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Chiang H, Mudar RA, Dugas CS, Motes MA, Kraut MA, Hart J. A modified neural circuit framework for semantic memory retrieval with implications for circuit modulation to treat verbal retrieval deficits. Brain Behav 2024; 14:e3490. [PMID: 38680077 PMCID: PMC11056716 DOI: 10.1002/brb3.3490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/23/2024] [Accepted: 04/03/2024] [Indexed: 05/01/2024] Open
Abstract
Word finding difficulty is a frequent complaint in older age and disease states, but treatment options are lacking for such verbal retrieval deficits. Better understanding of the neurophysiological and neuroanatomical basis of verbal retrieval function may inform effective interventions. In this article, we review the current evidence of a neural retrieval circuit central to verbal production, including words and semantic memory, that involves the pre-supplementary motor area (pre-SMA), striatum (particularly caudate nucleus), and thalamus. We aim to offer a modified neural circuit framework expanded upon a memory retrieval model proposed in 2013 by Hart et al., as evidence from electrophysiological, functional brain imaging, and noninvasive electrical brain stimulation studies have provided additional pieces of information that converge on a shared neural circuit for retrieval of memory and words. We propose that both the left inferior frontal gyrus and fronto-polar regions should be included in the expanded circuit. All these regions have their respective functional roles during verbal retrieval, such as selection and inhibition during search, initiation and termination of search, maintenance of co-activation across cortical regions, as well as final activation of the retrieved information. We will also highlight the structural connectivity from and to the pre-SMA (e.g., frontal aslant tract and fronto-striatal tract) that facilitates communication between the regions within this circuit. Finally, we will discuss how this circuit and its correlated activity may be affected by disease states and how this circuit may serve as a novel target engagement for neuromodulatory treatment of verbal retrieval deficits.
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Affiliation(s)
- Hsueh‐Sheng Chiang
- Department of NeurologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonTexasUSA
| | - Raksha A. Mudar
- Department of Speech and Hearing ScienceUniversity of Illinois Urbana‐ChampaignChampaignIllinoisUSA
| | - Christine S. Dugas
- School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonTexasUSA
| | - Michael A. Motes
- School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonTexasUSA
| | - Michael A. Kraut
- Department of Radiology and Radiological ScienceJohns Hopkins UniversityBaltimoreMarylandUSA
| | - John Hart
- Department of NeurologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonTexasUSA
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2
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Bulut T, Hagoort P. Contributions of the left and right thalami to language: A meta-analytic approach. Brain Struct Funct 2024:10.1007/s00429-024-02795-3. [PMID: 38625556 DOI: 10.1007/s00429-024-02795-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 03/25/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Despite a pervasive cortico-centric view in cognitive neuroscience, subcortical structures including the thalamus have been shown to be increasingly involved in higher cognitive functions. Previous structural and functional imaging studies demonstrated cortico-thalamo-cortical loops which may support various cognitive functions including language. However, large-scale functional connectivity of the thalamus during language tasks has not been examined before. METHODS The present study employed meta-analytic connectivity modeling to identify language-related coactivation patterns of the left and right thalami. The left and right thalami were used as regions of interest to search the BrainMap functional database for neuroimaging experiments with healthy participants reporting language-related activations in each region of interest. Activation likelihood estimation analyses were then carried out on the foci extracted from the identified studies to estimate functional convergence for each thalamus. A functional decoding analysis based on the same database was conducted to characterize thalamic contributions to different language functions. RESULTS The results revealed bilateral frontotemporal and bilateral subcortical (basal ganglia) coactivation patterns for both the left and right thalami, and also right cerebellar coactivations for the left thalamus, during language processing. In light of previous empirical studies and theoretical frameworks, the present connectivity and functional decoding findings suggest that cortico-subcortical-cerebellar-cortical loops modulate and fine-tune information transfer within the bilateral frontotemporal cortices during language processing, especially during production and semantic operations, but also other language (e.g., syntax, phonology) and cognitive operations (e.g., attention, cognitive control). CONCLUSION The current findings show that the language-relevant network extends beyond the classical left perisylvian cortices and spans bilateral cortical, bilateral subcortical (bilateral thalamus, bilateral basal ganglia) and right cerebellar regions.
