Gavin M. Bidelman, Ph.D. - US grants

Project Summary Successful perception of the world requires that the human brain assemble diverse sensory information into common, well-formed groupings, a process known as categorical perception (CP). At its core, CP is known as the ?invariance-? or ?many-to-one mapping? problem: an infinite collection of sensory features must be converted into a finite, invariant perceptual space to be acted upon by the perceptual system. Categorization manifests in nearly all aspects of human cognition and learning including the perception of faces, colors, and music. Skilled categorization is particularly important in the context of spoken and written language as evident by its integral role in reading acquisition and auditory-based learning disorders (e.g., dyslexia, specific language impairment). Despite a wealth of behavioral studies and its importance to understanding receptive human communication, the neural mechanisms underlying the core ability of CP remain poorly understood. In a series of studies, the proposed work will address foundational questions of when, where, and how the brain converts continuous acoustic signals into discrete, meaningful categories exploited by the perceptual system. High-density neuroelectric brain recordings (EEG/ERP) will be obtained from human listeners during tasks designed to tap different attributes of categorical processing and modulate its neurobiology. Our central hypothesis is that auditory categorization skills recruit a common, parsimonious frontotemporal neural network that is both dynamically and differentially engaged depending on attention, familiarity of stimulus context/complexity, learning, and prior listening experience. Novel multivariate analytic techniques will be used to derive ?neurometric functions? from listeners? ERPs to ?decode? listeners? speech perception behaviors from their underlying brain activity. This common neurocomputational approach will be used to investigate several factors that modulate auditory categorization skills through five research aims: (Aim 1) the spatiotemporal emergence of CP in the brain; (Aim 2) linear vs. nonlinear signal dynamics; (Aim 3) identifying sounds from different domains (e.g., speech vs. music); (Aim 4) prior listening experience and novel learning. Aim 5 will measure functional connectivity from EEG recordings to determine how the directed flow of information within the CP brain network changes with the manipulations of prior aims (e.g., learning vs. processing mature categories). Providing a more complete biological description of the acoustic-to-phonetic mapping problem of CP will ultimately offer a window into not only normal speech perception but may reveal important neural mechanisms to target in disorders that impair the formation of auditory categories.

Project Summary/Abstract Alzheimer?s disease and its related dementias (AD/ADRD) account for ~$240M in annual healthcare costs in the USA. Mild cognitive impairment (MCI) is a transitional phase in progression of cognitive aging and is a key risk factor for developing ADRDs in reduced timeframes. It is an ideal stage to monitor early cognitive decline (and its neural markers) to identify older adults at risk for ADRDs. Conventional views of MCI neuropathology are memory/cognitive-centric; it is widely assumed speech-language symptoms are among the last to emerge in the cascade of events during preclinical ADRD. Challenging these views, we have recently documented several key findings that MCI compromises auditory neural processing and the brain?s ability to accurately categorize speech sounds, exposing a new sensory-based pathophysiology of early cognitive decline outside typical memory and executive functioning sequela. We now aim to expand our ongoing brain imaging studies on speech categorization and novel sound learning (R01DC016267) to characterize early neural declines in these fundamental speech functions in older adults with MCI/ADRDs. In preliminary work, we have discovered MCI is associated with hypersensitive cortical brain responses to speech as measured via EEG. These physiological changes occur in tandem with behavioral deficits in rapid speech labeling and are not observed in normal aging adults. Using behavior and neuroimaging (EEG) approaches, we will test the hypothesis that MCI is associated with ?pre-clinical? deficits in auditory-sensory brain processing that manifest in a reduced ability to properly map acoustic signals to the perceptual categories required for speech understanding. Aim 1 will compare speech-evoked EEGs from young, neurotypical listeners to new data collected in older adults with and without clinical diagnosis of MCI/ADRD. Outcomes will establish efficacy of speech-EEG as a new functional biomarker of MCI and resolve whether their perceptual speech deficits are due to impaired auditory- or linguistic-based processing. Comparing MCI vs. AD groups will assess ERP changes that track with disease progression. Aim 2 will compare the new functional (EEG) measures with gold-standard structural indices (MRI volumetrics) to establish direct links between electrophysiological and anatomical biomarkers of MCI/ADRDs. Relevance to Alzheimer?s disease and/or its related dementias: Uncovering MCI-related deficits in the brain?s precise encoding of speech (a listening skill) would be a significant advance which could offer a more sensitive neurodiagnostic for cognitive decline in ADRDs, potentially before the emergence of obvious memory and cognitive problems. Success of these exploratory studies may therefore fuel additional research to fully develop our EEG paradigms into rapid (< 5-10 min), low-cost, and non-invasive diagnostics for early cognitive impairments.