Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI
Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/12/1341 |
| _version_ | 1827575614033362944 |
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| author | Nikola K. Kasabov Helena Bahrami Maryam Doborjeh Alan Wang |
| author_facet | Nikola K. Kasabov Helena Bahrami Maryam Doborjeh Alan Wang |
| author_sort | Nikola K. Kasabov |
| collection | DOAJ |
| description | Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery. |
| first_indexed | 2024-03-08T20:59:32Z |
| format | Article |
| id | doaj.art-44575dc7ad5a4bf4acacbd3c706fbdc4 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-08T20:59:32Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-44575dc7ad5a4bf4acacbd3c706fbdc42023-12-22T13:53:57ZengMDPI AGBioengineering2306-53542023-11-011012134110.3390/bioengineering10121341Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRINikola K. Kasabov0Helena Bahrami1Maryam Doborjeh2Alan Wang3Knowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandKnowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandKnowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandAuckland Bioengineering Institute, University of Auckland, Auckland 1010, New ZealandHumans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.https://www.mdpi.com/2306-5354/10/12/1341spatio-temporal associative memorySTAMneuroimaging dataspiking neural networksNeuCubeEEG |
| spellingShingle | Nikola K. Kasabov Helena Bahrami Maryam Doborjeh Alan Wang Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI Bioengineering spatio-temporal associative memory STAM neuroimaging data spiking neural networks NeuCube EEG |
| title | Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI |
| title_full | Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI |
| title_fullStr | Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI |
| title_full_unstemmed | Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI |
| title_short | Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI |
| title_sort | brain inspired spatio temporal associative memories for neuroimaging data classification eeg and fmri |
| topic | spatio-temporal associative memory STAM neuroimaging data spiking neural networks NeuCube EEG |
| url | https://www.mdpi.com/2306-5354/10/12/1341 |
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