Publications by authors named "Rakib Al-Fahad"

3 Publications

  • Page 1 of 1

Decoding Hearing-Related Changes in Older Adults' Spatiotemporal Neural Processing of Speech Using Machine Learning.

Front Neurosci 2020 16;14:748. Epub 2020 Jul 16.

Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States.

Speech perception 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 ( in time) and spatial ( in the brain) characteristics of cortical speech-evoked responses that distinguish older adults with or without mild hearing loss. We performed source analyses to estimate cortical surface signals from the EEG recordings during a phoneme discrimination task conducted under clear and noise-degraded conditions. We computed source-level ERPs (i.e., mean activation within each ROI) from each of the 68 ROIs of the Desikan-Killiany (DK) atlas, averaged over a randomly chosen 100 trials without replacement to form feature vectors. We adopted a multivariate feature selection method called stability selection and control to choose features that are consistent over a range of model parameters. We use parameter optimized support vector machine (SVM) as a classifiers to investigate the and that segregate groups and speech clarity. For clear speech perception, whole-brain data revealed a classification accuracy of 81.50% [area under the curve (AUC) 80.73%; F1-score 82.00%], distinguishing groups within ∼60 ms after speech onset (i.e., as early as the P1 wave). We observed lower accuracy of 78.12% [AUC 77.64%; F1-score 78.00%] and delayed classification performance when speech was embedded in noise, with group segregation at 80 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 in the RH. Moreover, stability selection analysis identified 12 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 16 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (78.7% accuracy). Our results identify critical time-courses and brain regions that distinguish mild hearing loss from normal hearing in older adults and confirm a larger number of active areas, particularly in RH, when processing noise-degraded speech information.
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http://dx.doi.org/10.3389/fnins.2020.00748DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378401PMC
July 2020

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.

J Neural Eng 2020 02 5;17(1):016045. Epub 2020 Feb 5.

Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, United States of America.

Objective: Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions.

Approach: We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions.

Main Results: We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization.

Significance: Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC  =  0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.
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http://dx.doi.org/10.1088/1741-2552/ab6040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004853PMC
February 2020

Early Imaging Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL.

IEEE Access 2019 8;7:146662-146674. Epub 2019 Oct 8.

St. Jude Children's Research Hospital, Memphis, Tennessee, USA.

In the United States, Acute Lymphoblastic Leukemia (ALL), the most common child and adolescent malignancy, accounts for roughly 25% of childhood cancers diagnosed annually with a 5-year survival rate as high as 94% [1]. This improved survival rate comes with an increased risk for delayed neurocognitive effects in attention, working memory, and processing speed [2]. Predictive modeling and characterization of neurocognitive effects are critical to inform the family and also to identify patients for interventions targeting. Current state-of-the-art methods mainly use hypothesis-driven statistical testing methods to characterize and model such cognitive events. While these techniques have proven to be useful in understanding cognitive abilities, they are inadequate in explaining causal relationships, as well as individuality and variations. In this study, we developed multivariate data-driven models to measure the late neurocognitive effects of ALL patients using behavioral phenotypes, Diffusion Tensor Magnetic Resonance Imaging (DTI) based tractography data, morphometry statistics, tractography measures, behavioral, and demographic variables. Alongside conventional machine learning and graph mining, we adopted "Stability Selection" to select the most relevant features and choose models that are consistent over a range of parameters. The proposed approach demonstrated substantially improved accuracy (13% - 26%) over existing models and also yielded relevant features that were verified by domain experts.
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http://dx.doi.org/10.1109/access.2019.2946240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297193PMC
October 2019