I watched Interstellar last month under the stars on campus here at UC San Diego. Several weeks later, the words of this Dylan Thomas poem still resonate within my mind. How is my brain able to understand and remember this spoken message? When I have reached the wise old age of the wonderful Michael Caine, how will my auditory cortex have changed its ability to process these words? Dr. David Woods of UC Davis’s Human Cognitive Neurophysiology Laboratory at the VA Medical Center in Martinez, California is in the business of evaluating age-related changes in speech perception and verbal memory.

Randy Glasbergen, http://glasbergen.com

Age-related decline in verbal processing is in part due to changes in central auditory processing, but little is known of how healthy aging affects the human auditory cortex. Dr. Woods’s laboratory seeks to evaluate age-related changes in speech perception and memory in groups of young and older subjects and correlate them with structural and functional changes in human auditory cortex using several magnetic resonance imaging (MRI) techniques. Dr. Woods proposes to first establish baseline behavioral measures in speech reception thresholds in noise and auditory verbal short-term memory, then investigate age-related changes in auditory cortex surface structure using high-resolution T1-weighted MRI combined with cortical surface mapping. This allows for the analysis of auditory cortex thickness, area, and curvature. Diffusion tensor imaging, an MRI technique used to look at water diffusion, is applied to measure age-related changes in neuropil density and fiber connectivity; this connectivity will then be correlated with prior measured changes in cortical thickness and curvature. Behavioral measures of aging subjects can lend insight into how anatomical changes are associated with behavioral impairment. Lastly, Dr. Woods ties all this structural and behavioral data together with functional organization in the auditory cortex. Using fMRI techniques, he examines the automatic and attention-dependent processing of simple tone stimuli and consonant-vowel-consonant (CVC) syllables. Comparison of tone and CVC processing will elucidate the regions of auditory association cortex that show specific activations to different auditory stimuli and the neural circuits engaged in speech processing.

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Fig. 1 | (A) Cortical surface locations of activation peaks associated with phonological processing of speech sounds from a meta-analysis by Turkeltaub and Coslett (2010). Red dots indicate cortical surface locations of the reported coordinates on the left hemisphere of 60 individual subjects, while blue dots indicate those on the right hemisphere. Cyan cross indicates median location of activations in the left hemisphere; yellow cross indicates that of right. (B) Cortical surface locations of regions responding to CV syllables in comparison to bird song elements, musical instruments, or animal sounds, from Leaver and Rauschecker (2010). See (A) for color associations.

Previous work by Dr. Woods has sought to use population-based cortical-surface analysis of fMRI data to characterize the processing of CVCs and spectrally matched amplitude-modulated noise bursts (AMNBs) in human auditory cortex. Using average auditory cortical field locations (Fig. 1) defined from tonotopic mapping in a previous study, Woods et al. found that activations in the auditory cortex were defined by two stimulus-preference gradients.

Fig. 2 | A schematic map of auditory cortical fields showing stimulus preferences for consonant-vowel-conosonants (CVCs - indicated in ORANGE) and amplitude-modulated noise bursts (AMNBs - indicated in GREEN). The ratio of orange to green reflects relative magnitude of activations with respect to each stimulus type.

Fig. 2 | A schematic map of auditory cortical fields showing stimulus preferences for consonant-vowel-conosonants (CVCs – indicated in ORANGE) and amplitude-modulated noise bursts (AMNBs – indicated in GREEN). The ratio of orange to green reflects relative magnitude of activations with respect to each stimulus type.

Medial belt auditory cortical fields preferred AMNBs (Fig. 2 – green fields), while lateral belt and parabelt fields preferred CVCs (Fig. 2 – orange fields). This preference extends to the core cortical fields, as shown by the medial regions of primary auditory cortex (Fig. 2 – A1) and the rostral field preferring AMNBs and lateral regions preferring CVCs.

Fig. 3 | Quantification of activations by color and brightness. (A) Mean percent signal change of activations coded by brightness. Colors shows stimulus preference (CVC = red, green = AMNB). Yellow regions are activated by both stimuli. (B) Auditory cortical field locations projected onto average curvature map of the superior temporal plane.

Fig. 3 | Quantification of activations by color and brightness. (A) Mean percent signal change of activations coded by brightness. Colors shows stimulus preference (CVC = red, green = AMNB). Yellow regions are activated by both stimuli. (B) Auditory cortical field locations projected onto average curvature map of the superior temporal plane.

Woods, et al. also found a difference in magnitude of activation amongst the different field groups to the two stimuli. Anterior ACFs showed smaller activations (Fig. 3 – dullness of anterior fields), but more clearly defined, singular stimulus preferences (Fig. 3 – only green or red in anterior fields, rather than mixing) than posterior fields (Fig. 3 – note brightness and yellow color in posterior fields).

Fig. 4 | Effects of attention on activation magnitudes in different field groups. UA = unimodal auditory, BA = bimodal auditory attention, BV = bimodal visual attention.

Fig. 4 | Effects of attention on activation magnitudes in different field groups. UA = unimodal auditory, BA = bimodal auditory attention, BV = bimodal visual attention.

Attention significantly enhanced responses throughout the auditory cortex and within every field group, indicating that attentional enhancements to CVCs and AMNBs had similar magnitudes and distributions over auditory cortex (Fig. 4 – mean % of attentional enhancement indicated). This demonstrates that preference gradients are unaffected by auditory attention, which suggests that the preferences of each auditory cortical field reflects automatic rather than attention-dependent processing of difference sound features.

The above investigations are only a brushstroke on Dr. David Woods’s eclectic experimental canvas. To hear more about his adventures in verbal processing and memory, come join in on a lively journal club presentation by Erik Kaestner, a graduate student in the UCSD Neurosciences Graduate Program, on Monday, March 30th at 5:30 PM in Pac Hall 3502. Then, come hear Dr. David Woods himself speak:

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Thanks for listening!

David L. Woods,Timothy J. Herron, Anthony D. Cate, Xiaojian Kang, and E. W. Yund. Phonological Processing in Human Auditory Cortical Fields. Front Hum Neurosci. 2011; 5: 42. Published online 2011 Apr 20. doi:  10.3389/fnhum.2011.00042


Eulanca Y. Liu is a first year graduate student in UCSD Neurosciences and third year in the UCSD Medical Scientist MD/PhD Training Program. When not studying the physiological basis of fMRI in the lab of Dr. Rick Buxton, she enjoys jet-setting, calligraphy and graphic design, opera/theatre/dance, a day at the museum, great single-malt scotch, a hot caffeinated beverage, and dabbling. She will spend the weekend watching the Formula 1 Malaysia Grand Prix whilst writing this post. She can be reached at eyl015 at ucsd.edu and read at medium.com/@ZanzibarByCar. Feedback is welcome!

How is your auditory cortex processing the engine sounds of this Ford Fiesta ST?

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