Previous studies underline the role of dopamine in cognitive reinforcement learning. This has been demonstrated by a striatal involvement in feedback-based probabilistic classification learning. In order to determine to which extent the dopaminergic loss of Parkinson's disease and aging affects the feedback aspect in classification learning, we applied two versions of the same visual classification task. One version had to be learnt by trial-by-trial feedback, the other by observing the correct assignment of stimulus and category. Performance was evaluated in test blocks that were identical under the feedback and the observational conditions. There were 31 patients with Parkinson's disease (PD), 30 older controls and 20 younger controls tested. The results show that younger healthy participants perform better than older participants in the classification task and this difference significantly interacts with the learning condition: both groups show nearly the same level of performance under the observational condition but younger participants show a better performance than older ones under the feedback condition. In contrast, PD patients and older controls did not differ in their performance in the classification task; both groups performed better under the observational than under the feedback condition. These results demonstrate that healthy aging affects feedback-based learning but does not affect learning by observation. The fact that PD patients showed no additional deficit in feedback-based learning is an indication that the loss of dopamine does not play the key role under the feedback condition of our classification task. This finding questions the general role of the striatum in feedback-based learning and demonstrates that healthy aging particularly affects feedback-based learning.
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http://dx.doi.org/10.1016/j.brainres.2007.01.042 | DOI Listing |
JMIR Med Inform
January 2025
School of Software, Taiyuan University of Technology, Jingzhong, China.
Background: The prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive screening tests, prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary.
View Article and Find Full Text PDFBiological knowledgebases are essential resources for biomedical researchers, providing ready access to gene function and genomic data. Professional, manual curation of knowledgebases, however, is labor-intensive and thus high-performing machine learning methods that improve biocuration efficiency are needed. Here we report on sentence-level classification to identify biocuration-relevant sentences in the full text of published references for two gene function data types: gene expression and protein kinase activity.
View Article and Find Full Text PDFNeural Netw
January 2025
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 138632, Singapore.
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only task-specific layers of pre-trained models, overlooking the variability in input medical images. As a result, these approaches may lack efficiency or effectiveness.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Information Science, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China. Electronic address:
Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be addressed. To this end, we propose a multi-center data correction method, which employs a domain adaptation correction strategy with Wasserstein distance of optimal transport, along with maximum mean discrepancy to improve the generalizability of brain-age prediction models on small-sample datasets.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address:
For imbalanced classification problem, algorithm-level methods can effectively avoid the information loss and noise introduction of data-level methods. However, the differences in the characteristics of the datasets, such as imbalance ratio, data dimension, and sample distribution, make it difficult to determine the optimal parameters of the algorithm-level methods, which leads to low universality. This paper proposes a meta-learning imbalanced classification framework via boundary enhancement strategy with Bayes imbalance impact index.
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