Background: Finger-tapping has been widely studied using behavioral and neuroimaging paradigms. Evidence supports the use of finger-tapping as an endophenotype in schizophrenia, but its relationship with motor procedural learning remains unexplored. To our knowledge, this study presents the first use of index finger-tapping to study procedural learning in individuals with schizophrenia or schizoaffective disorder (SCZ/SZA) as compared to healthy controls.
Methods: A computerized index finger-tapping test was administered to 1169 SCZ/SZA patients (62% male, 88% right-handed), and 689 healthy controls (40% male, 93% right-handed). Number of taps per trial and learning slopes across trials for the dominant and non-dominant hands were examined for motor speed and procedural learning, respectively.
Results: Both healthy controls and SCZ/SZA patients demonstrated procedural learning for their dominant hand but not for their non-dominant hand. In addition, patients showed a greater capacity for procedural learning even though they demonstrated more variability in procedural learning compared to healthy controls. Left-handers of both groups performed better than right-handers and had less variability in mean number of taps between non-dominant and dominant hands. Males also had less variability in mean tap count between dominant and non-dominant hands than females. As expected, patients had a lower mean number of taps than healthy controls, males outperformed females and dominant-hand trials had more mean taps than non-dominant hand trials in both groups.
Conclusions: The index finger-tapping test can measure both motor speed and procedural learning, and motor procedural learning may be intact in SCZ/SZA patients.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351528 | PMC |
| http://dx.doi.org/10.1016/j.schres.2012.01.018 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.
Orthod Craniofac Res
January 2025
UFR Odontologie, Université Paris Cité, Paris, France.
Objective: To assess the accuracy of three commercially available and one open-source deep learning (DL) solutions for automatic tooth segmentation in cone beam computed tomography (CBCT) images of patients with multiple dental impactions.
Materials And Methods: Twenty patients (20 CBCT scans) were selected from a retrospective cohort of individuals with multiple dental impactions. For each CBCT scan, one reference segmentation and four DL segmentations of the maxillary and mandibular teeth were obtained.
Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.
Stroke
January 2025
Center for Brain Recovery, Boston University, MA (M.J.M., E.C., M.S., M.R.-M., S.K.).
Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.
Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.
Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.
Laryngoscope
January 2025
Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Objective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.
View Article and Find Full Text PDFUtilization of artificial intelligence in the diagnosis of pes planus and pes cavus with a smartphone camera.
World J Orthop
December 2024
Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Background: Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.
Aim: To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.
Total hip arthroplasty preoperative planning for childhood hip disorders' sequelae: Focus on developmental dysplasia of the hip.
World J Orthop
December 2024
Department of Trauma and Orthopaedics, AOSP Terni, Terni 05100, Italy.
Developmental dysplasia of the hip (DDH) poses significant challenges in both childhood and adulthood, affecting up to 10 per 1000 live births in the United Kingdom and United States. While newborn screening aims to detect DDH early, missed diagnoses can lead to severe complications such as hip dysplasia and early onset osteoarthritis in adults. Treatment options range from less invasive procedures like hip-preserving surgery to more extensive interventions such as total hip arthroplasty (THA), depending on the severity of the condition.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!