Details

Autor(en) / Beteiligte

• Krajcovicova, Lenka
• Barton, Marek
• Elfmarkova-Nemcova, Nela
• Mikl, Michal
• Marecek, Radek
• Rektorova, Irena

Titel

Changes in connectivity of the posterior default network node during visual processing in mild cognitive impairment: staged decline between normal aging and Alzheimer’s disease

Ist Teil von

• Journal of Neural Transmission, 2017-12, Vol.124 (12), p.1607-1619

Ort / Verlag

Vienna: Springer Vienna

Erscheinungsjahr

2017

Quelle

MEDLINE

Beschreibungen/Notizen

• Visual processing difficulties are often present in Alzheimer’s disease (AD), even in its pre-dementia phase (i.e. in mild cognitive impairment, MCI). The default mode network (DMN) modulates the brain connectivity depending on the specific cognitive demand, including visual processes. The aim of the present study was to analyze specific changes in connectivity of the posterior DMN node (i.e. the posterior cingulate cortex and precuneus, PCC/P) associated with visual processing in 17 MCI patients and 15 AD patients as compared to 18 healthy controls (HC) using functional magnetic resonance imaging. We used psychophysiological interaction (PPI) analysis to detect specific alterations in PCC connectivity associated with visual processing while controlling for brain atrophy. In the HC group, we observed physiological changes in PCC connectivity in ventral visual stream areas and with PCC/P during the visual task, reflecting the successful involvement of these regions in visual processing. In the MCI group, the PCC connectivity changes were disturbed and remained significant only with the anterior precuneus. In between-group comparison, we observed significant PPI effects in the right superior temporal gyrus in both MCI and AD as compared to HC. This change in connectivity may reflect ineffective “compensatory” mechanism present in the early pre-dementia stages of AD or abnormal modulation of brain connectivity due to the disease pathology. With the disease progression, these changes become more evident but less efficient in terms of compensation. This approach can separate the MCI from HC with 77% sensitivity and 89% specificity.