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Affiliation(s)
- Talat Bulut
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
- Department of Speech and Language Therapy, School of Health Sciences, Istanbul Medipol University, Istanbul, Turkey.
| | - Peter Hagoort
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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3
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Neef NE, Chang SE. Knowns and unknowns about the neurobiology of stuttering. PLoS Biol 2024; 22:e3002492. [PMID: 38386639 PMCID: PMC10883586 DOI: 10.1371/journal.pbio.3002492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Abstract
Stuttering occurs in early childhood during a dynamic phase of brain and behavioral development. The latest studies examining children at ages close to this critical developmental period have identified early brain alterations that are most likely linked to stuttering, while spontaneous recovery appears related to increased inter-area connectivity. By contrast, therapy-driven improvement in adults is associated with a functional reorganization within and beyond the speech network. The etiology of stuttering, however, remains enigmatic. This Unsolved Mystery highlights critical questions and points to neuroimaging findings that could inspire future research to uncover how genetics, interacting neural hierarchies, social context, and reward circuitry contribute to the many facets of stuttering.
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Affiliation(s)
- Nicole E. Neef
- Institute for Diagnostic and Interventional Neuroradiology, University Medical Center Göttingen, Göttingen, Germany
| | - Soo-Eun Chang
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Communication Disorders, Ewha Womans University, Seoul, Korea
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Matsuhashi K, Itahashi T, Aoki R, Hashimoto RI. Meta-analysis of structural integrity of white matter and functional connectivity in developmental stuttering. Brain Res Bull 2023; 205:110827. [PMID: 38013029 DOI: 10.1016/j.brainresbull.2023.110827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 11/29/2023]
Abstract
Developmental stuttering is a speech disfluency disorder characterized by repetitions, prolongations, and blocks of speech. While a number of neuroimaging studies have identified alterations in localized brain activation during speaking in persons with stuttering (PWS), it is unclear whether neuroimaging evidence converges on alterations in structural integrity of white matter and functional connectivity (FC) among multiple regions involved in supporting fluent speech. In the present study, we conducted coordinate-based meta-analyses according to the PRISMA guidelines for available publications that studied fractional anisotropy (FA) using tract-based spatial statistics (TBSS) for structural integrity and the seed-based voxel-wise FC analyses. The search retrieved 11 publications for the TBSS FA studies, 29 seed-based FC datasets from 6 publications for the resting-state, and 29 datasets from 6 publications for the task-based studies. The meta-analysis of TBSS FA revealed that PWS exhibited FA reductions in the middle and posterior segments of the left superior longitudinal fasciculus. Furthermore, the analysis of resting-state FC demonstrated that PWS had reduced FC in the right supplementary motor area and inferior parietal cortex, whereas an increase in FC was observed in the left cerebellum crus I. Conversely, we observed increased FC for task-based FC in regions implicated in speech production or sequential movements, including the anterior cingulate cortex, posterior insula, and bilateral cerebellum crus I in PWS. Functional network characterization of the altered FCs revealed that the sets of reduced resting-state and increased task-based FCs were largely distinct, but the somatomotor and striatum/thalamus networks were foci of alterations in both conditions. These observations indicate that developmental stuttering is characterized by structural and functional alterations in multiple brain networks that support speech fluency or sequential motor processes, including cortico-cortical and subcortical connections.
