Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

White matter tractography of the neural network for speech-motor control in children who stutter.

ABSTRACT: Stuttering is a neurodevelopmental speech disorder with a phenotype characterized by speech sound repetitions, prolongations and silent blocks during speech production. Developmental stuttering affects 1% of the population and 5% of children. Neuroanatomical abnormalities in the major white matter tracts, including the arcuate fasciculus, corpus callosum, corticospinal, and frontal aslant tracts (FAT), are associated with the disorder in adults who stutter but are less well studied in children who stutter (CWS). We used deterministic tractography to assess the structural connectivity of the neural network for speech production in CWS and controls. CWS had higher fractional anisotropy and axial diffusivity in the right FAT than controls. Our findings support the involvement of the corticostriatal network early in persistent developmental stuttering.

SUBMITTER: Misaghi E

PROVIDER: S-EPMC5839127 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

White matter tractography of the neural network for speech-motor control in children who stutter.

Misaghi Ehsan E Zhang Zhaoran Z Gracco Vincent L VL De Nil Luc F LF Beal Deryk S DS

Neuroscience letters 20180105

Stuttering is a neurodevelopmental speech disorder with a phenotype characterized by speech sound repetitions, prolongations and silent blocks during speech production. Developmental stuttering affects 1% of the population and 5% of children. Neuroanatomical abnormalities in the major white matter tracts, including the arcuate fasciculus, corpus callosum, corticospinal, and frontal aslant tracts (FAT), are associated with the disorder in adults who stutter but are less well studied in children w ...[more]

PMID: 29309858

Similar Datasets

Neural network connectivity differences in children who stutter.

Project description:Affecting 1% of the general population, stuttering impairs the normally effortless process of speech production, which requires precise coordination of sequential movement occurring among the articulatory, respiratory, and resonance systems, all within millisecond time scales. Those afflicted experience frequent disfluencies during ongoing speech, often leading to negative psychosocial consequences. The aetiology of stuttering remains unclear; compared to other neurodevelopmental disorders, few studies to date have examined the neural bases of childhood stuttering. Here we report, for the first time, results from functional (resting state functional magnetic resonance imaging) and structural connectivity analyses (probabilistic tractography) of multimodal neuroimaging data examining neural networks in children who stutter. We examined how synchronized brain activity occurring among brain areas associated with speech production, and white matter tracts that interconnect them, differ in young children who stutter (aged 3-9 years) compared with age-matched peers. Results showed that children who stutter have attenuated connectivity in neural networks that support timing of self-paced movement control. The results suggest that auditory-motor and basal ganglia-thalamocortical networks develop differently in stuttering children, which may in turn affect speech planning and execution processes needed to achieve fluent speech motor control. These results provide important initial evidence of neurological differences in the early phases of symptom onset in children who stutter.

| S-EPMC3859219 | biostudies-literature

Anomalous white matter morphology in adults who stutter.

Project description:Developmental stuttering is now generally considered to arise from genetic determinants interacting with neurologic function. Changes within speech-motor white matter (WM) connections may also be implicated. These connections can now be studied in great detail by high-angular-resolution diffusion magnetic resonance imaging. Therefore, diffusion spectrum imaging was used to reconstruct streamlines to examine white matter connections in people who stutter (PWS) and in people who do not stutter (PWNS).WM morphology of the entire brain was assayed in 8 right-handed male PWS and 8 similarly aged right-handed male PWNS. WM was exhaustively searched using a deterministic algorithm that identifies missing or largely misshapen tracts. To be abnormal, a tract (defined as all streamlines connecting a pair of gray matter regions) was required to be at least one 3rd missing, in 7 out of 8 subjects in one group and not in the other group.Large portions of bilateral arcuate fasciculi, a heavily researched speech pathway, were abnormal in PWS. Conversely, all PWS had a prominent connection in the left temporo-striatal tract connecting frontal and temporal cortex that was not observed in PWNS.These previously unseen structural differences of WM morphology in classical speech-language circuits may underlie developmental stuttering.

| S-EPMC4675119 | biostudies-literature

Diffusion imaging of cerebral white matter in persons who stutter: evidence for network-level anomalies.

