Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method

As the largest human cerebellar nucleus, the dentate nucleus (DN) functions significantly in the communication between the cerebellum and the rest of the brain. Structural connectivity-based parcellation has the potential to reveal the topography of the DN and enable the study of its subregions. In this paper, we investigate a deep nonnegative matrix factorization clustering method (DNMFC) for parcellation of the human DN based on its structural connectivity using diffusion MRI tractography. We propose to describe the connectivity of the DN using a set of curated tractography fiber clusters within the cerebellum. Experiments are conducted on the diffusion MRI data of 50 healthy adults from the Human Connectome Project. In comparison with state-of-the-art clustering methods, DN parcellations resulting from DNMFC show better quality and consistency of parcels across subjects

Direct and indirect contributions of executive function to word decoding and reading comprehension in kindergarten

Extant research is increasingly recognizing the contribution of executive function (EF) to reading comprehension alongside established predictors like word decoding and oral language. The nature of the association between EF and reading comprehension is commonly investigated in older children and in those with reading impairments. However, less is known about this relationship in emerging readers in kindergarten, where word decoding and reading comprehension are highly intertwined. Moreover, a better understanding of the mechanisms by which EF influences reading comprehension is needed. The present study investigated direct contributions of EF to reading comprehension, as well as indirect contributions via word decoding in 97 kindergarteners. Results indicated that there was a significant indirect effect of EF on reading comprehension, with word decoding mediating this association. The direct contribution of EF to reading comprehension was not significant. Implications for reading instruction and intervention for early readers are discussed.NICHDUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) [R01HD078351, R01HD086168, R01HD096261, R01HD094834, P50HD052120]; National Science FoundationNational Science Foundation (NSF) [NSF-1540854]; University of California Office of the President Multicampus Research Programs and Initiatives AwardUniversity of California System [MRP-17-454925]; Oak Foundation [ORIO-16-012, OCAY-19-215]; UCSF Dyslexia Center; Dyslexia Training Institute; The Potter Family; ALTA; San Mateo County of Education; IMBES; SfN; Hyde Park Day School; University of Chicago Laboratory Schools24 month embargo; published online: 25 September 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize pointwise tissue microstructure and positional information from all points within a fiber tract. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, we propose a Critical Region Localization algorithm to identify highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. The localized critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.Comment: 28 pages, 7 figure

Direct and indirect contributions of executive function to word decoding and reading comprehension in kindergarten

Extant research is increasingly recognizing the contribution of executive function (EF) to reading comprehension alongside established predictors like word decoding and oral language. The nature of the association between EF and reading comprehension is commonly investigated in older children and in those with reading impairments. However, less is known about this relationship in emerging readers in kindergarten, where word decoding and reading comprehension are highly intertwined. Moreover, a better understanding of the mechanisms by which EF influences reading comprehension is needed. The present study investigated direct contributions of EF to reading comprehension, as well as indirect contributions via word decoding in 97 kindergarteners. Results indicated that there was a significant indirect effect of EF on reading comprehension, with word decoding mediating this association. The direct contribution of EF to reading comprehension was not significant. Implications for reading instruction and intervention for early readers are discussed.NICHDUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) [R01HD078351, R01HD086168, R01HD096261, R01HD094834, P50HD052120]; National Science FoundationNational Science Foundation (NSF) [NSF-1540854]; University of California Office of the President Multicampus Research Programs and Initiatives AwardUniversity of California System [MRP-17-454925]; Oak Foundation [ORIO-16-012, OCAY-19-215]; UCSF Dyslexia Center; Dyslexia Training Institute; The Potter Family; ALTA; San Mateo County of Education; IMBES; SfN; Hyde Park Day School; University of Chicago Laboratory Schools24 month embargo; published online: 25 September 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Language combinations of multilinguals are reflected in their first-language knowledge and processing

Abstract Consequences of multilingualism vary from offering cognitive benefits to poor educational and cognitive outcomes. One aspect of multilingualism that has not been systematically examined is the typology of multilinguals' languages: Do differences and similarities between languages multilinguals are exposed to contribute to the development of their cognition and brain? We investigated n = 162 5–6-year-olds with various language backgrounds on a monolingual-to-quintilingual continuum. Our results show that typological linguistic diversity can be related to expressive vocabulary knowledge in the dominant language. On neural level, it relates to brain activation patterns in (among others) the PGa area in the bilateral IPL, a brain region previously associated with multilingual experience, but never with language typology. We propose an ecologically valid way of describing the continuum of multilingual language experience and provide evidence for both the cognition and the brain of multilingual kindergartners to be related to the typological linguistic diversity of their environment

Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro-oncology surgical planning

Background: Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps. Methods: Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm2. For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type. Results: A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin. Conclusion: We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning