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Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning

View ORCID ProfileMd Sultan Mahmud, Faruk Ahmed, Rakib Al-Fahad, Kazi Ashraf Moinuddin, Mohammed Yeasin, Claude Alain, View ORCID ProfileGavin M. Bidelman
doi: https://doi.org/10.1101/786566
Md Sultan Mahmud
1Department of Electrical and Computer Engineering, University of Memphis, TN, USA
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  • For correspondence: mmahmud@memphis.edu
Faruk Ahmed
1Department of Electrical and Computer Engineering, University of Memphis, TN, USA
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Rakib Al-Fahad
1Department of Electrical and Computer Engineering, University of Memphis, TN, USA
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Kazi Ashraf Moinuddin
1Department of Electrical and Computer Engineering, University of Memphis, TN, USA
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Mohammed Yeasin
1Department of Electrical and Computer Engineering, University of Memphis, TN, USA
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Claude Alain
2Rotman Research Institute–Baycrest Centre for Geriatric Care, Toronto, Ontario, Canada
3University of Toronto, Department of Psychology, Toronto, Ontario, Canada
4University of Toronto, Institute of Medical Sciences, Toronto, Ontario, Canada
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Gavin M. Bidelman
5Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA
6School of Communication Sciences & Disorders, University of Memphis, Memphis, TN, USA
7University of Tennessee Health Sciences Center, Department of Anatomy and Neurobiology, Memphis, TN, USA
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ABSTRACT

Speech comprehension in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of the cortex’s speech-evoked response that distinguish older adults with or without mild hearing loss. We used source montage to model scalp-recorded during a phoneme discrimination task conducted under clear and noise-degraded conditions. We applied machine learning analyses (stability selection and control) to choose features of the speech-evoked response that are consistent over a range of model parameters and support vector machine (SVM) classification to investigate the time course and brain regions that segregate groups and speech clarity. Whole-brain data analysis revealed a classification accuracy of 82.03% [area under the curve (AUC)=81.18%; F1-score 82.00%], distinguishing groups within ∼50 ms after speech onset (i.e., as early as the P1 wave).We observed lower accuracy of 78.39% [AUC=78.74%; F1-score=79.00%] and delayed classification performance when the speech token were embedded in noise, with group segregation at 60 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured over the RH. Moreover, stability selection analysis identified 13 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 15 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (76% accuracy). Our results identify two core neural networks associated with complex speech perception in older adults and confirm a larger number of neural regions, particularly in RH and frontal lobe, are active when processing degraded speech information.

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Posted September 30, 2019.
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Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning
Md Sultan Mahmud, Faruk Ahmed, Rakib Al-Fahad, Kazi Ashraf Moinuddin, Mohammed Yeasin, Claude Alain, Gavin M. Bidelman
bioRxiv 786566; doi: https://doi.org/10.1101/786566
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Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning
Md Sultan Mahmud, Faruk Ahmed, Rakib Al-Fahad, Kazi Ashraf Moinuddin, Mohammed Yeasin, Claude Alain, Gavin M. Bidelman
bioRxiv 786566; doi: https://doi.org/10.1101/786566

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