| First Author | Rechnitz O | Year | 2021 |
| Journal | Curr Biol | Volume | 31 |
| Issue | 15 | Pages | 3292-3302.e6 |
| PubMed ID | 34146487 | Mgi Jnum | J:328622 |
| Mgi Id | MGI:6741164 | Doi | 10.1016/j.cub.2021.05.039 |
| Citation | Rechnitz O, et al. (2021) Hippocampal sub-networks exhibit distinct spatial representation deficits in Alzheimer's disease model mice. Curr Biol 31(15):3292-3302.e6 |
| abstractText | Not much is known about how the dentate gyrus (DG) and hippocampal CA3 networks, critical for memory and spatial processing, malfunction in Alzheimer's disease (AD). While studies of associative memory deficits in AD have focused mainly on behavior, here, we directly measured neurophysiological network dysfunction. We asked what the pattern of deterioration of different networks is during disease progression. We investigated how the associative memory-processing capabilities in different hippocampal subfields are affected by familial AD (fAD) mutations leading to amyloid-beta dyshomeostasis. Specifically, we focused on the DG and CA3, which are known to be involved in pattern completion and separation and are susceptible to pathological alterations in AD. To identify AD-related deficits in neural-ensemble dynamics, we recorded single-unit activity in wild-type (WT) and fAD model mice (APPSwe+PSEN1/DeltaE9) in a novel tactile morph task, which utilizes the extremely developed somatosensory modality of mice. As expected from the sub-network regional specialization, we found that tactile changes induced lower rate map correlations in the DG than in CA3 of WT mice. This reflects DG pattern separation and CA3 pattern completion. In contrast, in fAD model mice, we observed pattern separation deficits in the DG and pattern completion deficits in CA3. This demonstration of region-dependent impairments in fAD model mice contributes to understanding of brain networks deterioration during fAD progression. Furthermore, it implies that the deterioration cannot be studied generally throughout the hippocampus but must be researched at a finer resolution of microcircuits. This opens novel systems-level approaches for analyzing AD-related neural network deficits. |
