Biology subjects

Yeatman, J. D.

Publications and source records attributed to Yeatman, J. D..

35 records · Page 2Linked to original sources

bioRxiv2022

Development of the alpha rhythm is linked to visual white matter pathways and visual detection performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, strong variations can be observed in alpha properties during development, with an increase of alpha frequency over childhood and adulthood. Here we tested the hypothesis that these changes of alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large dMRI-EEG dataset (dMRI n=2,747, EEG n=2,561) of children and adolescents (age range: 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

neuroscience↗

bioRxiv2022

White matter and literacy: a dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individuals educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to reading skills over development or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N=14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the single-observation datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

neuroscience↗

bioRxiv2022

Reading instruction causes changes in category-selective visual cortex

Education sculpts specialized neural circuits for skills like reading that are critical to success in modern society but were not anticipated by the selective pressures of evolution. Does the emergence of brain regions that selectively process novel visual stimuli like words occur at the expense of cortical representations of other stimuli like faces and objects? To answer this question we conducted a randomized controlled trial with pre-school children (five years of age). We found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of visual cortex. Reading instruction enhanced the response to text but did not diminish the response to other categories. How these changes play out over a longer timescale is still unknown but, based on these data, we can surmise that high-level visual cortex undergoes rapid changes as children enter school and begin establishing new skills like literacy.

neuroscience↗

bioRxiv2021

Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex

The latency of neural responses in the visual cortex changes systematically across the lifespan. Here we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion MRI and MEG sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individuals brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.

neuroscience↗

bioRxiv2021

Catch me if you can: Least myelinated white matter develops fastest during early infancy

Development of myelin, a fatty sheath that insulates nerve fibers, is critical for brain function. Myelination during infancy has been studied with histology, but postmortem data cannot evaluate the longitudinal trajectory of white matter development. Here, we obtained longitudinal diffusion MRI and quantitative MRI measures of R1 in 0, 3 and 6 months-old human infants, and (ii) developed an automated method to identify white matter bundles and quantify their properties in each infants brain. We find that R1 increases from newborns to 6-months-olds in all bundles. R1 development is nonuniform: there is faster development in white matter that is less mature in newborns, and along inferior-to-superior as well as anterior-to-posterior spatial gradients. As R1 is linearly related to myelin fraction in white matter bundles, these findings open new avenues to elucidate typical and atypical white matter myelination in early infancy, which has important implications for early identification of neurodevelopmental disorders.

neuroscience↗

bioRxiv2021

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project - a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and grey matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference mplementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and cognitive neuroscience research. It will ease the translation of DKI advantages into clinical applications.

neuroscience↗

bioRxiv2021

Evaluating the reliability of human brain white matter tractometry

The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on reliability of the analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to demonstrate the reliability of computational analysis pipelines in neuroimaging.

neuroscience↗

bioRxiv2020

Measuring reading ability in the web-browser with a lexical decision task

An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the web-browser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test (r = 0.91, disattenuated r = 0.94). Importantly, the LDT reading ability measure is highly reliable (r = 0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 76 trials (2-3 minutes) provides a reliable (r = 0.95) measure of reading ability. Thus, the self-administered, Rapid Online Assessment of Reading ability (ROAR) developed here overcomes the constraints of resource-intensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability.

animal behavior and cognition↗

bioRxiv2020

White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex

Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the location of reading-related responses in lateral VTC. We obtained functional (fMRI), diffusion (dMRI), and quantitative (qMRI) magnetic resonance imaging data in 30 adults. fMRI was used to map reading-related responses by contrasting responses in a reading task with those in adding and color tasks; dMRI was used to identify the brains fascicles and to map their endpoints density in lateral VTC; qMRI was used to measure proton relaxation time (T1), which depends on cortical tissue microstructure. We fit linear models that predict reading-related responses in lateral VTC from endpoint density and T1 and used leave-one-subject-out cross-validation to assess prediction accuracy. Using a subset of our participants (N=10, feature selection set), we find that i) endpoint density of the arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), and vertical occipital fasciculus (VOF) are significant predictors of reading-related responses, and ii) cortical T1 of lateral VTC further improves the predictions of the fascicle model. Next, in the remaining 20 participants (validation set), we showed that a linear model that includes T1, AF, ILF and VOF significantly predicts i) the map of reading-related responses across lateral VTC and ii) the location of the visual word form area, a region critical for reading. Overall, our data-driven approach reveals that the AF, ILF, VOF and cortical microstructure have a consistent spatial relationship with an individuals reading-related responses in lateral VTC. HighlightsThe ILF, AF, and VOF predict the spatial layout of reading-related responses in VTC Gray matter microstructure improves the prediction of reading-related responses Fascicles and gray matter structure together predict the location of the VWFA

neuroscience↗

bioRxiv2019

Automaticity in the reading circuitry

Skilled reading requires years of practice associating visual symbols with speech sounds. Over the course of the learning process, this association becomes effortless and automatic. Here we test whether automatic activation of spoken-language circuits in response to visual words is a hallmark of skilled reading. Magnetoencephalography was used to measure word-selective responses under multiple cognitive tasks (N = 42, 7-12 years of age). Even when attention was drawn away from the words by performing an attention-demanding fixation task, strong word-selective responses were found in a language region (i.e., superior temporal gyrus) starting at ~300 ms after stimulus onset. Critically, this automatic word-selective response was indicative of reading skill: the magnitude of word-selective responses correlated with individual reading skill. Our results suggest that automatic recruitment of spoken-language circuits is a hallmark of skilled reading; with practice, reading becomes effortless as the brain learns to automatically translate letters into sounds and meaning.

neuroscience↗

bioRxiv2019

Bridging sensory and language theories of dyslexia: towards a multifactorial model

Competing theories of dyslexia posit that reading disability arises from impaired sensory, phonological, or statistical learning mechanisms. Importantly, many theories posit that dyslexia reflects a cascade of impairments emanating from a \"core deficit\". Here we collect a battery of psychophysical and language measures in 106 school-aged children to investigate whether dyslexia is best conceptualized under a core-deficit model, or as a disorder with heterogenous origins. Specifically, by capitalizing on the drift diffusion model to separate sensory encoding from task-related influences on performance in a visual motion discrimination experiment, we show that deficits in motion perception, decision making and phonological processing manifest largely independently. Based on statistical models of how variance in reading skill is parceled across measures of sensory encoding, phonological processing and decision-making, our results challenge the notion that a unifying deficit characterizes dyslexia. Instead, these findings indicate a model where reading skill is explained by several distinct, additive predictors, or risk factors, of reading (dis)ability.\n\nResearch HighlightsO_LIOur research provides direct evidence that a single-mechanism, or core-deficit, model of dyslexia cannot account for the range of linguistic and sensory outcomes in children.\nC_LIO_LIIndividual differences in visual motion processing, perceptual decision making, phonological awareness and rapid naming each account for unique variance in reading skill.\nC_LIO_LIOur data support an additive risk-factor model, in which multiple independent dimensions each confer risk for reading difficulties.\nC_LI

neuroscience↗

bioRxiv2018

Parallel spatial channels converge at a bottleneck in anterior word-selective cortex

In most environments, the visual system is confronted with many relevant objects simultaneously. That is especially true during reading. However, behavioral data demonstrate that a serial bottleneck prevents recognition of more than one word at a time. We used functional magnetic resonance imaging (fMRI) to investigate how parallel spatial channels of visual processing converge into a serial bottleneck for word recognition. Observers viewed pairs of words presented simultaneously. We found that retinotopic cortex processed the two words in parallel spatial channels, one in each contralateral hemisphere. Responses were higher for attended words than ignored words, but were not reduced when attention was divided, even though behavioral performance suffered greatly. We then analyzed two word-selective regions along the occipito-temporal sulcus (OTS) of both hemispheres (i.e., sub-regions of visual word form area, VWFA). Unlike retinotopic cortex, each word-selective region responded to words on both sides of fixation. Nonetheless, a single region in the left hemisphere (VWFA-1 in posterior OTS) contained spatial channels for both hemifields that were independently modulated by selective attention. Thus, the left posterior VWFA supports parallel processing of multiple words. In contrast, a more anterior word-selective region in the left hemisphere (VWFA-2 in mid-OTS) showed limited spatial and attentional selectivity, consistent with activity of a single channel. Therefore, the visual system can process two words in parallel up to a late stage in the ventral stream. The transition from two parallel channels to a single channel in more anterior regions is consistent with the observed bottleneck in behavior.

