The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia

Abstract

Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.

Functional and structural network changes related with cognition in semantic dementia longitudinally

“…In the current study, we used the global and local graph properties to identify specific patterns of functional and structural alterations and investigated the neural correlates of cognitive performance in SD. Graph theory allows describing the brain as a complex network identifying topological properties that reflects global and local information communication, which has been increasingly applied in evaluating the brain connectivity in FTD (Nigro, Filardi, et al, 2022 ). Recent studies have reported that the abnormalities of SD in brain structure and function were predominantly in the frontal, temporal, and subcortical regions, progressing to posterior areas eventually (Yu et al, 2021 ).…”

“…Given that the semantic network is a highly interactive system and the role of altered structural and functional network should be considered. Graph analyses have recently been applied to the brain networks to elucidate the SD‐associated topological changes (Nigro, Filardi, et al, 2022 ). Results from functional MRI studies reported reduced functional connectivity in global/frontolimbic network integration and demonstrated elevated local connectivity within the prefrontal cortex in SD, but evidence regarding the topological properties of white matter network is still lacking (Dev et al, 2021 ; Pengo et al, 2022 ).…”

Transdiagnostic brain module dysfunctions across sub-types of frontotemporal dementia: a connectome-based investigation

“…This suggests a trend towards dedifferentiation in the overall brain network organization within these individuals. Previous research has reported lower average clustering coefficients, decreased global efficiency, and increased path lengths in both BV-FTD and SV-FTD cases [31]. Global efficiency and path length provide insights into the connectivity distance within the brain, while clustering coefficients reflect network segregation and its role in processing information within specialized brain modules [31].…”

“…Previous research has reported lower average clustering coefficients, decreased global efficiency, and increased path lengths in both BV-FTD and SV-FTD cases [31]. Global efficiency and path length provide insights into the connectivity distance within the brain, while clustering coefficients reflect network segregation and its role in processing information within specialized brain modules [31]. A higher clustering coefficient indicates a greater tendency for the brain to perform specialized processes within these network modules [32].…”

Rapid eye movement sleep behavior disorder and its relation to Parkinson's disease: The potential of graph measures as brain biomarkers to identify the underlying physiopathology of the disorder

“…The graph theory analysis allows the study of brain areas and their different networks by decreasing the complex structure of the brain network to a set of parameters that characterize the network topological measure (Bullmore & Sporns, 2009;Semmel et al, 2022). Furthermore, promising findings suggest that the extraction of topological measures from any imaging modality using graph theory analysis may improve clinical interpretability (Nigro et al, 2022;Semmel et al, 2022). As yet, the graph theory was mainly used to describe brain diagrams obtained with anatomical, morpho- Structural imaging modalities and functional connectivity in iRBD have been thoroughly reviewed recently, but no relationship was established between the features extracted from this theory using various imaging modalities and clinical findings (Campabadal et al, 2021;Valli et al, 2022).…”