Aditya P. Mathur, Professor Department of Computer Science Purdue University, West Lafayette

1. Computational models to study auditory processing and learning disorders in children A proposal for exploratory research[January 2006-June 2007] Aditya P. Mathur, Professor Department of Computer Science Purdue University, West Lafayette Consultants: Nina Kraus, Professor University Institute for Neuroscience in the School of Communications, Northwestern University Sumit Dhar, Assistant Professor Department of Communication Sciences and Disorders, Northwestern University Thursday June 16, ‘05. A SERC Showcase Presentation.

2. Research Objectives • Construct and validate computational models that • Mimic experimental results of auditory processing tasks in children diagnosed with auditory processing disorders and learning disabilities. • Experiment with the validated models to understand the impact of treatments on children with auditory disorders and learning disabilities. Computational Models: Auditory Processing

3. Research Objectives: Modeling Issues • (Interpretive) Neural network models? • (Non-interpretive) Dynamical parallel distributed models ? (likely choice) • (Non-interpretive) Electrical models? Computational Models: Auditory Processing

4. Language Impairment • “A language impairment affects the understanding of language (receptive language disorder), the formulation of an utterance (saying what one intends to say--expressive language disorder), or both.” Sarah Morales, Children's Speech Care Center • “Specific Language Impairment (language acquisition) is just one of the many communication disorders that affect more than 1 million students in the public schools.” The University of Kansas Merrill Advanced Studies Center Computational Models: Auditory Processing

5. Language Impairment: A few research questions • What neural processing abnormalities cause language impairment? • How do the brainstem and cortical auditory processes correlate in LP children? • How treatments affect such processing? Computational Models: Auditory Processing

6. The brainstem is located at the juncture of the cerebrum and the spinal column. It consists of the midbrain, medulla oblongata, and the pons. Brainstem and the Auditory Cortex The auditory cortex is located in the Sylvian fissure of the Temporal Lobe. The Sylvian fissure - the deepest and most prominent of the cortical fissures; separates the frontal lobes and temporal lobes in both hemispheres Computational Models: Auditory Processing

7. W. R. Zemin: Speech and Hearing Science: Anatomy and Physiology, 1997. Location-related Identity-related Ascending Auditory Pathway Computational Models: Auditory Processing

8. Recent Findings: LP Children Abnormal functional relationship between brainstem and cortical auditory processing. [Wible,Nicol, Kraus; Brain 2005] Let us examine some details…. Computational Models: Auditory Processing

9. Correlation between brainstem and cortical auditory processes in LP Children [1][Wible,Nicol, Kraus; Brain 2005] • Questions: • Is there a functional relationship between brainstem and cortical activity? • Would such relationship be observed across normal and LP children? Computational Models: Auditory Processing

10. Correlation between brainstem and cortical auditory processes in LP Children [2] • Why? • “Consistent functional relationships could imply common functional connections between brainstem and cortex for all children.” • “This could suggest that language problems result primarily from a suboptimal degree of processing at lower levels of the auditory pathway..” Computational Models: Auditory Processing

11. 40ms Normal children (11) Average response to the first stimulus LP Children (9) 30ms Stimuli train (total=6000 stimuli) Correlation between brainstem and cortical auditory processes in LP Children [3] 12ms Stimulus: /da/, consists of five formants starting from the consonant /d/ to vowel /a/. Computational Models: Auditory Processing

12. Mean auditory cortical response to the first and fourth stimuli (in noise) for normal children. LP children first Normal children fourth Normalized mean brainstem response to the first stimulus. Mean auditory cortical response to the first and fourth stimuli (in noise) for LP children. Correlation between brainstem and cortical auditory processes in LP Children [4] Computational Models: Auditory Processing

13. Subsequent processing leads to wave Vn. Activity leading to wave V. V and Vn waves Computational Models: Auditory Processing

14. Results [1] [Wible,Nicol, Kraus; Brain 2005] • Duration of the V-Vn complex was more prolonged for LP children than for normal children. • Effect of noise in diminishing the correlation between the first and fourth stimuli in a train was more pronounced for LP children compared with normal children. • Effect of noise on correlations (as above) caused a a significant degradation with respect to quiet for LP children only. • Stronger correlation between brainstem and cortical auditory processing demonstrated by normal children. Computational Models: Auditory Processing

15. Results [2] • The normal and LP children differed in IQ, there was no correlation between IQ and the measure of brainstem wave duration. • Suggestion: “.. Increased synchrony amongst mechanisms that encode transient acoustic information at the level of the brainstem contributes to more robust processing at the cortical level.” Computational Models: Auditory Processing

16. Research Plan Model selection: Select or develop a suitable model M of speech processing. Validate the model with respect to observations made by ANL scientists. LP modeling/validation: Understand and model the neural dynamics associated with selected disabilities in children. Treatment modeling/validation: Identify and model the dynamics of treatment(s) associated with the selected disabilities. Computational Models: Auditory Processing

17. Research Plan: ANN versus Dynamical Models • ANNs postulate an artificial architecture for a given task and enforce a learning process. [e.g. Hinton and Shallice, ‘91, Jenison, ‘96] • Dynamical models use the architecture observed anatomically and examine dynamic behavior (e.g. waveforms) for validation. • ANNs are specific to a task (e.g. word recognition). • Dynamical models are task dependent; tasks are side effects of the model Computational Models: Auditory Processing

18. Research Plan: Benefits • Reduced need for experimentation with live subjects. • Treatment evaluation can be done using mathematical models. Only the most promising treatments, as suggested by the models need to be validated against live subjects. • Improved understanding of the auditory processes that are the cause of learning disabilities. Computational Models: Auditory Processing

19. Relation to Software Engineering • New biologically inspired models of computation might emerge out of the proposed work. • Such models will likely be applicable in areas such as robotics and autonomic computing • Dynamical models might provide new insights into fault tolerance and reduced level functionality. • A new notion of “fuzzy errors” might develop which changes the concept of the binary-notion of program failure. Computational Models: Auditory Processing

20. References G. E. Hinton and T. Shallice, Lesioning an attractor network: Investigations of acquired dyslexia, Psychological Review, Vol. 98, 1991, pp.74-95. R. L. Jenison, A computational model of reorganization in auditory cortex in response to cochlear lesions, Modeling Sensorineural Hearing Loss. Ed.: W. Jesteadt, Lawrence Erlbaum Associates, Inc., New Jersey, 1996, pp. 49-65. B. Wible , T. Nocol, and N. Kraus, Correlation between brainstem and cortical auditory processes in normal and language-impaired children, Brain, 2005, Vol. 128, pp. 417--423, Oxford University Press Computational Models: Auditory Processing