Transcript Computational models to study auditory processing and learning disorders in children Consultants:

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.
Research Objectives
Construct and validate computational models that
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Mimic experimental results of auditory processing tasks
in children diagnosed with auditory processing disorders
and learning disabilities.
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Experiment with the validated models to understand the
impact of treatments on children with auditory disorders
and learning disabilities.
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Research Objectives: Modeling Issues
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(Interpretive) Neural network models?
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(Non-interpretive) Dynamical parallel distributed models
? (likely choice)
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(Non-interpretive) Electrical models?
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Language Impairment
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“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
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“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
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Language Impairment: A few research
questions
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What neural processing abnormalities cause language
impairment?
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How do the brainstem and cortical auditory processes
correlate in LP children?
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How treatments affect such processing?
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Brainstem and the Auditory Cortex
The brainstem is located at the juncture of the
cerebrum and the spinal column. It consists of
the midbrain, medulla oblongata, and the pons.
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
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Ascending Auditory Pathway
W. R. Zemin: Speech and
Hearing Science: Anatomy
and Physiology, 1997.
Identity-related
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Location-related
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Recent Findings: LP Children
Abnormal functional relationship between brainstem and cortical
auditory processing.
[Wible,Nicol, Kraus; Brain 2005]
Let us examine some details….
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Correlation between brainstem and cortical
auditory processes in LP Children [1]
[Wible,Nicol, Kraus; Brain 2005]
Questions:
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Is there a functional relationship between brainstem and
cortical activity?
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Would such relationship be observed across normal and LP
children?
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Correlation between brainstem and cortical
auditory processes in LP Children [2]
Why?
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“Consistent functional relationships could imply common
functional connections between brainstem and cortex for all
children.”
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“This could suggest that language problems result primarily
from a suboptimal degree of processing at lower levels of the
auditory pathway..”
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Correlation between brainstem and cortical
auditory processes in LP Children [3]
40ms
Normal children (11)
LP Children (9)
12ms
Average response to
the first stimulus
30ms
Stimuli train (total=6000 stimuli)
Stimulus:
/da/, consists of five formants starting from the
consonant /d/ to vowel /a/.
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Correlation between brainstem and cortical
auditory processes in LP Children [4]
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.
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V and Vn waves
Subsequent processing leads to wave Vn.
Activity leading to wave V.
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Results [1] [Wible,Nicol, Kraus; Brain 2005]
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Duration of the V-Vn complex was more prolonged for LP children
than for normal children.
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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.
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Effect of noise on correlations (as above) caused a a significant
degradation with respect to quiet for LP children only.
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Stronger correlation between brainstem and cortical auditory
processing demonstrated by normal children.
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Results [2]
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The normal and LP children differed in IQ, there was no correlation
between IQ and the measure of brainstem wave duration.
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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.”
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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.
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Research Plan: ANN versus Dynamical Models
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ANNs postulate an artificial architecture for a given task and
enforce a learning process. [e.g. Hinton and Shallice, ‘91,
Jenison, ‘96]
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Dynamical models use the architecture observed
anatomically and examine dynamic behavior (e.g.
waveforms) for validation.
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ANNs are specific to a task (e.g. word recognition).
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Dynamical models are task dependent; tasks are side effects
of the model
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Research Plan: Benefits
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Reduced need for experimentation with live subjects.
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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.
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Improved understanding of the auditory processes that are
the cause of learning disabilities.
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Relation to Software Engineering
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New biologically inspired models of computation might
emerge out of the proposed work.
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Such models will likely be applicable in areas such as
robotics and autonomic computing
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Dynamical models might provide new insights into fault
tolerance and reduced level functionality.
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A new notion of “fuzzy errors” might develop which changes
the concept of the binary-notion of program failure.
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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
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