1
|
Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review. Neuropsychol Rev 2021; 32:651-675. [PMID: 34235627 DOI: 10.1007/s11065-021-09512-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 04/23/2021] [Indexed: 10/20/2022]
Abstract
Graph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks. This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest alterations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention.
Collapse
|
2
|
Imms P, Domínguez D JF, Burmester A, Seguin C, Clemente A, Dhollander T, Wilson PH, Poudel G, Caeyenberghs K. Navigating the link between processing speed and network communication in the human brain. Brain Struct Funct 2021; 226:1281-1302. [PMID: 33704578 DOI: 10.1007/s00429-021-02241-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 02/22/2021] [Indexed: 01/21/2023]
Abstract
Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.
Collapse
Affiliation(s)
- Phoebe Imms
- Mary MacKillop Institute for Health Research, Australian Catholic University, 5/215 Spring Street, Melbourne, VIC, 3000, Australia.
| | - Juan F Domínguez D
- Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | - Alex Burmester
- Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, 3/161 Barry Street, Carlton, VIC, 3053, Australia
| | - Adam Clemente
- Mary MacKillop Institute for Health Research, Australian Catholic University, 5/215 Spring Street, Melbourne, VIC, 3000, Australia
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, 50 Flemington Road, Parkville, VIC, 3052, Australia
| | - Peter H Wilson
- Healthy Brain and Mind Research Centre, School of Behavioural, Health and Human Sciences, Faculty of Health Sciences, Australian Catholic University, 115 Victoria Parade, Fitzroy, VIC, 3065, Australia
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, 5/215 Spring Street, Melbourne, VIC, 3000, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia
| |
Collapse
|