| bibtype |
J -
Journal Article
|
| ARLID |
0638625 |
| utime |
20251006142256.5 |
| mtime |
20250904235959.9 |
| SCOPUS |
105015048097 |
| WOS |
001562541400019 |
| DOI |
10.1126/sciadv.adw6404 |
| title
(primary) (eng) |
Path integration impairments reveal early cognitive changes in subjective cognitive decline |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0480043 |
| ISSN |
2375-2548 |
| title
|
Science Advances |
| volume_id |
11 |
| publisher |
| name |
American Association for the Advancement of Science |
|
|
| keyword |
Subjective cognitive decline |
| keyword |
Alzheimer’s disease |
| keyword |
Bayesian computational model |
| author
(primary) |
| ARLID |
cav_un_auth*0494631 |
| name1 |
Segen |
| name2 |
V. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0494632 |
| name1 |
Kabir |
| name2 |
M. R. |
| country |
US |
|
| author
|
| ARLID |
cav_un_auth*0494633 |
| name1 |
Streck |
| name2 |
A. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0370372 |
| name1 |
Slavík |
| name2 |
Jakub |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0494636 |
| name1 |
Glanz |
| name2 |
W. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0494637 |
| name1 |
Butryn |
| name2 |
M. |
| country |
DE |
|
| author
|
| ARLID |
cav_un_auth*0494638 |
| name1 |
Newman |
| name2 |
E. |
| country |
US |
|
| author
|
| ARLID |
cav_un_auth*0494639 |
| name1 |
Tiganj |
| name2 |
Z. |
| country |
US |
|
| author
|
| ARLID |
cav_un_auth*0494640 |
| name1 |
Wolbers |
| name2 |
T. |
| country |
DE |
|
| source |
|
| cas_special |
| abstract
(eng) |
Path integration, the ability to track one’s position using self-motion cues, is critically dependent on the grid cell network in the entorhinal cortex, a region vulnerable to early Alzheimer’s disease pathology. In this study, we examined path integration performance in individuals with subjective cognitive decline (SCD), a group at increased risk for Alzheimer’s disease, and healthy controls using an immersive virtual reality task. We developed a Bayesian computational model to decompose path integration errors into distinct components. SCD participants exhibited significantly higher path integration error, primarily driven by increased memory leak, while other modeling-derived error sources, such as velocity gain, sensory, and reporting noise, remained comparable across groups. Our findings suggest that path integration deficits, specifically memory leak, may serve as an early marker of neurodegeneration in SCD and highlight the potential of self-motion–based navigation tasks for detecting presymptomatic Alzheimer’s disease–related cognitive changes. |
| result_subspec |
WOS |
| RIV |
FH |
| FORD0 |
30000 |
| FORD1 |
30100 |
| FORD2 |
30103 |
| reportyear |
2026 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0370054 |
| confidential |
S |
| article_num |
eadw6404 |
| mrcbC91 |
C |
| mrcbT16-e |
MULTIDISCIPLINARYSCIENCES |
| mrcbT16-f |
14.1 |
| mrcbT16-g |
2.4 |
| mrcbT16-h |
4.1 |
| mrcbT16-i |
0.36789 |
| mrcbT16-j |
4.859 |
| mrcbT16-k |
181324 |
| mrcbT16-q |
288 |
| mrcbT16-s |
4.324 |
| mrcbT16-y |
69.4 |
| mrcbT16-x |
12.06 |
| mrcbT16-3 |
80733 |
| mrcbT16-4 |
Q1 |
| mrcbT16-5 |
12.300 |
| mrcbT16-6 |
2263 |
| mrcbT16-7 |
Q1 |
| mrcbT16-C |
92 |
| mrcbT16-M |
2.82 |
| mrcbT16-N |
Q1 |
| mrcbT16-P |
91.5 |
| arlyear |
2025 |
| mrcbU14 |
105015048097 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001562541400019 WOS |
| mrcbU63 |
cav_un_epca*0480043 Science Advances Roč. 11 č. 36 2025 2375-2548 2375-2548 American Association for the Advancement of Science |
|