Dianne K. Patterson

Syntactic processing depends on dorsal language tracts

Frontal and temporal language areas involved in syntactic processing are connected by several dorsal and ventral tracts, but the functional roles of the different tracts are not well understood. To identify which white matter tract(s) are important for syntactic processing, we examined the relationship between white matter damage and syntactic deficits in patients with primary progressive aphasia, using multimodal neuroimaging and neurolinguistic assessment. Diffusion tensor imaging showed that microstructural damage to left hemisphere dorsal tracts--the superior longitudinal fasciculus including its arcuate component--was strongly associated with deficits in comprehension and production of syntax. Damage to these dorsal tracts predicted syntactic deficits after gray matter atrophy was taken into account, and fMRI confirmed that these tracts connect regions modulated by syntactic processing. In contrast, damage to ventral tracts--the extreme capsule fiber system or the uncinate fasciculus--was not associated with syntactic deficits. Our findings show that syntactic processing depends primarily on dorsal language tracts.

The nature of the language input affects brain activation during learning from a natural language

Artificial language studies have demonstrated that learners are able to segment individual word-like units from running speech using the transitional probability information. However, this skill has rarely been examined in the context of natural languages, where stimulus parameters can be quite different. In this study, two groups of English-speaking learners were exposed to Norwegian sentences over the course of three fMRI scans. One group was provided with input in which transitional probabilities predicted the presence of target words in the sentences. This group quickly learned to identify the target words and fMRI data revealed an extensive and highly dynamic learning network. These results were markedly different from activation seen for a second group of participants. This group was provided with highly similar input that was modified so that word learning based on syllable co-occurrences was not possible. These participants showed a much more restricted network. The results demonstrate that the nature of the input strongly influenced the nature of the network that learners employ to learn the properties of words in a natural language.

Dynamic changes in network activations characterize early learning of a natural language

Those who are initially exposed to an unfamiliar language have difficulty separating running speech into individual words, but over time will recognize both words and the grammatical structure of the language. Behavioral studies have used artificial languages to demonstrate that humans are sensitive to distributional information in language input, and can use this information to discover the structure of that language. This is done without direct instruction and learning occurs over the course of minutes rather than days or months. Moreover, learners may attend to different aspects of the language input as their own learning progresses. Here, we examine processing associated with the early stages of exposure to a natural language, using fMRI. Listeners were exposed to an unfamiliar language (Icelandic) while undergoing four consecutive fMRI scans. The Icelandic stimuli were constrained in ways known to produce rapid learning of aspects of language structure. After approximately 4 min of exposure to the Icelandic stimuli, participants began to differentiate between correct and incorrect sentences at above chance levels, with significant improvement between the first and last scan. An independent component analysis of the imaging data revealed four task-related components, two of which were associated with behavioral performance early in the experiment, and two with performance later in the experiment. This outcome suggests dynamic changes occur in the recruitment of neural resources even within the initial period of exposure to an unfamiliar natural language.

Language lateralization shifts with learning by adults

For the majority of the population, language is a left-hemisphere lateralized function. During childhood, a pattern of increasing left lateralization for language has been described in brain imaging studies, suggesting that this trait develops. This development could reflect change due to brain maturation or change due to skill acquisition, given that children acquire and refine language skills as they mature. We test the possibility that skill acquisition, independent of age-associated maturation can result in shifts in language lateralization in classic language cortex. We imaged adults exposed to an unfamiliar language during three successive fMRI scans. Participants were then asked to identify specific words embedded in Norwegian sentences. Exposure to these sentences, relative to complex tones, resulted in consistent activation in the left and right superior temporal gyrus. Activation in this region became increasingly left-lateralized with repeated exposure to the unfamiliar language. These results demonstrate that shifts in lateralization can be produced in the short term within a learning context, independent of maturation.

Measurement of grey parrot (Psittacus erithacus) trachea via magnetic resonance imaging, dissection, and electron beam computed tomography

To produce a model to explain the acoustic properties of human speech sounds produced by Grey parrots (Psittacus erithacus) and to compare these properties across species (e.g., with humans, other psittacine and nonpsittacine mimics), researchers need adequate measurements of the chambers that constitute the parrot vocal tract. Various methods can provide such data. Here we compare results for tracheal measurements provided by a) magnetic resonance imaging (MRI) of a live bird, b) caliper measurements of four preserved specimens, and c) electron beam computed tomography (EBCT) of three of these preserved specimens. We find that EBCT scans provide data that correspond to the inner area of the dissected trachea, whereas MRI results correspond to area measurements that include tracheal ring thickness. We briefly discuss how these data may predict formant values for Grey parrot reproduction of human vowels. Our results suggest how noninvasive techniques can be used for cross‐species comparisons, including the coevolution of structure and function in avian mimicry. J. Morphol. 238:81–91, 1998.

An fMRI study of implicit language learning in developmental language impairment

Individuals with developmental language impairment can show deficits into adulthood. This suggests that neural networks related to their language do not normalize with time. We examined the ability of 16 adults with and without impaired language to learn individual words in an unfamiliar language. Adults with impaired language were able to segment individual words from running speech, but needed more time to do so than their normal-language peers. ICA analysis of fMRI data indicated that adults with language impairment activate a neural network that is comparable to that of adults with normal language. However, a regional analysis indicated relative hyperactivation of a collection of regions associated with language processing. These results are discussed with reference to the Statistical Learning Framework and the sub-skills thought to relate to word segmentation.

Dynamic Data Visualization with Weave and Brain Choropleths.

This article introduces the neuroimaging community to the dynamic visualization workbench, Weave (https://www.oicweave.org/), and a set of enhancements to allow the visualization of brain maps. The enhancements comprise a set of brain choropleths and the ability to display these as stacked slices, accessible with a slider. For the first time, this allows the neuroimaging community to take advantage of the advanced tools already available for exploring geographic data. Our brain choropleths are modeled after widely used geographic maps but this mashup of brain choropleths with extant visualization software fills an important neuroinformatic niche. To date, most neuroinformatic tools have provided online databases and atlases of the brain, but not good ways to display the related data (e.g., behavioral, genetic, medical, etc). The extension of the choropleth to brain maps allows us to leverage general-purpose visualization tools for concurrent exploration of brain images and related data. Related data can be represented as a variety of tables, charts and graphs that are dynamically linked to each other and to the brain choropleths. We demonstrate that the simplified region-based analyses that underlay choropleths can provide insights into neuroimaging data comparable to those achieved by using more conventional methods. In addition, the interactive interface facilitates additional insights by allowing the user to filter, compare, and drill down into the visual representations of the data. This enhanced data visualization capability is useful during the initial phases of data analysis and the resulting visualizations provide a compelling way to publish data as an online supplement to journal articles.