Project description:Deficits in brain white matter have been a main focus of recent neuroimaging studies on stuttering. However, no prior study has examined brain connectivity on the global level of the cerebral cortex in persons who stutter (PWS). In the current study, we analyzed the results from probabilistic tractography between regions comprising the cortical speech network. An anatomical parcellation scheme was used to define 28 speech production-related ROIs in each hemisphere. We used network-based statistic (NBS) and graph theory to analyze the connectivity patterns obtained from tractography. At the network-level, the probabilistic corticocortical connectivity from the PWS group were significantly weaker than that from persons with fluent speech (PFS). NBS analysis revealed significant components in the bilateral speech networks with negative correlations with stuttering severity. To facilitate comparison with previous studies, we also performed tract-based spatial statistics (TBSS) and regional fractional anisotropy (FA) averaging. Results from tractography, TBSS and regional FA averaging jointly highlight the importance of several regions in the left peri-Rolandic sensorimotor and premotor areas, most notably the left ventral premotor cortex (vPMC) and middle primary motor cortex, in the neuroanatomical basis of stuttering.

| S-EPMC3920071 | biostudies-literature

Mapping brain anatomical connectivity using white matter tractography.

Project description:Integration of the neural processes in the human brain is realized through interconnections that exist between different neural centers. These interconnections take place through white matter pathways. White matter tractography is currently the only available technique for the reconstruction of the anatomical connectivity in the human brain noninvasively and in vivo. The trajectory and terminations of white matter pathways are estimated from local orientations of nerve bundles. These orientations are obtained using measurements of water diffusion in the brain. In this article, the techniques for estimating fiber directions from diffusion measurements in the human brain are reviewed. Methods of white matter tractography are described, together with the current limitations of the technique, including sensitivity to image noise and partial voluming. The applications of white matter tractography to the topographical characterization of the white matter connections and the segmentation of specific white matter pathways, and corresponding functional units of gray matter, are discussed. In this context, the potential impact of white matter tractography in mapping the functional systems and subsystems in the human brain, and their interrelations, is described. Finally, the applications of white matter tractography to the study of brain disorders, including fiber tract localization in brains affected by tumors and the identification of impaired connectivity routes in neurologic and neuropsychiatric diseases, are discussed.

| S-EPMC4503207 | biostudies-other

Structural network topology revealed by white matter tractography in cannabis users: a graph theoretical analysis.

Project description:Endocannabinoid receptors modulate synaptic plasticity in the brain and may therefore impact cortical connectivity not only during development but also in response to substance abuse in later life. Such alterations may not be evident in volumetric measures utilized in brain imaging, but could affect the local and global organization of brain networks. To test this hypothesis, we used a novel computational approach to estimate network measures of structural brain connectivity derived from diffusion tensor imaging (DTI) and white matter tractography. Twelve adult cannabis (CB) users and 13 healthy subjects were evaluated using a graph theoretic analysis of both global and local brain network properties. Structural brain networks in both CB subjects and controls exhibited robust small-world network attributes in both groups. However, CB subjects showed significantly decreased global network efficiency and significantly increased clustering coefficients (degree to which nodes tend to cluster around individual nodes). CB subjects also exhibited altered patterns of local network organization in the cingulate region. Among all subjects, schizotypal and impulsive personality characteristics correlated with global efficiency but not with the clustering coefficient. Our data indicate that structural brain networks in CB subjects are less efficiently integrated and exhibit altered regional connectivity. These differences in network properties may reflect physiological processes secondary to substance abuse-induced synaptic plasticity, or differences in brain organization that increase vulnerability to substance use.

| S-EPMC3604771 | biostudies-literature

Hemodynamics of speech production: An fNIRS investigation of children who stutter.

