Connectome-based markers predict the sub-types of frontotemporal dementia.

Abstract

Frontotemporal dementia (FTD) presents a complex spectrum of neurodegenerative disorders, encompassing distinct subtypes with varied clinical manifestations. This study investigates alterations in brain module organization associated with FTD subtypes using connectome analysis, aiming to identify potential biomarkers and enhance subtype prediction. Resting-state functional magnetic resonance imaging data were obtained from 41 individuals with behavioral variant frontotemporal dementia (BV-FTD), 32 with semantic variant frontotemporal dementia (SV-FTD), 28 with progressive non-fluent aphasia frontotemporal dementia (PNFA-FTD), and 94 healthy controls. Individual functional brain networks were constructed at the voxel level and binarized based on density thresholds. Modular segregation index (MSI) and participation coefficient (PC) were calculated to assess module integrity and identify regions with altered nodal properties. The relationship between modular measures and clinical scores was examined, and machine learning models were developed for subtype prediction. Both BV-FTD and SV-FTD groups exhibited decreased MSI in the subcortical module (SUB), default mode network (DMN), and ventral attention network (VAN) compared to healthy controls. Additionally, BV-FTD specifically displayed disrupted frontoparietal network (FPN) integrity compared to other FTD subtypes and controls. All FTD subtypes showed increased PC values in the insular region and reduced connections between the insular and VAN/FPN compared to controls. Moreover, significant associations between specific network alterations and clinical variables were observed. Machine learning models utilizing these matrices achieved high performance in differentiating FTD subtypes. This pilot study reveals diverse brain module organization across FTD subtypes, shedding light on both shared and distinct neurobiological underpinnings of the disorder.