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Affiliation(s)
- Kengo Matsuhashi
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ryuta Aoki
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan; Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
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5
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Miller HE, Garnett EO, Heller Murray ES, Nieto-Castañón A, Tourville JA, Chang SE, Guenther FH. A comparison of structural morphometry in children and adults with persistent developmental stuttering. Brain Commun 2023; 5:fcad301. [PMID: 38025273 PMCID: PMC10653153 DOI: 10.1093/braincomms/fcad301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 12/01/2023] Open
Abstract
This cross-sectional study aimed to differentiate earlier occurring neuroanatomical differences that may reflect core deficits in stuttering versus changes associated with a longer duration of stuttering by analysing structural morphometry in a large sample of children and adults who stutter and age-matched controls. Whole-brain T1-weighted structural scans were obtained from 166 individuals who stutter (74 children, 92 adults; ages 3-58) and 191 controls (92 children, 99 adults; ages 3-53) from eight prior studies in our laboratories. Mean size and gyrification measures were extracted using FreeSurfer software for each cortical region of interest. FreeSurfer software was also used to generate subcortical volumes for regions in the automatic subcortical segmentation. For cortical analyses, separate ANOVA analyses of size (surface area, cortical thickness) and gyrification (local gyrification index) measures were conducted to test for a main effect of diagnosis (stuttering, control) and the interaction of diagnosis-group with age-group (children, adults) across cortical regions. Cortical analyses were first conducted across a set of regions that comprise the speech network and then in a second whole-brain analysis. Next, separate ANOVA analyses of volume were conducted across subcortical regions in each hemisphere. False discovery rate corrections were applied for all analyses. Additionally, we tested for correlations between structural morphometry and stuttering severity. Analyses revealed thinner cortex in children who stutter compared with controls in several key speech-planning regions, with significant correlations between cortical thickness and stuttering severity. These differences in cortical size were not present in adults who stutter, who instead showed reduced gyrification in the right inferior frontal gyrus. Findings suggest that early cortical anomalies in key speech planning regions may be associated with stuttering onset. Persistent stuttering into adulthood may result from network-level dysfunction instead of focal differences in cortical morphometry. Adults who stutter may also have a more heterogeneous neural presentation than children who stutter due to their unique lived experiences.
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Affiliation(s)
- Hilary E Miller
- Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Emily O Garnett
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elizabeth S Heller Murray
- Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA
- Department of Communication Sciences & Disorders, Temple University, Philadelphia, PA 19122, USA
| | - Alfonso Nieto-Castañón
- Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Jason A Tourville
- Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Soo-Eun Chang
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Communication Disorders, Ewha Womans University, Seoul 03760, Korea
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI 48824, USA
| | - Frank H Guenther
- Department of Speech, Language, & Hearing Sciences, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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6
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Asci F, Marsili L, Suppa A, Saggio G, Michetti E, Di Leo P, Patera M, Longo L, Ruoppolo G, Del Gado F, Tomaiuoli D, Costantini G. Acoustic analysis in stuttering: a machine-learning study. Front Neurol 2023; 14:1169707. [PMID: 37456655 PMCID: PMC10347393 DOI: 10.3389/fneur.2023.1169707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Background Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS). Objective We assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine - SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering. Methods Fifty-three PWS (20 children, 33 younger adults) and 71 age-/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN). Results Acoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings. Conclusion Acoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment).
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Affiliation(s)
- Francesco Asci
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, Italy
| | - Luca Marsili
- Department of Neurology, James J. and Joan A. Gardner Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, United States
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, Italy
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | | | - Pietro Di Leo
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Martina Patera
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Lucia Longo
- Department of Sense Organs, Otorhinolaryngology Section, Sapienza University of Rome, Rome, Italy
| | | | | | | | - Giovanni Costantini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
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7
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Cheng L, Zhan L, Huang L, Zhang H, Sun J, Huang G, Wang Y, Li M, Li H, Gao Y, Jia X. The atypical functional connectivity of Broca's area at multiple frequency bands in autism spectrum disorder. Brain Imaging Behav 2022; 16:2627-2636. [PMID: 36163448 DOI: 10.1007/s11682-022-00718-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2022] [Indexed: 11/30/2022]
Abstract
As a developmental disorder, autism spectrum disorder (ASD) has drawn much attention due to its severe impacts on one's language capacity. Broca's area, an important brain region of the language network, is largely involved in language-related functions. Using the Autism Brain Image Data Exchange (ABIDE) dataset, a mega-analysis was performed involving a total of 1454 participants (including 618 individuals with ASD and 836 healthy controls (HCs). To detect the neural pathophysiological mechanism of ASD from the perspective of language, we conducted a functional connectivity (FC) analysis with Broca's area as the seed in multiple frequency bands (conventional: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz). We found that compared with HC, ASD patients demonstrated increased FC in the left thalamus, left precuneus, left anterior cingulate and paracingulate gyri, and left medial orbital of the superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The results of the slow-5 frequency band (0.01-0.027 Hz) presented increased FC values of the left precuneus, left medial orbital of the superior frontal gyrus, right medial orbital of the superior frontal gyrus and right thalamus. No significant cluster was detected in the slow-4 frequency band (0.027-0.073 Hz). In conclusion, the abnormal functional connectivity in patients with ASD has frequency-specific properties. Furthermore, the slow-5 frequency band (0.01-0.027 Hz) mainly contributed to the findings of the conventional frequency band (0.01-0.08 Hz). The current study might shed new light on the neural pathophysiological mechanism of language impairments in people with ASD.
