New paper: mapping speech comprehension with optical imaging (Hassanpour et al.)

Although fMRI is great for a lot of things, it also presents challenges, especially for auditory neuroscience. Echoplanar imaging is loud, and this acoustic noise can obscure stimuli or change the cognitive demand of a task (Peelle, 2014). In addition, patients with implanted medical devices can't be scanned.

My lab has been working with Joe Culver's optical radiology lab to develop a solution to these problems using high-density diffuse optical tomography (HD-DOT). Similar to fNIRS, HD-DOT uses light spectroscopy to image oxygenated and deoxygenated blood signals, related to the BOLD response in fMRI. HD-DOT also incorporates realistic light models to facilitate source reconstruction—this of huge importance for studies of cognitive function and facilitates  combining results across subjects. A detailed description of our current large field-of-view HD-DOT system can be found in Eggebrecht et al. (2014).

Because HD-DOT is relatively new, an important first step in using it for speech studies was to verify that it is indeed able to capture responses to spoken sentences, both in terms of effect size and spatial location. Mahlega Hassanpour is a PhD student who enthusiastically took on this challenge. In our paper now out in NeuroImage (Hassanpour et al., 2015), Mahlega used a well-studied comparison of syntactic complexity looking at sentences containing subject-relative or object-relative center embedded clauses (taken from our previous fMRI study; Peelle et al 2010).

Consistent with previous fMRI work, we found a sensible increase from a low level acoustic control condition (1 channel vocoded speech) to subject-relative sentences to object-relative sentences. The results were seen at both the single subject level (with some expected noise) and the group level.

We are really glad to see nice responses to spoken sentences with HD-DOT and are already pursuing several other projects. More to come!


Eggebrecht AT, Ferradal SL, Robichaux-Viehoever A, Hassanpour MS, Dehghani H, Snyder AZ, Hershey T, Culver JP (2014) Mapping distributed brain function and networks with diffuse optical tomography. Nature Photonics 8:448-454. doi:10.1038/nphoton.2014.107

Hassanpour MS, Eggebrecht AT, Culver JP, Peelle JE (2015) Mapping cortical responses to speech using high-density diffuse optical tomography. NeuroImage 117:319–326. doi:10.1016/j.neuroimage.2015.05.058 (PDF)

Peelle JE (2014) Methodological challenges and solutions in auditory functional magnetic resonance imaging. Frontiers in Neuroscience 8:253. doi:10.3389/fnins.2014.00253 (PDF)

Peelle JE, Troiani V, Wingfield A, Grossman M (2010) Neural processing during older adults' comprehension of spoken sentences: Age differences in resource allocation and connectivity. Cerebral Cortex 20:773-782. doi:10.1093/cercor/bhp142 (PDF)

New paper: Listening effort and accented speech

Out now in Frontiers: A short opinion piece on listening effort and accented speech, written in collaboration with Wash U colleague Kristin Van Engen. The crux of the article is that there is increasing agreement that listening to degraded speech requires listeners to engage additional cognitive processes, under a generic label of "listening effort". Listening effort is typically discussed in terms of hearing impairment or background noise, both of which obscure acoustic features in the speech signal and make it more difficult to understand. In this paper Kristin and I argue that accented speech is also difficult to understand, and should be thought of in a similar context.

We have tried to frame these issues in a general way that incorporates multiple kinds of acoustic challenge. That is, the degree to which the incoming speech signal does not match our stored representations determines the amount of cognitive support needed. This mismatch could come from background noise, or from systematic phonemic or suprasegmental deviations associated with accented speech. A related point is that comprehension accuracy depends both on the quality of the incoming acoustic signal, and the amount of additional cognitive support a listener allocates: Degraded or accented speech may be perfectly intelligible if sufficient cognitive resources are available (and engaged).

Figure 1. (A) Speech signals that match listeners' perceptual expectations are processed relatively automatically, but when acoustic match is reduced (due to, for example, noise or unfamiliar accents), additional cognitive resources are needed to compensate. (B) Executive resources are recruited in proportion to the degree of acoustic mismatch between incoming speech and listeners' representations. When acoustic match is high, good comprehension is possible without executive support. However, as the acoustic match becomes poorer, successful comprehension cannot be accomplished unless executive resources are engaged. Not shown is the extreme situation in which acoustic mismatch is so poor that comprehension is impossible.

I like this article because it raises a number of interesting questions that can be experimentally tested. One of the big ones is the degree to which the type of acoustic mismatch matters: that is, are similar cognitive processes engaged when speech is degraded due to background noise as when an unfamiliar accent reduces intelligibility? My instinct says yes, but I wouldn't bet on it until more data are in.


Van Engen KJ, Peelle JE (2014) Listening effort and accented speech. Front Hum Neurosci 8:577.