Dual PET-fMRI reveals a link between neuroinflammation, amyloid binding and compensatory task-related brain activity in Alzheimer’s disease

Abstract

The interplay among neuropathological mechanisms underlying Alzheimer’s disease (AD), as neuroinflammation and amyloid-beta (Aβ), as well their impact on neuronal function remains elusive. A major gap in knowledge is the functional impact of neuroinflammation. The posterior cingulate cortex (PCC), as the most prominent site of amyloid pathology in AD, is a pivotal region to investigate the concomitant presence of pathophysiological mechanisms such as microglia activation, indexing neuroinflammation, and changes in task related activity. Here we used a dual PET approach to simultaneously study Aβ load and neuroinflammation (TSPO uptake marker), using 11C-PiB and 11C-PK11195 radiotracers, respectively and fMRI to study task related neural activation in an AD sample (n = 19) and matched controls (n = 19). Here we show significantly increased Aβ deposition, neuroinflammation and brain activity related to a visual object working memory task in this key region. Microglia activation was associated with increased brain activity specifically in patients, independently of amyloid binding, raising the possibility that abnormal brain activity might be restored in clinical trials aimed at reducing microglia activation.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

“…Furthermore, some studies have shown reduced posterior cingulate gyrus/precuneus activation and hyperactivation in frontal regions, including the precentral gyrus, in MCI patients in comparison to CN individuals during memory tasks (Hampstead et al ., 2011 ; Jin et al ., 2012 ; Terry et al ., 2015 ). In AD, MTL hypoactivation, reduced posterior cingulate gyrus/praecuneus deactivation and frontal hyperactivation are approximately consistent findings during memory tasks (Canário et al ., 2022 ; Jonin et al ., 2022 ; Parra et al ., 2013 ; Petrella et al ., 2007 ; Pihlajamäki and Sperling, 2009 ; Yetkin et al ., 2006 ).…”

Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging

“…The early detection of AD is of paramount importance for effective patient management and prognostication. PET tracers for glucose metabolism [8,[22][23][24], amyloid [25,26], tau [27][28][29][30][31][32], and neuroinflammation imaging [33][34][35][36][37][38][39], as well as MRI techniques such as arterial spin labeling (ASL) [40][41][42], resting-state fMRI and task-related fMRI [43][44][45][46][47][48][49][50][51][52], multi-nuclear MRI [53,54], and chemical exchange saturation transfer (CEST) [55,56], have provided valuable insights into the pathological mechanisms of AD in patients (Table 1). Synapse loss is a major pathological change in AD, but its relationship to functional and structural connectivity dysfunction remains unclear.…”