Multiplex connectomics reveal altered networks in frontotemporal dementia: A multisite study

Area of Science:

• Network neuroscience
• Neurodegenerative disorders
• Connectomics

Background:

• Frontotemporal dementia (FTD) is a group of neurodegenerative disorders.
• Understanding network alterations in FTD subtypes is crucial for diagnosis and treatment.
• Previous studies have explored network changes, but a multiplex approach offers a more comprehensive view.

Purpose of the Study:

• To investigate network alterations in frontotemporal dementia (FTD) subtypes using a novel multiplex connectomics approach.
• To differentiate network disruptions between behavioral variant FTD (bvFTD) and primary progressive aphasia (PPA).
• To assess the utility of multiplex connectomics in identifying neurodegenerative disease markers.

Main Methods:

• Employed a dual-layer multiplex connectomics model integrating cortical thickness (CTH) and fluorodeoxyglucose-positron emission tomography (FDG-PET).
• Analyzed data from FTD subtypes (bvFTD, PPA), mild cognitive impairment (MCI), and cognitively normal (CN) individuals across two sites.
• Utilized the BRAPH2 toolbox to derive multiplex participation coefficient (MPC) and analyze network alterations.

Main Results:

• Primary progressive aphasia (PPA) exhibited increased global multiplex participation coefficient (MPC) at both study sites, indicating disease presence.
• Nodal MPC alterations were observed in frontal, anterior cingulate, and temporal lobes in PPA, more extensive than in bvFTD when compared to controls.
• Multiplex connectomics successfully identified distinct network disruption patterns in FTD subtypes.

Conclusions:

• Multiplex connectomics is a promising tool for identifying network disruptions in neurodegenerative disorders like FTD.
• The findings highlight the potential of this approach for future research and clinical applications in diagnosing and understanding FTD.
• Network alterations in PPA are more widespread than in bvFTD, suggesting differential disease mechanisms.