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Affiliation(s)
- Lulu Cheng
- School of Foreign Studies, China University of Petroleum (East China), Qingdao, 266580, China.,Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Hongqiang Zhang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China. .,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China. .,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
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8
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G D, B H S, Gajbe U, Singh BR, Sawal A, Balwir T. The Role of Basal Ganglia and Its Neuronal Connections in the Development of Stuttering: A Review Article. Cureus 2022; 14:e28653. [PMID: 36196326 PMCID: PMC9525748 DOI: 10.7759/cureus.28653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/31/2022] [Indexed: 11/05/2022] Open
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9
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Reinvestigating the Neural Bases Involved in Speech Production of Stutterers: An ALE Meta-Analysis. Brain Sci 2022; 12:brainsci12081030. [PMID: 36009093 PMCID: PMC9406059 DOI: 10.3390/brainsci12081030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Stuttering is characterized by dysfluency and difficulty in speech production. Previous research has found abnormalities in the neural function of various brain areas during speech production tasks. However, the cognitive neural mechanism of stuttering has still not been fully determined. Method: Activation likelihood estimation analysis was performed to provide neural imaging evidence on neural bases by reanalyzing published studies. Results: Our analysis revealed overactivation in the bilateral posterior superior temporal gyrus, inferior frontal gyrus, medial frontal gyrus, precentral gyrus, postcentral gyrus, basal ganglia, and cerebellum, and deactivation in the anterior superior temporal gyrus and middle temporal gyrus among the stutterers. The overactivated regions might indicate a greater demand in feedforward planning in speech production, while the deactivated regions might indicate dysfunction in the auditory feedback system among stutterers. Conclusions: Our findings provide updated and direct evidence on the multi-level impairment (feedforward and feedback systems) of stutterers during speech production and show that the corresponding neural bases were differentiated.
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10
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Biswas R, Shlizerman E. Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study. Front Syst Neurosci 2022; 16:817962. [PMID: 35308566 PMCID: PMC8924489 DOI: 10.3389/fnsys.2022.817962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property—an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.
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Affiliation(s)
- Rahul Biswas
- Department of Statistics, University of Washington, Seattle, WA, United States
| | - Eli Shlizerman
- Department of Applied Mathematics, Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States
- *Correspondence: Eli Shlizerman
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11
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Toyomura A, Fujii T, Sowman PF. Performance of Bimanual Finger Coordination Tasks in Speakers Who Stutter. Front Psychol 2021; 12:679607. [PMID: 34630201 PMCID: PMC8495154 DOI: 10.3389/fpsyg.2021.679607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/12/2021] [Indexed: 11/13/2022] Open
Abstract
Stuttering is a neurodevelopmental speech disorder characterized by the symptoms of speech repetition, prolongation, and blocking. Stuttering-related dysfluency can be transiently alleviated by providing an external timing signal such as a metronome or the voice of another person. Therefore, the existence of a core motor timing deficit in stuttering has been speculated. If this is the case, then motoric behaviors other than speech should be disrupted in stuttering. This study examined motoric performance on four complex bimanual tasks in 37 adults who stutter and 31 fluent controls. Two tasks utilized bimanual rotation to examine motor dexterity, and two tasks used the bimanual mirror and parallel tapping movements to examine timing control ability. Video-based analyses were conducted to determine performance accuracy and speed. The results showed that individuals who stutter performed worse than fluent speakers on tapping tasks but not on bimanual rotation tasks. These results suggest stuttering is associated with timing control for general motor behavior.
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Affiliation(s)
- Akira Toyomura
- Graduate School of Health Sciences, Gunma University, Maebashi, Japan.,Research Center for Advanced Technologies, Tokyo Denki University, Inzai, Japan
| | | | - Paul F Sowman
- School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
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12
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Busan P. Developmental stuttering and the role of the supplementary motor cortex. JOURNAL OF FLUENCY DISORDERS 2020; 64:105763. [PMID: 32361030 DOI: 10.1016/j.jfludis.2020.105763] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 04/05/2020] [Accepted: 04/08/2020] [Indexed: 06/11/2023]
Abstract
Developmental stuttering is a frequent neurodevelopmental disorder with a complex neurobiological basis. Robust neural markers of stuttering include imbalanced activity of speech and motor related brain regions, and their impaired structural connectivity. The dynamic interaction of cortical regions is regulated by the cortico-basal ganglia-thalamo-cortical system with the supplementary motor area constituting a crucial cortical site. The SMA integrates information from different neural circuits, and manages information about motor programs such as self-initiated movements, motor sequences, and motor learning. Abnormal functioning of SMA is increasingly reported in stuttering, and has been recently indicated as an additional "neural marker" of DS: anatomical and functional data have documented abnormal structure and activity of the SMA, especially in motor and speech networks. Its connectivity is often impaired, especially when considering networks of the left hemisphere. Compatibly, recent data suggest that, in DS, SMA is part of a poorly synchronized neural network, thus resulting in a likely substrate for the appearance of DS symptoms. However, as evident when considering neural models of stuttering, the role of SMA has not been fully clarified. Herein, the available evidence is reviewed, which highlights the role of the SMA in DS as a neural "hub", receiving and conveying altered information, thus "gating" the release of correct or abnormal motor plans.
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13
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Sares AG, Deroche MLD, Ohashi H, Shiller DM, Gracco VL. Neural Correlates of Vocal Pitch Compensation in Individuals Who Stutter. Front Hum Neurosci 2020; 14:18. [PMID: 32161525 PMCID: PMC7053555 DOI: 10.3389/fnhum.2020.00018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 01/17/2020] [Indexed: 02/06/2023] Open
Abstract
Stuttering is a disorder that impacts the smooth flow of speech production and is associated with a deficit in sensorimotor integration. In a previous experiment, individuals who stutter were able to vocally compensate for pitch shifts in their auditory feedback, but they exhibited more variability in the timing of their corrective responses. In the current study, we focused on the neural correlates of the task using functional MRI. Participants produced a vowel sound in the scanner while hearing their own voice in real time through headphones. On some trials, the audio was shifted up or down in pitch, eliciting a corrective vocal response. Contrasting pitch-shifted vs. unshifted trials revealed bilateral superior temporal activation over all the participants. However, the groups differed in the activation of middle temporal gyrus and superior frontal gyrus [Brodmann area 10 (BA 10)], with individuals who stutter displaying deactivation while controls displayed activation. In addition to the standard univariate general linear modeling approach, we employed a data-driven technique (independent component analysis, or ICA) to separate task activity into functional networks. Among the networks most correlated with the experimental time course, there was a combined auditory-motor network in controls, but the two networks remained separable for individuals who stuttered. The decoupling of these networks may account for temporal variability in pitch compensation reported in our previous work, and supports the idea that neural network coherence is disturbed in the stuttering brain.
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Affiliation(s)
- Anastasia G Sares
- Speech Motor Control Lab, Integrated Program in Neuroscience and School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada.,Centre for Research on Brain, Language, and Music, Montreal, QC, Canada
| | - Mickael L D Deroche
- Centre for Research on Brain, Language, and Music, Montreal, QC, Canada.,Laboratory for Hearing and Cognition, Department of Psychology, Concordia University, Montreal, QC, Canada
| | | | - Douglas M Shiller
- Centre for Research on Brain, Language, and Music, Montreal, QC, Canada.,École d'orthophonie et d'audiologie, Université de Montréal, Montreal, QC, Canada
| | - Vincent L Gracco
- Speech Motor Control Lab, Integrated Program in Neuroscience and School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada.,Centre for Research on Brain, Language, and Music, Montreal, QC, Canada.,Haskins Laboratories, New Haven, CT, United States
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14
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Sengupta R, Yaruss JS, Loucks TM, Gracco VL, Pelczarski K, Nasir SM. Theta Modulated Neural Phase Coherence Facilitates Speech Fluency in Adults Who Stutter. Front Hum Neurosci 2019; 13:394. [PMID: 31798431 PMCID: PMC6878001 DOI: 10.3389/fnhum.2019.00394] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 10/22/2019] [Indexed: 12/03/2022] Open
Abstract
Adults who stutter (AWS) display altered patterns of neural phase coherence within the speech motor system preceding disfluencies. These altered patterns may distinguish fluent speech episodes from disfluent ones. Phase coherence is relevant to the study of stuttering because it reflects neural communication within brain networks. In this follow-up study, the oscillatory cortical dynamics preceding fluent speech in AWS and adults who do not stutter (AWNS) were examined during a single-word delayed reading task using electroencephalographic (EEG) techniques. Compared to AWNS, fluent speech preparation in AWS was characterized by a decrease in theta-gamma phase coherence and a corresponding increase in theta-beta coherence level. Higher spectral powers in the beta and gamma bands were also observed preceding fluent utterances by AWS. Overall, there was altered neural communication during speech planning in AWS that provides novel evidence for atypical allocation of feedforward control by AWS even before fluent utterances.
