Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease.

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

CSF biomarkers for the differential diagnosis of Alzheimer's disease: A large-scale international multicenter study.

INTRODUCTION: The aim of this study was to test the diagnostic value of cerebrospinal fluid (CSF) beta-amyloid (Aß1-42), phosphorylated tau, and total tau (tau) to discriminate Alzheimer's disease (AD) dementia from other forms of dementia. METHODS: A total of 675 CSF samples collected at eight memory clinics were obtained from healthy controls, AD dementia, subjective memory impairment, mild cognitive impairment, vascular dementia, Lewy body dementia (LBD), fronto-temporal dementia (FTD), depression, or other neurological diseases. RESULTS: CSF Aß1-42 showed the best diagnostic accuracy among the CSF biomarkers. At a sensitivity of 85%, the specificity to differentiate AD dementia against other diagnoses ranged from 42% (for LBD, 95% confidence interval or CI = 32-62) to 77% (for FTD, 95% CI = 62-90). DISCUSSION: CSF Aß1-42 discriminates AD dementia from FTD, but shows significant overlap with other non-AD forms of dementia, possibly reflecting the underlying mixed pathologies.