neuroscience↗

bioRxiv2018

Applying biophysical models to understand the role of white matter in cognitive development

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular biophysical models to characterize axonal properties. We first assess the scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences in white matter that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon density in poor readers. Both models support this interpretation, highlighting the utility of biophysical models for testing specific hypotheses about cognitive development.

neuroscience↗

bioRxiv2018

Sensitive periods for white matter plasticity in human

As a child matures, some brain circuits stabilize while others remain plastic. However, the literature on maturational changes in the brains capacity for experience-dependent plasticity is primarily based on experiments in animals that mature over dramatically different time-scales than humans. Moreover, while principles of plasticity for sensory and motor systems might be conserved across species, the myriad of late-developing and uniquely human cognitive functions such as literacy cannot be studied with animal models. Here we use an intensive reading intervention program, in combination with longitudinal diffusion MRI measurements in school-aged children with dyslexia, to investigate the sensitive period for white matter plasticity and literacy learning. We find that the intervention induces large-scale changes in white matter diffusion properties, and improvements in reading scores, but that the magnitude and time-course of plasticity does not depend on the subjects age. Thus, we conclude that, for the intensive, one-on-one reading intervention program employed here, if a sensitive period exists, it does not end before middle school.

neuroscience↗

bioRxiv2018

Investigating the relationship between phoneme categorization and reading ability

Dyslexia is associated with abnormal performance on many auditory psychophysics tasks, particularly those involving the categorization of speech sounds. However, it is debated whether those apparent auditory deficits arise from (a) reduced sensitivity to particular acoustic cues, (b) the difficulty of experimental tasks, or (c) unmodeled lapses of attention. Here we investigate the relationship between phoneme categorization and reading ability, with special attention to the nature of the cue encoding the phoneme contrast (static versus dynamic), differences in task paradigm difficulty, and methodological details of psychometric model fitting. We find a robust relationship between reading ability and categorization performance, show that task difficulty cannot fully explain that relationship, and provide evidence that the deficit is not restricted to dynamic cue contrasts, contrary to prior reports. Finally, we demonstrate that improved modeling of behavioral responses suggests that the gap between dyslexics and typical readers may be smaller than previously reported.

animal behavior and cognition↗

bioRxiv2018

Word selectivity in high-level visual cortex and reading skill

Word-selective neural responses in human ventral occipito-temporal cortex (VOTC) emerge as children learn to read, creating a visual word form area (VWFA) in the literate brain. It has been suggested that the VWFA arises through competition between pre-existing selectivity for other stimulus categories, changing the topography of VOTC to support rapid word recognition. Here, we hypothesized that competition between words and objects would be resolved as children acquire reading skill. Using functional magnetic resonance imaging (fMRI), we examined the relationship between responses to words and objects in VOTC in two ways. First, we defined the VWFA using a words > objects contrast and found that only skilled readers had a region that responded more to words than objects. Second, we defined the VWFA using a words > faces contrast and examined selectivity for words over objects in this region. We found that word selectivity strongly correlated with reading skill, suggesting reading skill-dependent tuning for words. Furthermore, we found that low word selectivity in struggling readers was not due to a lack of response to words, but to a high response to objects. Our results suggest that the fine-tuning of word-selective responses in VOTC is a critical component of skilled reading.

neuroscience↗

bioRxiv2017

AFQ-Browser: Supporting reproducible human neuroscience research through browser-based visualization tools

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is an important component of reproducible research, it is not always clear how to usefully share data in a manner that allows other labs to understand and reproduce published findings. Here we describe a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a published diffusion MRI study. Because AFQ-browser is portable -- it runs in any modern web-browser -- it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of a diffusion MRI dataset (anatomy, quantitative diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new scientific discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, sharing and aggregating data across labs, and publishing data alongside manuscripts.

neuroscience↗