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Affiliation(s)
- Ranit Sengupta
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, United States
| | - J Scott Yaruss
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States
| | - Torrey M Loucks
- Department of Communication Sciences and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada.,Institute for Stuttering Treatment and Research, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | | | - Kristin Pelczarski
- School of Family Studies and Human Services, Kansas State University, Manhattan, KS, United States
| | - Sazzad M Nasir
- Haskins Laboratories, New Haven, CT, United States.,Indiana Academy, Ball State University, Muncie, IN, United States
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15
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Garnett EO, Chow HM, Nieto-Castañón A, Tourville JA, Guenther FH, Chang SE. Anomalous morphology in left hemisphere motor and premotor cortex of children who stutter. Brain 2019; 141:2670-2684. [PMID: 30084910 DOI: 10.1093/brain/awy199] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 06/04/2018] [Indexed: 02/06/2023] Open
Abstract
Stuttering is a neurodevelopmental disorder that affects the smooth flow of speech production. Stuttering onset occurs during a dynamic period of development when children first start learning to formulate sentences. Although most children grow out of stuttering naturally, ∼1% of all children develop persistent stuttering that can lead to significant psychosocial consequences throughout one's life. To date, few studies have examined neural bases of stuttering in children who stutter, and even fewer have examined the basis for natural recovery versus persistence of stuttering. Here we report the first study to conduct surface-based analysis of the brain morphometric measures in children who stutter. We used FreeSurfer to extract cortical size and shape measures from structural MRI scans collected from the initial year of a longitudinal study involving 70 children (36 stuttering, 34 controls) in the 3-10-year range. The stuttering group was further divided into two groups: persistent and recovered, based on their later longitudinal visits that allowed determination of their eventual clinical outcome. A region of interest analysis that focused on the left hemisphere speech network and a whole-brain exploratory analysis were conducted to examine group differences and group × age interaction effects. We found that the persistent group could be differentiated from the control and recovered groups by reduced cortical thickness in left motor and lateral premotor cortical regions. The recovered group showed an age-related decrease in local gyrification in the left medial premotor cortex (supplementary motor area and and pre-supplementary motor area). These results provide strong evidence of a primary deficit in the left hemisphere speech network, specifically involving lateral premotor cortex and primary motor cortex, in persistent developmental stuttering. Results further point to a possible compensatory mechanism involving left medial premotor cortex in those who recover from childhood stuttering.
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Affiliation(s)
- Emily O Garnett
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Ho Ming Chow
- Nemours/Alfred I. DuPont Hospital for Children, Wilmington, DE, USA
| | | | - Jason A Tourville
- Department of Speech Language and Hearing Sciences, Boston University, Boston, MA, USA
| | - Frank H Guenther
- Department of Speech Language and Hearing Sciences, Boston University, Boston, MA, USA.,Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Soo-Eun Chang
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
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16
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Montag C, Bleek B, Reuter M, Müller T, Weber B, Faber J, Markett S. Ventral striatum and stuttering: Robust evidence from a case-control study applying DARTEL. Neuroimage Clin 2019; 23:101890. [PMID: 31255948 PMCID: PMC6606830 DOI: 10.1016/j.nicl.2019.101890] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/30/2019] [Accepted: 06/04/2019] [Indexed: 10/26/2022]
Abstract
A prominent theory of developmental stuttering highlights (dys-)function of the basal ganglia (and in particular the ventral striatum) as a main neural mechanism behind this speech disorder. Although the theory is intriguing, studies on gray matter volume differences in the basal ganglia between people who stutter and control persons have reported heterogeneous findings, either showing more or less gray matter volume of the aforementioned brain structure across the brain's hemispheres. Moreover, some studies did not observe any differences at all. From today's perspective several of the earlier studies are rather underpowered and also used less powerful statistical approaches to investigate differences in brain structure between people who stutter and controls. Therefore, the present study contrasted a comparably larger sample of n = 36 people who stutter with n = 34 control persons and applied the state of the art DARTEL algorithm (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) to analyze the available brain data. In the present data set stuttering was associated with higher gray matter volume of the right caudate and putamen region of the basal ganglia in patients. Our observation strongly supports a recent finding reporting a larger nucleus accumbens in the right hemisphere in people who stutter when compared to control persons. The present findings are discussed in the context of both compensatory effects of the brain and putative therapeutic effects due to treatment of stuttering.
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Affiliation(s)
- Christian Montag
- Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Germany.
| | - Benjamin Bleek
- Department of Psychology, University of Bonn, Bonn, Germany
| | - Martin Reuter
- Department of Psychology, University of Bonn, Bonn, Germany; Center for Economics and Neuroscience (CENs), University of Bonn, Bonn, Germany
| | - Thilo Müller
- Department for the Treatment of Stuttering, LVR Clinic Bonn, Bonn, Germany
| | - Bernd Weber
- Center for Economics and Neuroscience (CENs), University of Bonn, Bonn, Germany; Department for NeuroCognition, Life & Brain Center, Germany; Institute of Experimental Epileptology and Cognition Research, University Hospital of Bonn, Germany
| | - Jennifer Faber
- Department of Neurology, University Hospital Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt Universität zu Berlin, Berlin, Germany.
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17
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Jackson ES, Wijeakumar S, Beal DS, Brown B, Zebrowski P, Spencer JP. A fNIRS Investigation of Speech Planning and Execution in Adults Who Stutter. Neuroscience 2019; 406:73-85. [DOI: 10.1016/j.neuroscience.2019.02.032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 02/25/2019] [Accepted: 02/26/2019] [Indexed: 01/05/2023]
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18
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Visibility graph analysis of speech evoked auditory brainstem response in persistent developmental stuttering. Neurosci Lett 2019; 696:28-32. [DOI: 10.1016/j.neulet.2018.12.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 12/09/2018] [Accepted: 12/10/2018] [Indexed: 10/27/2022]
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19
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Busan P, Del Ben G, Russo LR, Bernardini S, Natarelli G, Arcara G, Manganotti P, Battaglini PP. Stuttering as a matter of delay in neural activation: A combined TMS/EEG study. Clin Neurophysiol 2019; 130:61-76. [DOI: 10.1016/j.clinph.2018.10.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Revised: 08/27/2018] [Accepted: 10/15/2018] [Indexed: 10/27/2022]
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20
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Qiao J, Lv Y, Cao C, Wang Z, Li A. Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis. Front Aging Neurosci 2018; 10:417. [PMID: 30618723 PMCID: PMC6304436 DOI: 10.3389/fnagi.2018.00417] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/03/2018] [Indexed: 12/11/2022] Open
Abstract
Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.
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Affiliation(s)
- Jianping Qiao
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Data Science and Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Yingru Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chongfeng Cao
- Department of Emergency, Jinan Central Hospital Affiliated to Shandong University, Jinan, China
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Anning Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
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21
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Wang Z, Yan X, Liu Y, Spray GJ, Deng Y, Cao F. Structural and functional abnormality of the putamen in children with developmental dyslexia. Neuropsychologia 2018; 130:26-37. [PMID: 30030195 DOI: 10.1016/j.neuropsychologia.2018.07.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 07/05/2018] [Accepted: 07/12/2018] [Indexed: 12/14/2022]
Abstract
There is currently debate with regards to the role of phonological deficit in Chinese reading difficulty, even though some researchers have suggested that the deficit of phonological processing is also a signature of developmental dyslexia in Chinese, as has been found in alphabetic languages. In this study, we examined the brain mechanisms of phonological deficit in Chinese children with developmental dyslexia (DD) during an auditory rhyming judgment task. First, we examined structural differences in Chinese dyslexia by comparing gray and white matter volume in Chinese children with DD, age-matched controls (AC), and reading-matched controls (RC). Next, we examined whether the regions with an abnormal volume in DD showed deficient functional connectivity with the rest of the brain during a phonological task (i.e. auditory rhyming judgment). We found that both AC and RC had greater gray matter volume (GMV) at the left putamen and right dorsal lateral frontal cortex than DD, suggesting possible neural signatures of developmental dyslexia. Functional connectivity analysis revealed that the left putamen was more connected with the right inferior occipital gyrus (IOG) in AC and RC than in DD, suggesting that automatic orthographic involvement during spoken language processing is more salient in controls, while the left putamen was more connected with the left transverse temporal gyrus (TTG) and left insula in DD than in AC and RC, suggesting the phonological articulation -auditory feedback loop is more involved in DD. These findings suggest that the reduced left putamen might contribute to phonological deficits experienced in DD, since it showed deficient connectivity with the rest of the brain during phonological processing.
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Affiliation(s)
- Zhao Wang
- Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, and Department of Psychology, Sun Yat-Sen University, Guangzhou, China; Beijing Normal University, Beijing, China; Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States
| | - Xin Yan
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States
| | - Yanni Liu
- University of Michigan, Ann Arbor, MI, United States
| | - Gregory J Spray
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States
| | - Yuan Deng
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Fan Cao
- Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, and Department of Psychology, Sun Yat-Sen University, Guangzhou, China; Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States; School of Humanities and Social Sciences, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China.
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22
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Qiao J, Li A, Cao C, Wang Z, Sun J, Xu G. Aberrant Functional Network Connectivity as a Biomarker of Generalized Anxiety Disorder. Front Hum Neurosci 2017; 11:626. [PMID: 29375339 PMCID: PMC5770732 DOI: 10.3389/fnhum.2017.00626] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 12/08/2017] [Indexed: 12/14/2022] Open
Abstract
Neural disruptions during emotion regulation are common of generalized anxiety disorder (GAD). Identifying distinct functional and effective connectivity patterns in GAD may provide biomarkers for their diagnoses. This study aims to investigate the differences of features of brain network connectivity between GAD patients and healthy controls (HC), and to assess whether those differences can serve as biomarkers to distinguish GAD from controls. Independent component analysis (ICA) with hierarchical partner matching (HPM-ICA) was conducted on resting-state functional magnetic resonance imaging data collected from 20 GAD patients with medicine-free and 20 matched HC, identifying nine highly reproducible and significantly different functional brain connectivity patterns across diagnostic groups. We then utilized Granger causality (GC) to study the effective connectivity between the regions that identified by HPM-ICA. The linear discriminant analysis was finally used to distinguish GAD from controls with these measures of neural connectivity. The GAD patients showed stronger functional connectivity in amygdala, insula, putamen, thalamus, and posterior cingulate cortex, but weaker in frontal and temporal cortex compared with controls. Besides, the effective connectivity in GAD was decreased from the cortex to amygdala and basal ganglia. Applying the ICA and GC features to the classifier led to a classification accuracy of 87.5%, with a sensitivity of 90.0% and a specificity of 85.0%. These findings suggest that the presence of emotion dysregulation circuits may contribute to the pathophysiology of GAD, and these aberrant brain features may serve as robust brain biomarkers for GAD.
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Affiliation(s)
- Jianping Qiao
- School of Physics and Electronics, Shandong Normal University, Jinan, China.,Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Shandong Normal University, Jinan, China.,Institute of Data Science and Technology, Shandong Normal University, Jinan, China
| | - Anning Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Chongfeng Cao
- Department of Emergency, Jinan Central Hospital Affiliated to Shandong University, Jinan, China
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Jiande Sun
- Institute of Data Science and Technology, Shandong Normal University, Jinan, China.,School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Guangrun Xu
- Department of Neurology, Qilu Hospital of Shandong University, Jinan, China
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