Objective: The impact that amyloid beta (Aβ) accumulation in the brain has on the progression of Parkinson’s disease (PD) pathology is complex and poorly understood [1][2]. The network topology of Aβ has been studied previously in Alzheimer’s disease and PD [3][4] but the relationship between Aβ accumulation and cognitive decline in PD has not yet been studied using graph theory analysis.

Background: Increased Aβ has been associated with more rapid cognitive decline in PD [4] however Aβ accumulation is also often seen in healthy aging [6]. A recent study found that PD patients with higher brain Aβ deposits (PDAβ+) scored worse on the Montreal Cognitive Assessment (MoCA) than those with lower brain Aβ deposits (PDAβ-) [7] while another study found no difference in MoCA score between PDAβ+ and PDAβ- groups [6]. To better understand this complex relationship, we used graph theory analysis which can capture subtle brain differences that would otherwise be missed by regional comparisons of radioligand binding.

Method: Our study used Parkinson’s disease patients and healthy control data from the Parkinson’s Progression Marker Initiative (PPMI) cohort which underwent a [18F]florbetaben positron emission tomography (PET) scan which can measure the density of Aβ in vivo in the brain. We performed group level connectivity analyses using 20 bilateral cortical regions of interest analyzed using Graph Analysis Toolbox for MATLAB. A SUVR > 1.43 was the cut-off for Aβ+ and MoCA < 26 was the cut-off for PD with mild cognitive impairment (PD-MCI). We performed two main comparisons: PDAβ- vs. PDAβ+ and cognitively unimpaired PD (PD-CU) vs. PD-MCI.

Results: In our dataset, there were 10/67 (15%) PDAβ+ and 15/45 (33%) PD-MCI patients, the rest being PD Aβ- and PD-CU respectively. When comparing the PDAβ+ group to the PDAβ- group, we found that the PDAβ+ group had decreased transitivity (p<0.05). When comparing the PD-CU group to the PD-MCI group, we found that the PD-MCI group had increased local efficiency and clustering (p<0.05).

Conclusion: Higher Aβ burden led to networks that were less segregated while cognitive decline paradoxically created more clustered networks. The latter result is possibly capturing the core spread of Aβ into neighbouring regions forming a clustered pattern while the PD-CU network was the noise of the weaker early Aβ spread that is more heterogenous and patchy.

References: 1] Irwin DJ, Lee VM, Trojanowski JQ. Parkinson’s disease dementia: convergence of α-synuclein, tau and amyloid-β pathologies. Nature Reviews Neuroscience. 2013 Sep;14(9):626-36. 2] Petrou M, Bohnen NI, Müller ML, Koeppe RA, Albin RL, Frey KA. Aβ-amyloid deposition in patients with Parkinson disease at risk for development of dementia. Neurology. 2012 Sep 11;79(11):1161-7 3] Kim J, Ghadery C, Cho SS, Mihaescu A, Christopher L, Valli M, Houle S, Strafella AP. Network patterns of Beta-amyloid deposition in Parkinson’s disease. Molecular neurobiology. 2019 Nov;56(11):7731-40. 4] Villemagne VL, Ong K, Mulligan RS, Holl G, Pejoska S, Jones G, O’Keefe G, Ackerman U, Tochon-Danguy H, Chan JG, Reininger CB. Amyloid imaging with 18F-florbetaben in Alzheimer disease and other dementias. Journal of Nuclear Medicine. 2011 Aug 1;52(8):1210-7. 5] Irwin DJ, Grossman M, Weintraub D, Hurtig HI, Duda JE, Xie SX, Lee EB, Van Deerlin VM, Lopez OL, Kofler JK, Nelson PT. Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: a retrospective analysis. The Lancet Neurology. 2017 Jan 1;16(1):55-65. 6] Rowe CC, Ellis KA, Rimajova M, Bourgeat P, Pike KE, Jones G, Fripp J, Tochon-Danguy H, Morandeau L, O’Keefe G, Price R. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiology of aging. 2010 Aug 1;31(8):1275-83. 7] Fiorenzato E, Biundo R, Cecchin D, Frigo AC, Kim J, Weis L, Strafella AP, Antonini A. Brain amyloid contribution to cognitive dysfunction in early-stage Parkinson’s disease: the PPMI dataset. Journal of Alzheimer’s Disease. 2018 Jan 1;66(1):229-37.

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