Project description:Stuttering affects nearly 1% of the population worldwide and often has life-altering negative consequences, including poorer mental health and emotional well-being, and reduced educational and employment achievements. Over two decades of neuroimaging research reveals clear anatomical and physiological differences in the speech neural networks of adults who stutter. However, there have been few neurophysiological investigations of speech production in children who stutter. Using functional near-infrared spectroscopy (fNIRS), we examined hemodynamic responses over neural regions integral to fluent speech production including inferior frontal gyrus, premotor cortex, and superior temporal gyrus during a picture description task. Thirty-two children (16 stuttering and 16 controls) aged 7-11 years participated in the study. We found distinctly different speech-related hemodynamic responses in the group of children who stutter compared to the control group. Whereas controls showed significant activation over left dorsal inferior frontal gyrus and left premotor cortex, children who stutter exhibited deactivation over these left hemisphere regions. This investigation of neural activation during natural, connected speech production in children who stutter demonstrates that in childhood stuttering, atypical functional organization for speech production is present and suggests promise for the use of fNIRS during natural speech production in future research with typical and atypical child populations.

| S-EPMC5481456 | biostudies-literature

Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study.

Project description:Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, "tractography") approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template-using high-dimensional, non-linear warping methods-we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.

| S-EPMC4630538 | biostudies-literature

Impaired processing speed in categorical perception: Speech perception of children who stutter.

Project description:There have been controversial debates across multiple disciplines regarding the underlying mechanism of developmental stuttering. Stuttering is often related to issues in the speech production system; however, the presence and extent of a speech perception deficit is less clear. This study aimed to investigate the speech perception of children who stutter (CWS) using the categorical perception paradigm to examine their ability to categorize different acoustic variations of speech sounds into the same or different phonemic categories. In this study, 15 CWS and 16 children who do not stutter (CWNS) completed identification and discrimination tasks involving acoustic variations of Cantonese speech sounds in three stimulus contexts: consonants (voice onset times, VOTs), lexical tones, and vowels. The results showed similar categorical perception performance in boundary position and width in the identification task and similar d' scores in the discrimination task between the CWS and CWNS groups. However, the reaction times (RTs) were slower in the CWS group compared with the CWNS group in both tasks. Moreover, the CWS group had slower RTs in identifying stimuli located across categorical boundaries compared with stimuli located away from categorical boundaries. Overall, the data implied that the phoneme representation evaluated in speech perception might be intact in CWS as revealed by similar patterns in categorical perception as those in CWNS. However, the CWS group had slower processing speeds during categorical perception, which may indicate an insufficiency in accessing the phonemic representations in a timely manner, especially when the acoustic stimuli were ambiguous.

| S-EPMC6485773 | biostudies-literature

Diffusion Tensor Imaging Tractography Reveals Disrupted White Matter Structural Connectivity Network in Healthy Adults with Insomnia Symptoms.

Project description:Neuroimaging studies have revealed that insomnia is characterized by aberrant neuronal connectivity in specific brain regions, but the topological disruptions in the white matter (WM) structural connectivity networks remain largely unknown in insomnia. The current study uses diffusion tensor imaging (DTI) tractography to construct the WM structural networks and graph theory analysis to detect alterations of the brain structural networks. The study participants comprised 30 healthy subjects with insomnia symptoms (IS) and 62 healthy subjects without IS. Both the two groups showed small-world properties regarding their WM structural connectivity networks. By contrast, increased local efficiency and decreased global efficiency were identified in the IS group, indicating an insomnia-related shift in topology away from regular networks. In addition, the IS group exhibited disrupted nodal topological characteristics in regions involving the fronto-limbic and the default-mode systems. To our knowledge, this is the first study to explore the topological organization of WM structural network connectivity in insomnia. More importantly, the dysfunctions of large-scale brain systems including the fronto-limbic pathways, salience network and default-mode network in insomnia were identified, which provides new insights into the insomnia connectome. Topology-based brain network analysis thus could be a potential biomarker for IS.

| S-EPMC5715269 | biostudies-literature

Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation.

Project description:White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.

| S-EPMC7483951 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data