Dataset Information

Neural specificity predicts fluid processing ability in older adults.

ABSTRACT: We investigated whether individual differences in neural specificity-the distinctiveness of different neural representations-could explain individual differences in cognitive performance in older adults. Neural specificity was estimated based on how accurately multivariate pattern analysis identified neural activation patterns associated with specific experimental conditions. Neural specificity calculated from a same/different task on two categories of visual stimuli (faces and houses) significantly predicted performance on a range of fluid processing behavioral tasks (dot-comparison, digit-symbol, Trails-A, Trails-B, verbal-fluency) in older adults, whereas it did not correlate with a measure of crystallized knowledge (Shipley-vocabulary). In addition, the neural specificity measure accounted for 30% of the variance in a composite measure of fluid processing ability. These results are consistent with the hypothesis that loss of neural specificity, or dedifferentiation, contributes to reduced fluid processing ability in old age.

SUBMITTER: Park J

PROVIDER: S-EPMC2913723 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Neural specificity predicts fluid processing ability in older adults.

Park Joonkoo J Carp Joshua J Hebrank Andrew A Park Denise C DC Polk Thad A TA

The Journal of neuroscience : the official journal of the Society for Neuroscience 20100701 27

We investigated whether individual differences in neural specificity-the distinctiveness of different neural representations-could explain individual differences in cognitive performance in older adults. Neural specificity was estimated based on how accurately multivariate pattern analysis identified neural activation patterns associated with specific experimental conditions. Neural specificity calculated from a same/different task on two categories of visual stimuli (faces and houses) significa ...[more]

PMID: 20610760

Similar Datasets

Project description:Speech perception in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of cortical speech-evoked responses that distinguish older adults with or without mild hearing loss. We performed source analyses to estimate cortical surface signals from the EEG recordings during a phoneme discrimination task conducted under clear and noise-degraded conditions. We computed source-level ERPs (i.e., mean activation within each ROI) from each of the 68 ROIs of the Desikan-Killiany (DK) atlas, averaged over a randomly chosen 100 trials without replacement to form feature vectors. We adopted a multivariate feature selection method called stability selection and control to choose features that are consistent over a range of model parameters. We use parameter optimized support vector machine (SVM) as a classifiers to investigate the time course and brain regions that segregate groups and speech clarity. For clear speech perception, whole-brain data revealed a classification accuracy of 81.50% [area under the curve (AUC) 80.73%; F1-score 82.00%], distinguishing groups within ?60 ms after speech onset (i.e., as early as the P1 wave). We observed lower accuracy of 78.12% [AUC 77.64%; F1-score 78.00%] and delayed classification performance when speech was embedded in noise, with group segregation at 80 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured in the RH. Moreover, stability selection analysis identified 12 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 16 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (78.7% accuracy). Our results identify critical time-courses and brain regions that distinguish mild hearing loss from normal hearing in older adults and confirm a larger number of active areas, particularly in RH, when processing noise-degraded speech information.

Dataset Information

Neural specificity predicts fluid processing ability in older adults.

Publications

Neural specificity predicts fluid processing ability in older adults.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets