Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults

Abstract

The accumulation of amyloid plaques is one of the earliest pathological changes in Alzheimer’s disease (AD) and may occur 20 years before the onset of symptoms. Examining associations between amyloid pathology and other early brain changes is critical for understanding the pathophysiological underpinnings of AD. Alterations in gray matter networks might already start at early preclinical stages of AD. In this study, we examined the regional relationship between amyloid aggregation measured with positron emission tomography (PET) and gray matter network measures in elderly subjects with subjective memory complaints. Single-subject gray matter networks were extracted from T1-weigthed structural MRI in cognitively normal subjects (n = 318, mean age 76.1 ± 3.5, 64% female, 28% amyloid positive). Degree, clustering, path length and small world properties were computed. Global and regional amyloid load was determined using [18F]-Florbetapir PET. Associations between standardized uptake value ratio (SUVr) values and network measures were examined using linear regression models. We found that higher global SUVr was associated with lower clustering (β = −0.12, p < 0.05), and small world values (β = −0.16, p < 0.01). Associations were most prominent in orbito- and dorsolateral frontal and parieto-occipital regions. Local SUVr values showed less anatomical variability and did not convey additional information beyond global amyloid burden. In conclusion, we found that in cognitively normal elderly subjects, increased global amyloid pathology is associated with alterations in gray matter networks that are indicative of incipient network breakdown towards AD dementia.

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

“…A between-group analysis performed by Chetelat et al indicated that, in participants with SCD, individuals with a higher level of amyloid deposition showed significant gray matter atrophy compared with individuals with a low level of amyloid deposition [ 180 ]. Further correlation analyses between imaging modalities also supported the relationship between amyloid pathology and reduced integrity of brain structures in both the gray matter and WM ranging from voxel level to brain connectome properties in subjects with SCD [ 101 , 181 , 182 ]. Ferreira et al tested a disease severity index generated from a multivariate analysis involving amyloid PET and structural MRI data, and this index may potentially identify individuals with SCD with the AD-like pattern, as an appropriate risk population [ 183 ].…”

“…Both groups of researchers showed that the gray matter network of individuals with SCD was more randomly organized than HCs, and the disrupted network properties were associated with a steeper decline in global cognition and a higher risk of disease progression. Moreover, Ten Kate et al [ 101 ] observed an association between a higher level of global amyloid deposition and lower clustering and fewer small-world properties of gray matter structural networks in subjects with SCD. Overall, although some negative results have been reported, individuals with SCD related to AD have been repeatedly shown to present a reduced gray matter volume and cortical thickness and a disrupted gray matter network.…”

“…DC in bilateral hippocampus and left fusiform relate to total tau and phosphorylated tau, but not to amyloid deposition Scheef et al (2012) [ 59 ] Memory clinic consultation 2 binary questions FDG-PET MRI Cross-sectional Controls: n = 56 (66.4±7.2) SMI: n = 31 (67.6±6.2) Hypometabolism in right precuneus and hypermetabolism in right medial temporal and reduced gray matter volume in hippocampus in SMI group. Longitudinal memory decline relates to reduced glucose metabolism in right precuneus in SMI Shokouhi et al (2019) [ 191 ] E-Cog 18 F-flortaucipir PET 18 F-florbetapir PET Cross-sectional All: n = 86 (78±8) Tau pathology predict everyday planning in SCD, and amyloid pathology relate to everyday organization and memory in SCD Teipel et al (2018) [ 178 ] 2 binary questions 18 F-florbetapir PET MRI EEG Cross-sectional Amyloid negative: n = 63 (75.9±3.5) Amyloid positive: n = 255 (76.7±3.5) No significant relationship between amyloid load and phase-lag index in any frequency band Teipel et al (2017) [ 182 ] 2 binary questions 18 F-florbetapir PET MRI Cross-sectional All: n = 318 (76.1±3.5) Association between amyloid uptake and reduced gray matter structural integrity and poorer objective cognitive performance Ten Kate et al (2018) [ 101 ] 2 binary questions 18 F-florbetapir PET MRI Cross-sectional All: n=318(76 74±78) Amyloid-: n = 230 (76 73±78) Amyloid+: n = 88 (77 75±79) Association between higher global SUVR and lower clustering, and small world values in orbito-and dorsolateral frontal and parietooccipital regions. Wirth et al (2018) [ 184 ] Memory clinic consultation 18 F-florbetapir PET MRI FDG-PET Cross-sectional HC: n=41 (66.1 ±7.7) ApoE ɛ4+: n = 17 (63.9±8.6) SCD: n=16 (68.9±7.3) MCI: n=30 (73.4±7.2) AD: n=22 (68.7±9.4) (1) in medial-temporal regions, local gray matter volume reduction exceeded hypometabolism, (2) in tempor...…”

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease

“…We examined relationships between grey matter network small-world coefficient and established Alzheimer’s disease markers within mutation carriers. Previous research suggested grey matter networks may be disrupted in response to Aβ accumulation, precipitating cognitive decline ( Ten Kate et al , 2018 ). For this reason, our models included either precuneus PET Aβ as a predictor and grey matter network metrics as outcomes or grey matter network metrics as a predictor and cortical thickness (precuneus), brain metabolism (meta-ROI), or cognition (DIAN cognitive composite) as the respective outcomes.…”

“…In pre-dementia stages, such loss of network integrity predicts clinical progression and cognitive decline ( Dicks et al , 2018 ; Tijms et al , 2018 ). The presence of amyloid β (Aβ) pathology in cognitively normal individuals has also been associated with grey matter network alterations ( Tijms et al , 2016 ; Ten Kate et al , 2018 ; Voevodskaya et al , 2018 ). Together, these observations suggest that these network properties change over the course of Alzheimer’s disease, from early stages, and that individual grey matter network extractions could possibly be used to monitor disease progression.…”