Functional connectome architecture of Alzheimer’s disease, mild cognitive impairment and behavioral variant of frontotemporal dementia: a graph analysis study.
Elisa Canu1, Federica Agosta1, Silvia Basaia1, Alessandro Meani1, Sebastiano Galantucci1, Francesca Caso1, Giuseppe Magnani2, Roberto Santangelo2, Monica Falautano2, Giancarlo Comi2, Andrea Falini3, and Massimo Filippi1,2

1Neuroimaging Research Unit, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, 2Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, 3Department of Neuroradiology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy

Synopsis

This is a graph analysis study applying a new parcellation approach, which combines the need for equal sized nodes with respecting brain anatomy, on resting state fMRI data from a population of 247 patients with neurodegenerative cognitive impairment (early [EO] and late onset [LO] Alzheimer’s disease (AD), behavioural frontotemporal dementia [bvFTD], mild cognitive impairment [MCI]) and 86 controls. Compared to other groups, AD patients showed disrupted global network connectivity, while MCI had specific regional changes, suggesting that graph-analysis is promising to detect early features of neurodegeneration. Global and regional graph network properties were able to distinguish EOAD and bvFTD.

Purpose

Graph theory provides a powerful tool to describe quantitatively the topological organization of brain connectivity. A better understanding of network disruption in Alzheimer’s disease (AD) and other neurodegenerative diseases may provide non-invasive biomarkers for dementia differential diagnosis and disease monitoring. This study aimed at investigating functional brain network architecture in late-onset (LO) and early-onset (EO) AD, mild cognitive impairment (MCI), and behavioral variant of frontotemporal dementia (bvFTD).

Methods

The study involved 122 AD patients, 61 MCI patients, and 51 age-matched controls. 35 EOAD patients were also compared with 29 bvFTD patients and 35 age-matched controls. All subjects underwent 3D T1-weighted and resting state (RS) functional MRI. Network nodes were defined parcellating the AAL atlas (Fig.1A) into 262 regions, using a new parcellation approach which combines the need for high number of equal sized nodes with respecting brain anatomy (Fig.1B). The parcellated atlas was coregistred to the RS subject space (Fig.1C) and the BOLD signal was extracted from each node and correlated among node pairs to obtain functional matrices (Fig.1D). Graph theory analysis was used to measure the global topological properties of functional brain networks and to define brain modules. Differences in regional functional networks among groups were investigated using Network-based statistic.

Results

While controls showed high-densely connected modules, AD groups and bvFTD patients showed a loss of long-distance intra-module connections, involving all modules in AD and the fronto-parietal-parahippocampal module in bvFTD (Fig.2). Regardless the age of onset, AD patients showed altered global network measures (lower network degree and clustering coefficient, and longer path length) compared to controls and to the other patient groups. Although MCI patients did not show global network alterations, they were characterized by a decreased regional functional connectivity in the fronto-parietal connections compared with controls. A decreased regional functional connectivity was prominent in the parieto-occipital connections in EOAD and in the fronto-temporal-parietal connections in bvFTD patients (Fig.3).

Discussion and Conclusions

Global graph properties of brain networks are severely altered in AD, while they are relatively maintained in the other patient groups, thus suggesting that they are promising in distinguishing EOAD from bvFTD patients. Furthermore, the fronto-parietal connectivity disruptions in MCI patients could reflect an early marker of the disease. Graph analysis is promising to detect functional changes in the early phase of neurodegenerative diseases and to serve for a prompt differential diagnosis among dementia syndromes.

Acknowledgements

Partially supported by the Italian Ministry of Health (#GR-2010-2303035) and the Alzheimer’s Drug Discovery Foundation (#20131211).

References

No reference found.

Figures

Figure 1. Brain parcellation and functional matrix reconstruction. A. AAL atlas. B. AAL parcellation into 262 equal sized nodes. C. Coregistration of the parcellated AAL atlas to the subject resting state functional image. D. Reconstruction of the subject functional matrices.

Figure 2. Module reconstruction for each group. Red=Fronto-parietal; Yellow=Fronto-parieto-parahippocampal; Blue=Insulo-operculo-striatal; Green= Inferior fronto-temporal; Pink= Occipital; Cyan=Sensorimotor.

Figure 3. Network Based Statistic. Regional functional connectivity differences between patients with early-onset Alzheimer’s disease (EOAD) and behavioral variant of frontotemporal dementia (bvFTD).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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