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To smooth or not to smooth? ROC analysis of perfusion fMRI data
2005
Magnetic Resonance Imaging
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Consequently, different strategies are expected to be employed in the statistical analysis of functional magnetic resonance imaging (fMRI) data based on perfusion contrast. ...
In this study, the effect of different analysis methods upon signal detection efficacy, as assessed by receiver operator characteristic (ROC) measures, was examined for perfusion fMRI data. ...
Temporal smoothing, particularly a bmatched filter,Q is therefore beneficial for detecting small signal out of a white-noise background based on prior knowledge of the target signal [21] . ...
doi:10.1016/j.mri.2004.11.009
pmid:15733791
fatcat:aod6hrvobvdkvbpdrhktk5yb5y
Graph convolutional network for fMRI analysis based on connectivity neighborhood
2020
Network Neuroscience
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Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. ...
Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications. ...
A major contribution of the present work is that rather than performing convolution on structured image grids, ROI-based fMRI data are considered graphs based on FC, and cGCN is carried out between connectomic ...
doi:10.1162/netn_a_00171
pmid:33688607
pmcid:PMC7935029
fatcat:r6xwhefj2zbnpdmcgffyzjf2am
Deep Multimodal Learning for the Diagnosis of Autism Spectrum Disorder
2020
Journal of Imaging
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data. ...
In this paper, we propose a deep multimodal model that learns a joint representation from two types of connectomic data offered by fMRI scans. ...
co-activation between the signals in the two ROI based on the time series data. ...
doi:10.3390/jimaging6060047
pmid:34460593
pmcid:PMC8321065
fatcat:rrlt4vsikbcxtcaxuyzjc2abza
Paradigm-free mapping with morphological component analysis: getting most out of fMRI data
2011
Wavelets and Sparsity XIV
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This work proposes a novel method for fMRI data analysis that enables the decomposition of the fMRI signal in its sources based on morphological descriptors. ...
Functional magnetic resonance imaging (fMRI) is a non-invasive imaging technique that maps the brain's response to neuronal activity based on the blood oxygenation level dependent (BOLD) effect. ...
Note that traditional model-based techniques for fMRI data analysis test for the presence of a given haemodynamic regressor, which is modeled as the convolution of the HRF with a stimulus signal based ...
doi:10.1117/12.893920
fatcat:pkqdod3trrc5zf4qk4dd5n7gxi
Seizure Prediction Based on Convolution Neural Network with Subnet Loss Function
2022
International Journal of Intelligent Engineering and Systems
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Various existing models were applied for the prediction of seizures based on feature extraction and deep learning techniques and it also has the limitations of lower efficiency in feature analysis and ...
Electroencephalogram (EEG) signals of patients are applying for the classification and prediction of seizures. Accurate prediction of seizures helps the doctor to decide on the patients' treatment. ...
After pre-processing of EEG signal data, spectrum images of time-frequency sets are generated. ...
doi:10.22266/ijies2022.0430.13
fatcat:qhg3htwqdngsvowm2wpcyqeoxu
Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network
2021
Sensors
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The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance ...
Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. ...
Results generated from this work are using dataset from the autism brain imaging data exchange (ABIDE) [24] which is an online data source for rs-fMRI data of ASD patients and normal control (NC) groups ...
doi:10.3390/s21165256
pmid:34450699
pmcid:PMC8398492
fatcat:eh3ox5qrkbfrhmvlknxyinn5bq
Retrieving the Hemodynamic Response Function in resting state fMRI: Methodology and application
2015
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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In this paper we present a procedure to retrieve the hemodynamic response function (HRF) from resting state functional magnetic resonance imaging (fMRI) data. ...
The typical HRF shape at rest for a group of healthy subject is presented. ...
INTRODUCTION Functional magnetic resonance imaging (fMRI) time series can be modeled as a convolution of a latent neural signal (which is not measured) and the hemodynamic response function (HRF). ...
doi:10.1109/embc.2015.7319771
pmid:26737671
dblp:conf/embc/WuDLM15
fatcat:qxpru5qw5jbwvnbdptq2mxgyrq
fMRI Brain Image Retrieval Based on ICA Components
2007
Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007)
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This manuscript proposes a retrieval system for fMRI brain images. ...
