Alzheimer’s disease remains incurable, and the failures of current disease-modifying strategies for Alzheimer’s disease could be attributed to a lack of in vivo models that recapitulate the underlying etiology of late-onset Alzheimer’s disease. The etiology of late-onset Alzheimer’s disease is not based on mutations related to amyloid-β (Aβ) or tau production which are currently the basis of in vivo models of Alzheimer’s disease. It has recently been suggested that mechanisms like chronic neuroinflammation may occur prior to amyloid-β and tau pathologies in late-onset Alzheimer’s disease. The aim of this study is to analyze the characteristics of rodent models of neuroinflammation in late-onset Alzheimer’s disease. Our search criteria were based on characteristics of an idealistic disease model that should recapitulate causes, symptoms, and lesions in a chronological order similar to the actual disease. Therefore, a model based on the inflammation hypothesis of late-onset Alzheimer’s disease should include the following features: (i) primary chronic neuroinflammation, (ii) manifestations of memory and cognitive impairment, and (iii) late development of tau and Aβ pathologies. The following models fit the pre-defined criteria: lipopolysaccharide- and PolyI:C-induced models of immune challenge; streptozotocin-, okadaic acid-, and colchicine neurotoxin-induced neuroinflammation models, as well as interleukin-1β, anti-nerve growth factor and p25 transgenic models. Among these models, streptozotocin, PolyI:C-induced, and p25 neuroinflammation models are compatible with the inflammation hypothesis of Alzheimer’s disease.

The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.default mode network | resting state networks | PET | principal component analysis | neurodegeneration T he persistence of local brain function in the absence of focused cognitive activity has attracted much interest over the past decade (1-3). Functional MRI (fMRI) is the most commonly used method to identify resting-state functional brain networks (RSNs), particularly the default mode network (DMN). Because of the spatiotemporal complexity of resting-state fMRI recordings, the extraction of stable RSN topographies using this technique has had to rely on processing algorithms, such as independent component analysis (ICA), to isolate discrete sources of signal in the data. Although this approach has delineated consistent patterns of resting activity in healthy populations (4-6), few validated methods exist to quantify and compare the expression of specific RSNs in individual subjects. Such measurements are particularly relevant in the study of progressive neurodegenerative disorders, in which stereotyped abnormalities develop selectively over time in one or another neural system (7). Indeed, associations between new network topographies and previously reported RSNs, particularly the DMN, have often been descriptive (8-10). In this vein, seed-based functional connectivity measurements have been used to delineate areas correlating with activity profiles in a specific nodal region. Regions identified by this method, however, may not exhibit t...

Objective: To determine whether cognitive impairment in Parkinson disease (PD) and Alzheimer disease (AD) derives from the same network pathology.Methods: We analyzed 18 F-fluorodeoxyglucose PET scans from 40 patients with AD and 40 agematched healthy controls from the Alzheimer's Disease Neuroimaging Initiative and scanned an additional 10 patients with AD and 10 healthy controls at The Feinstein Institute for Medical Research to derive an AD-related metabolic pattern (ADRP) analogous to our previously established PD cognition-related pattern (PDCP) and PD motor-related pattern (PDRP). We computed individual subject expression values for ADRP and PDCP in 89 patients with PD and correlated summary scores for cognitive functioning with network expression. We also evaluated changes in ADRP and PDCP expression in a separate group of 15 patients with PD scanned serially over a 4-year period.Results: Analysis revealed a significant AD-related metabolic topography characterized by covarying metabolic reductions in the hippocampus, parahippocampal gyrus, and parietal and temporal association regions. Expression of ADRP, but not PDCP, was elevated in both AD groups and correlated with worse cognitive summary scores. Patients with PD showed slight ADRP expression, due to topographic overlap with the network underlying PD motor-related pattern degeneration, but only their PDCP expression values increased as cognitive function and executive performance declined. Longitudinal data in PD disclosed an analogous dissociation of network expression. Conclusions:Cognitive dysfunction in PD is associated with a specific brain network that is largely spatially and functionally distinct from that seen in relation to AD. Cognitive impairment is commonly observed in patients with Parkinson disease (PD), even early in the clinical course, 1,2 but its cause remains unclear. The postmortem observation of amyloid-b plaques and tau neurofibrilliary tangles, pathologic hallmarks of Alzheimer disease (AD), in individuals with PD and dementia has led to the hypothesis that the cognitive changes in PD are caused by comorbid AD.3-5 Many patients with PD have substantial cognitive loss without forming plaques and tangles, however, and the severity of neuropsychological deficits in patients with PD with coexisting cortical Lewy body and AD-like pathology correlates only with From the Center for Neurosciences (P.J.M., M.N., W