutationally Invariant Polynomials (PIPs) and Fundamental Invariants (FIs), are effective and concise descriptors for incorporating permutation symmetry into neural network (NN) potential energy surface (PES) fitting. The traditional algorithm for generating such symmetric polynomials has a factorial time complexity of , where is the number of identical atoms, posing a significant challenge to applying symmetric polynomials as descriptors of NN PESs for larger systems, particularly with more than 10 atoms. Herein, we report a new algorithm which has only linear time complexity for identical atoms.
View Article and Find Full Text PDFAlterations in local activity and whole-brain functional connectivity in human immunodeficiency virus-associated neurocognitive disorders: a resting-state functional magnetic resonance imaging study.
Quant Imaging Med Surg
January 2025
Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Background: Approximately half of human immunodeficiency virus (HIV) patients experience HIV-associated neurocognitive disorders (HAND); however, the neurophysiological mechanisms underlying HAND remain unclear. This study aimed to evaluate changes in functional brain activity patterns during the early stages of HIV infection by comparing local and global indicators using resting-state functional magnetic resonance imaging (rs-fMRI).
Methods: A total of 165 people living with HIV (PLWH) but without neurocognitive disorders (PWND), 173 patients with asymptomatic neurocognitive impairment (ANI), and 100 matched healthy controls (HCs) were included in the study.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!