{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T14:00:38Z","timestamp":1790517638743,"version":"4.1.0"},"reference-count":83,"publisher":"Society for Neuroscience","issue":"1","license":[{"start":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T00:00:00Z","timestamp":1562025600000},"content-version":"vor","delay-in-days":242,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Neurosci."],"published-print":{"date-parts":[[2019,1,2]]},"abstract":"<jats:p>Healthy aging is associated with decreased neural selectivity (dedifferentiation) in category-selective cortical regions. This finding has prompted the suggestion that dedifferentiation contributes to age-related cognitive decline. Consistent with this possibility, dedifferentiation has been reported to negatively correlate with fluid intelligence in older adults. Here, we examined whether dedifferentiation is associated with performance in another cognitive domain\u2014episodic memory\u2014that is also highly vulnerable to aging. Given the proposed role of dedifferentiation in age-related cognitive decline, we predicted there would be a stronger link between dedifferentiation and episodic memory performance in older than in younger adults. Young (18\u201330 years) and older (64\u201375 years) male and female humans underwent fMRI scanning while viewing images of objects and scenes before a subsequent recognition memory test. We computed a differentiation index in two regions of interest (ROIs): parahippocampal place area (PPA) and lateral occipital complex (LOC). This index quantified the selectivity of the BOLD response to preferred versus nonpreferred category of an ROI (scenes for PPA, objects for LOC). The differentiation index in the PPA, but not the LOC, was lower in older than in younger adults. Additionally, the PPA differentiation index predicted recognition memory performance for the studied items. This relationship was independent of and not moderated by age. The PPA differentiation index also predicted performance on a latent \u201cfluency\u201d factor derived from a neuropsychological test battery; this relationship was also age invariant. These findings suggest that two independent factors, one associated with age, and the other with cognitive performance, influence neural differentiation.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>SIGNIFICANCE STATEMENT<\/jats:bold>\n                    Aging is associated with neural dedifferentiation\u2014reduced neural selectivity in \u201ccategory-selective\u201d cortical brain regions\u2014which has been proposed to contribute to cognitive aging. Here, we examined whether neural differentiation is predictive of episodic memory performance, and whether the relationship is moderated by age. A neural differentiation index was estimated for scene-selective (PPA) and object-selective (LOC) cortical regions while participants studied images for a subsequent memory test. Age-related reductions were observed for the PPA, but not for the LOC, differentiation index. Importantly, the PPA differentiation index demonstrated age-invariant correlations with subsequent memory performance and a fluency factor derived from a neuropsychological battery. Together, these findings suggest that neural differentiation is associated with two independent factors: age and cognitive performance.\n                  <\/jats:p>","DOI":"10.1523\/jneurosci.1498-18.2018","type":"journal-article","created":{"date-parts":[[2018,11,2]],"date-time":"2018-11-02T16:25:12Z","timestamp":1541175912000},"page":"149-162","source":"Crossref","is-referenced-by-count":132,"title":["The Relationship between Age, Neural Differentiation, and Memory Performance"],"prefix":"10.1523","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7286-5084","authenticated-orcid":false,"given":"Joshua D.","family":"Koen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nedra","family":"Hauck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0397-5749","authenticated-orcid":false,"given":"Michael D.","family":"Rugg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2018,11,2]]},"reference":[{"key":"2023041803341912000_39.1.149.1","first-page":"2940","article-title":"Principal components analysis","volume-title":"Encyclopedia of ecology","volume":"Vol 2","author":"Abdi","year":"2008"},{"key":"2023041803341912000_39.1.149.2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2010.00207"},{"key":"2023041803341912000_39.1.149.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.visres.2013.05.004"},{"key":"2023041803341912000_39.1.149.4","doi-asserted-by":"publisher","DOI":"10.1037\/0882-7974.12.1.12"},{"key":"2023041803341912000_39.1.149.5","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2015.0355"},{"key":"2023041803341912000_39.1.149.6","doi-asserted-by":"publisher","DOI":"10.1016\/0028-3932(68)90038-9"},{"key":"2023041803341912000_39.1.149.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2017.12.030"},{"key":"2023041803341912000_39.1.149.8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0029411"},{"key":"2023041803341912000_39.1.149.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.04.267"},{"key":"2023041803341912000_39.1.149.10","doi-asserted-by":"publisher","DOI":"10.1162\/jocn.2006.18.4.495"},{"key":"2023041803341912000_39.1.149.11","unstructured":"Cohen J (1988) Statistical power analysis for the social sciences, Ed 2. 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