Objectives: Generalization (or near-transfer) effects of an intervention to tasks not explicitly trained are the most desirable intervention outcomes. However, they are rarely reported and even more rarely explained. One hypothesis for generalization effects is that the tasks improved share the same brain function/computation with the intervention task. We tested this hypothesis in this study of transcranial direct current stimulation (tDCS) over the left inferior frontal gyrus (IFG) that is claimed to be involved in selective semantic retrieval of information from the temporal lobes.
Materials And Methods: In this study, we examined whether tDCS over the left IFG in a group of patients with primary progressive aphasia (PPA), paired with a lexical/semantic retrieval intervention (oral and written naming), may specifically improve semantic fluency, a nontrained near-transfer task that relies on selective semantic retrieval, in patients with PPA.
Results: Semantic fluency improved significantly more in the active tDCS than in the sham tDCS condition immediately after and two weeks after treatment. This improvement was marginally significant two months after treatment. We also found that the active tDCS effect was specific to tasks that require this IFG computation (selective semantic retrieval) but not to other tasks that may require different computations of the frontal lobes.
Conclusions: We provided interventional evidence that the left IFG is critical for selective semantic retrieval, and tDCS over the left IFG may have a near-transfer effect on tasks that depend on the same computation, even if they are not specifically trained.
Clinical Trial Registration: The Clinicaltrials.gov registration number for the study is NCT02606422.
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http://dx.doi.org/10.1016/j.neurom.2022.09.004 | DOI Listing |
Acad Radiol
December 2024
School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.); Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.). Electronic address:
Rationale And Objectives: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
Materials And Methods: A total of 724 pathologically confirmed early invasive lung adenocarcinoma patients were retrospectively included from two centers. Clinical and CT semantic features of the patients were collected, and 3D radiomics features were extracted from nonenhanced CT images.
Eur Radiol
December 2024
Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Sci Data
December 2024
Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, 1105, AZ, The Netherlands.
Faced with heterogeneity of healthcare data, we propose a novel approach for harmonizing data elements (i.e., attributes) across health data standards.
View Article and Find Full Text PDFSci Rep
December 2024
Research Center, Future University in Egypt, New Cairo, 11835, Egypt.
Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and can save lives. BT segmentation from Magnetic Resonance Images (MRI) is considered a big challenge owing to the difficulty of BT tissues, and segmenting them from the healthier tissue is challenging when manual segmentation is done through radiologists.
View Article and Find Full Text PDFSci Prog
January 2024
Department of Railroad Electrical and Electronic Engineering, Korea National University of Transportation, Uiwang-si, Korea.
In recent years, the application of pretrained models in specialized domains has become increasingly important. Traditionally, adapting these models involved fine-tuning their parameters and structures through retraining. However, these fine-tuning methods can be inefficient, particularly when addressing data from specific domains or when modifications are needed in the lower layers of large-scale pretrained models.
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