Results 31 to 40 of about 1,285,227 (187)
IEEE transactions on neural systems and rehabilitation engineering, 2023 To construct a more effective model with good generalization performance for inter-site autism spectrum disorder (ASD) diagnosis, domain adaptation based ASD diagnostic models are proposed to alleviate the inter-site heterogeneity. However, most existing
Xingdan Liu +5 more
semanticscholar +1 more source
Modeling Adaptation Effects in fMRI Analysis [PDF]
, 2009 The standard general linear model (GLM) for rapid event-related fMRI design protocols typically ignores reduction in hemodynamic responses in successive stimuli in a train due to incomplete recovery from the preceding stimuli. To capture this adaptation effect, we incorporate a region-specific adaptation model into GLM. The model quantifies the rate of
Ou, Wanmei +4 more
openaire +4 more sources
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results [PDF]
Medical Image Anal., 2020 Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time
Xiaoxiao Li +5 more
semanticscholar +1 more source
Functional magnetic resonance imaging of cortical changes in a low-grade glioma patient [PDF]
Vojnosanitetski Pregled, 2010 Introduction. New methods for studying brain functions have provided the new insights into human brain. It is really possible to study a cortical adaptation in adults who have sustained injury.
Šveljo Olivera +3 more
doaj +1 more source
Adaptive analysis of fMRI data
NeuroImage, 2003 This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method.
Ola, Friman +3 more
openaire +2 more sources
Adaptive smoothing in fMRI data processing neural networks [PDF]
2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2017 Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation.
Vilamala, Albert +2 more
openaire +2 more sources
Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling [PDF]
, 2021 Accepted at International Workshop on Machine Learning in Clinical Neuroimaging (MLCN2021)
el-Gazzar, Ahmed +2 more
openaire +3 more sources
Evaluating the capabilities and challenges of layer-fMRI VASO at 3T
Aperture Neuro, 2023 Sub-millimeter functional imaging has the potential to capture cortical layer–specific functional information flow within and across brain systems. Recent sequence advancements of fMRI signal readout and contrast generations resulted in wide adaptation ...
Laurentius Huber +7 more
doaj +1 more source
Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study
Frontiers in Neuroinformatics, 2022 The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis.
Rohan Panda +5 more
doaj +1 more source
fMRI Repetition Suppression: Neuronal Adaptation or Stimulus Expectation? [PDF]
Cerebral Cortex, 2011 Measurements of repetition suppression with functional magnetic resonance imaging (fMRI adaptation) have been used widely to probe neuronal population response properties in human cerebral cortex. fMRI adaptation techniques assume that fMRI repetition suppression reflects neuronal adaptation, an assumption that has been challenged on the basis of ...
Jonas, Larsson, Andrew T, Smith
openaire +2 more sources