Along with other heuristics, this property can be for fMRI image retrieval and classification. ...
Conclusion We proposed a new retrieval algorithm for fMRI images, based on independent component analysis and a new similarity measure. ...
doi:10.1109/enc.2007.4351419
fatcat:4ytdyxgrn5g4lgbe4u7kcm6jee
fMRI Brain Image Retrieval Based on ICA Components
2007
Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007)
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This manuscript proposes a retrieval system for fMRI brain images. ...
Along with other heuristics, this property can be for fMRI image retrieval and classification. ...
Conclusion We proposed a new retrieval algorithm for fMRI images, based on independent component analysis and a new similarity measure. ...
doi:10.1109/enc.2007.32
dblp:conf/enc/BaiKSS07
fatcat:33hcdz4kbndp3gxa5o456x6j24
Multimodal MRI-based classification of migraine: using deep learning convolutional neural network
2018
BioMedical Engineering OnLine
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on rs-fMRI data to distinguish not only between migraineurs and healthy controls, but also between the two subtypes of migraine. ...
Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. ...
with the fMRI features as a method for improving the discriminative power for migraine. ...
doi:10.1186/s12938-018-0587-0
pmid:30314437
pmcid:PMC6186044
fatcat:kceeaftcznagjp5n6pd4mkpnue
Removing the Effect of Hemodynamic Response Function in Joint Factorization of EEG and fMRI Datasets
2019
Frontiers in biomedical technologies
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Convolution of EEG signals with hemodynamic response function is one of the most important methods to consider the effect of HRF in the fusion of EEG and fMRI data. ...
Materials and Methods: In this paper, we have proposed a new method based on Advanced Coupled Matrix Tensor Factorization model to jointly factorize the EEG tensor and fMRI matrix while we simultaneously ...
Figure 1 . 1 Applying ACMTF model for EEG-fMRI data fusion when the temporal signature is considered as the common factor Based on this model, fMRI data is the matrix multiplication of HRF convolutional ...
doi:10.18502/fbt.v6i2.1686
fatcat:mlcsr26qkvhi5cyb5j36e7qlvi
Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
2008
IEEE Reviews in Biomedical Engineering
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Combining fMRI and EEG/MEG data allows us to study brain function from different perspectives. ...
in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording. ...
Furthermore, we can re-fit the source estimates to the EEG/MEG data while taking the fMRI-EEG/MEG co-registered source power estimates as time-variant prior spatial constraints. ...
doi:10.1109/rbme.2008.2008233
pmid:20634915
pmcid:PMC2903760
fatcat:6hogffiwvrbonmxn2swkyqstde
Ground-truth resting-state signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics
[article]
2020
arXiv
pre-print
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To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. ...
We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8-19% of total voxels. ...
Acknowledgements The research described in this paper was funded by the National Science Foundation (STTR Phase 1 The data used in the current manuscript was acquired on 05/19/2019. ...
arXiv:2004.06760v3
fatcat:djqg3prljvgfhgiuqg7m2rdmty
DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network
2021
Frontiers in Neuroinformatics
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There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. ...
The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. ...
based on fMRI images. ...
doi:10.3389/fninf.2021.635657
pmid:34248531
pmcid:PMC8265393
fatcat:fwowvazh3fbsza6qs2x4smfee4
Removal of phase artifacts from fMRI data using a Stockwell transform filter improves brain activity detection
2003
Magnetic Resonance in Medicine
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A novel and automated technique is described for removing fMRI image artifacts resulting from motion outside of the imaging field of view. ...
Using this technique, 1D Fourier transforms (FTs) are performed on raw image data to obtain phase profiles. ...
Louis Lauzon for technical assistance and helpful discussions. ...
doi:10.1002/mrm.10681
pmid:14705040
fatcat:zqctgf77pzf5pgdinops4ekvuq
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