Neuroinflammation and functional connectivity in Alzheimer’s disease: interactive influences on cognitive performance

Abstract

Neuroinflammation is a key part of the etio-pathogenesis of Alzheimer's disease. We test the relationship between neuroinflammation and the disruption of functional connectivity in large-scale networks, and their joint influence on cognitive impairment.We combined [ 11 C]PK11195 positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) in 28 humans (13 females/15 males) with clinical diagnosis of probable Alzheimer's disease or mild cognitive impairment with positive PET biomarker for amyloid, and 14 age-, sex-, and education-matched healthy humans (8 Significance StatementNeuroinflammation is an important aspect of Alzheimer's disease (AD), but it was not known whether the influence of neuroinflammation on brain network function in humans was important for cognitive deficit.Our study provides clear evidence that in vivo neuroinflammation in AD impairs largescale network connectivity; and that the link between inflammation and functional network connectivity is relevant to cognitive impairment.We suggest that future studies should address how neuroinflammation relates to network function as AD progresses; and whether the neuroinflammation in AD is reversible, as the basis of immunotherapeutic strategies to slow the progression of AD.

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

“…Therefore, future studies need to consider the independent and synergistic effects of many possible biomarkers, based on MRI, computed tomography, positron-emission tomography, CSF, blood and brain histopathology. For example, functional network impairment may be related to tau expression and tau pathology, amyloid load, or neurotransmitter deficits in neurodegenerative diseases, independent of atrophy [29,[40][41][42]. Importantly, studies need to recognise the rich multivariate nature of cognition and of neuroimaging in order to improve stratification procedures, e.g.…”

“…Importantly, studies need to recognise the rich multivariate nature of cognition and of neuroimaging in order to improve stratification procedures, e.g. based on integrative approaches that explain individual differences in cognitive impairment [29,43].…”

“…The location of the key cortical regions in each network was identified by spatial-ICA in an independent dataset of 298 age-matched healthy individuals from a large population-based cohort [22]. Full details about preprocessing and node definition are described previously [30]. Four networks commonly affected by neurodegenerative diseases including FTD [21] were identified by spatially matching to pre-existing templates [31].…”

The effects of age on resting‐state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

“…Similar to the CBF analyses, the GMV findings generalized across voxel-wise and component-based analysis, but the component-based analysis seemed to be more sensitive to the age effects on RSFA in both CBF and GMV data sets. The greater generalization across data sets with independent component analysis than voxel-based analysis may reflect several factors (Calhoun & Adali, 2008;Passamonti et al, 2019;Sui et al, 2012), for example, reducing the burden of multiple comparisons, pooling information across multiple voxels with similar profiles, separating sources of signal with different etiology but with overlapping topologies and possibly improving the spatial correspondence across imaging modalities with different spatial scales, sequence parameters and signal properties. Therefore, the use of component-based analysis in studies comparing approaches for normalization of physiological signals may improve understanding the nature of the signal and the extent to which these neuroimaging modalities are related to one another.…”

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

“…Similar to the CBF analyses, the GMV findings generalized across voxel-wise and component-based analysis, but the component-based analysis seemed to be more sensitive to the age effects on RSFA in both CBF and GMV datasets. The greater generalization across datasets with independent component analysis than voxel-based analysis may reflect several factors (Calhoun and Adali, 2008;Sui et al, 2012;Passamonti et al, 2019), e.g. reducing the burden of multiple comparisons, pooling information across multiple voxels with similar profiles, separating sources of signal with different etiology but with overlapping topologies and possibly improving the spatial correspondence across imaging modalities with different spatial scales, sequence parameters and signal properties.…”