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Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
[article]
2018
arXiv
pre-print
Preserved Fulltext
Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. ...
Medical imaging and, in particular, neuroscience studies often rely on such graph representations, with brain connectivity networks being a characteristic example, while ultimately seeking the locus of ...
This research has been conducted using the UK Biobank Resource under Application Number 12579 and funded by the EPSRC Doctoral Prize Fellowship funding scheme. ...
arXiv:1806.01764v1
fatcat:orqgczzaxzg75b4tup2dca5epq
Metric learning with spectral graph convolutions on brain connectivity networks
2018
NeuroImage
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Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. ...
traditional convolutions to irregular graphs and operates in the graph spectral domain. ...
To the best of our knowledge, this has been 125 the first application of metric learning with spectral graph convolutions on brain connectivity networks. ...
doi:10.1016/j.neuroimage.2017.12.052
pmid:29278772
fatcat:ihthm266gbclpcm3eqq3tu2zmm
A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-stating functional MRI Data
[article]
2020
bioRxiv
pre-print
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Herein we present a novel deep neural network architecture, combining both GNNs and temporal convolutional networks (TCNs), which is able to learn from the spatial and temporal components of rs-fMRI data ...
Recently, graph neural networks (GNNs) have seen a surge in popularity due to their successes in modelling unstructured relational data. ...
Rueckert, Graph saliency maps 577 through spectral convolutional networks: Application to sex classifica-578 tion with brain connectivity, in: Lecture Notes in Computer Science, 579 Springer International ...
doi:10.1101/2020.11.08.370288
fatcat:aogq724mrfholnzpoedqfwy7fu
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be ...
In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. ...
[199] introduced a spectral graph transformer (SGT) network to learn this transformation function across multiple brain surfaces directly in the spectral domain, mapping input spectral coordinates to ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality based on Structural Connectivity
[article]
2021
bioRxiv
pre-print
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Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which ...
Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain ...
Graph Saliency Maps Through Spectral 395 Convolutional Networks: Application to Sex Classification with Brain Connectivity, in: 396 Stoyanov, D., Taylor, Z., Ferrante, E., Dalca, A.V., Martel, A., Maier-Hein ...
doi:10.1101/2021.03.15.435531
fatcat:xbo5f4s5tjczzdaagwtnppbe2y
A Unified Framework for Personalized Regions Selection and Functional Relation Modeling for Early MCI Identification
2021
NeuroImage
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Furthermore, our method allows us to capture the functional relations of a subject-specific ROI subset through the use of a graph-based neural network. ...
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. ...
We implemented the network using two edge-to-edge layers with 10 output feature maps, one edge-to-node layer with 20 output feature maps, and one node-to-graph layer with 40 output feature maps, to train ...
doi:10.1016/j.neuroimage.2021.118048
pmid:33878379
fatcat:djdqfurbxfc4nmdk3ghefiwwum
Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer's disease
2022
Journal of Neuroinflammation
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Three distinct phenotypes emerged with our combination of markers, thus expanding the classic binary "homeostatic vs. reactive" classification to a third state, which could represent "transitional" or ...
Machine learning models applied on either high-plex signal intensity data (gradient boosting machines) or directly on image features (convolutional neural networks) accurately discriminated control vs. ...
Mezlini, PhD for helpful discussions on spectral clustering, and Tessa Connors, Angelica Gaona, and Patrick Dooley for technical support from the MADRC Brain Bank. ...
doi:10.1186/s12974-022-02383-4
pmid:35109872
pmcid:PMC8808995
fatcat:ambflhlltng2lk5pbrqqqohvdu
Deep learning-based electroencephalography analysis: a systematic review
2019
Journal of Neural Engineering
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Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. ...
Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. ...
Other techniques include Deeplift [97] , saliency maps [209] , input-feature unit-output correlation maps [167] , retrieval of closest examples [41] , analysis of performance with transferred layers ...
doi:10.1088/1741-2552/ab260c
pmid:31151119
fatcat:tgb2o34h2zbx7jft2d6bqbkvlu
Artificial intelligence for the echocardiographic assessment of valvular heart disease
2022
Heart
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Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. ...
Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve ...
Measures such as saliency maps, which show which parts of the images are analysed for classification, can help the user understand how the algorithm functions. 43 Widespread AI implementation has also ...
doi:10.1136/heartjnl-2021-319725
pmid:35144983
fatcat:xu652vr2qncflppp5htd42zehm
2022 Roadmap on Neuromorphic Computing and Engineering
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2105.05956v2 fatcat:n5zeug6lnfgn3jvmnh2w6sckzyOther Versions
learn or deal with complex data as our brain does. ...
Among their potential future applications, an important niche is moving the control from data centers to edge devices. ...
Concluding Remarks Integrating event-based vision sensing and processing with neuromorphic computation techniques is expected to yield solutions that will be able to penetrate the artificial vision market ...
arXiv:2105.05956v3
fatcat:pqir5infojfpvdzdwgmwdhsdi4
Neuroimaging-based biomarkers for pain
2019
PAIN Reports
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In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded ...
With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. ...
connectivity across a set of regions, graph theoretic properties such as global network efficiency, and more. ...
doi:10.1097/pr9.0000000000000751
pmid:31579847
pmcid:PMC6727991
fatcat:dj4o5h2vtvcyvjchkjhgbhkzsq
Deep learning-based electroencephalography analysis: a systematic review
[article]
2019
arXiv
pre-print
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As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. ...
In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and ...
Other techniques include Deeplift [87] , saliency maps [190] , input-feature unit-output correlation maps [150] , retrieval of closest examples [34] , analysis of performance with transferred layers ...
arXiv:1901.05498v2
fatcat:5ugb4i3oerdrvarwozxvepbzxe
29th Annual Computational Neuroscience Meeting: CNS*2020
2020
BMC Neuroscience
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Selection starts in the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea to selected visual locations via gaze shifts. ...
Investigations of this question have, to date, focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. ...
Institute (Challenge grants to SJ), the Research Corporation for Science Advancement (a Cottrell SEED Award to TV), and the German Research Foundation (DFG grant #ME 1535/7-1 to RM), and the Foundation ...
doi:10.1186/s12868-020-00593-1
pmid:33342424
fatcat:edosycf35zfifm552a2aogis7a
25th Annual Computational Neuroscience Meeting: CNS-2016
2016
BMC Neuroscience
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I will discuss theoretical results that point to functional advantages of splitting neural populations into subtypes, both in feedforward and recurrent networks. ...
Such classification scheme could augment classification schemes based on molecular, anatomical, and electrophysiological properties. ...
The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. ...
doi:10.1186/s12868-016-0283-6
pmid:27534393
pmcid:PMC5001212
fatcat:bt45etzj2bbolfcxlxo7hlv6ju
19th biennial IPEG Meeting
2016
Neuropsychiatric Electrophysiology
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We implemented a convolutional neural network (CNN) in python with TensorFlow on a CentOS system with the NVIDIA GTX-1080 as GPU. ...
Source estimates and connectivity measures were mapped using Low Resolution Brain Tomography (LORETA). ...
Stimulation of α2A noradrenergic receptors on PFC spines by clonidine leads to strengthening of network connectivity, increase in neuronal PFC firing, and thus improves PFC regulation of sensory gating ...
doi:10.1186/s40810-016-0021-4
fatcat:iynffofbojdx3nspgskpizfwt4
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