Mirror neurons (MNs) are considered to be the supporting neural mechanism for action understanding. MNs have been identified in monkey's area F5. The identification of MNs in the human homolog of monkeys' area F5 Broadmann Area 44/45 (BA 44/45) has been proven methodologically difficult. Cross-modal functional MRI (fMRI) adaptation studies supporting the existence of MNs restricted their analysis to a priori candidate regions, whereas studies that failed to find evidence used non-object-directed (NDA) actions. We tackled these limitations by using object-directed actions (ODAs) differing only in terms of their object directedness in combination with a cross-modal adaptation paradigm and a whole-brain analysis. Additionally, we tested voxels' blood oxygenation level-dependent (BOLD) response patterns for several properties previously reported as typical MN response properties. Our results revealed 52 voxels in left inferior frontal gyrus (IFG; particularly BA 44/45), which respond to both motor and visual stimulation and exhibit cross-modal adaptation between the execution and observation of the same action. These results demonstrate that part of human IFG, specifically BA 44/45, has BOLD response characteristics very similar to monkey's area F5.
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http://dx.doi.org/10.3389/fnhum.2016.00078 | DOI Listing |
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.
As the leading cause of dementia worldwide, Alzheimer's Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18).
View Article and Find Full Text PDFWorld J Gastrointest Endosc
January 2025
Department of Gastroenterology, Affiliated Jinhua Hospital of Wenzhou Medical University, Jinhua People's Hospital, Jinhua 321000, Zhejiang Province, China.
Background: Bouveret's syndrome is a rare (1%-4%) form of cholelithiasis characterized by gastric outlet obstruction. It presents mainly in elderly women with nausea, vomiting, and abdominal pain. On physical examination, common findings include dehydration signs such as tachycardia, decreased urine output, abdominal discomfort, and distention.
View Article and Find Full Text PDFImaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
View Article and Find Full Text PDFAcad Radiol
January 2025
Division of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany (F.B., M.G., H.P.S., S.D.); Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120 Heidelberg, Germany (T.F.W., M.W.).
Rationale And Objectives: To establish an advanced automated bone marrow (BM) segmentation model on whole-body (WB-)MRI in monoclonal plasma cell disorders (MPCD), and to demonstrate its robust performance on multicenter datasets with severe myeloma-related pathologies.
Materials And Methods: The study cohort comprised multi-vendor, multi-protocol imaging data acquired with varying field strength across 8 different centers. In total, 210 WB-MRIs of 207 MPCD patients were included.
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