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Kenyon KH, Boonstra F, Noffs G, Morgan AT, Vogel AP, Kolbe S, Van Der Walt A. The characteristics and reproducibility of motor speech functional neuroimaging in healthy controls. Front Hum Neurosci 2024; 18:1382102. [PMID: 39171097 PMCID: PMC11335534 DOI: 10.3389/fnhum.2024.1382102] [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: 02/05/2024] [Accepted: 07/22/2024] [Indexed: 08/23/2024] Open
Abstract
Introduction Functional magnetic resonance imaging (fMRI) can improve our understanding of neural processes subserving motor speech function. Yet its reproducibility remains unclear. This study aimed to evaluate the reproducibility of fMRI using a word repetition task across two time points. Methods Imaging data from 14 healthy controls were analysed using a multi-level general linear model. Results Significant activation was observed during the task in the right hemispheric cerebellar lobules IV-V, right putamen, and bilateral sensorimotor cortices. Activation between timepoints was found to be moderately reproducible across time in the cerebellum but not in other brain regions. Discussion Preliminary findings highlight the involvement of the cerebellum and connected cerebral regions during a motor speech task. More work is needed to determine the degree of reproducibility of speech fMRI before this could be used as a reliable marker of changes in brain activity.
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Affiliation(s)
- Katherine H. Kenyon
- Department of Neuroscience, School of Translational Medicine, Melbourne, VIC, Australia
| | - Frederique Boonstra
- Department of Neuroscience, School of Translational Medicine, Melbourne, VIC, Australia
| | - Gustavo Noffs
- Department of Neuroscience, School of Translational Medicine, Melbourne, VIC, Australia
- Redenlab Inc., Melbourne, VIC, Australia
| | - Angela T. Morgan
- Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia
- Department of Audiology and Speech Pathology, Faculty of Medicine, Dentistry and Health Sciences, Melbourne School of Health Sciences, University of Melbourne, Carlton, VIC, Australia
| | - Adam P. Vogel
- Redenlab Inc., Melbourne, VIC, Australia
- Department of Audiology and Speech Pathology, Parkville, VIC, Australia
| | - Scott Kolbe
- Department of Neuroscience, School of Translational Medicine, Melbourne, VIC, Australia
| | - Anneke Van Der Walt
- Department of Neuroscience, School of Translational Medicine, Melbourne, VIC, Australia
- Department of Neurology, Royal Melbourne Hospital, Melbourne, VIC, Australia
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Seghier ML. 7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery. Eur Radiol Exp 2024; 8:73. [PMID: 38945979 PMCID: PMC11214939 DOI: 10.1186/s41747-024-00472-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 04/22/2024] [Indexed: 07/02/2024] Open
Abstract
Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.
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Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Healtcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Sibbach BM, Karim HT, Lo D, Kasibhatla N, Santini T, Weber JC, Ibrahim TS, Banihashemi L. Manual segmentation of the paraventricular nucleus of the hypothalamus and the dorsal and ventral bed nucleus of stria terminalis using multimodal 7 Tesla structural MRI: probabilistic atlases for a stress-control triad. Brain Struct Funct 2024; 229:273-283. [PMID: 37812278 PMCID: PMC10917873 DOI: 10.1007/s00429-023-02713-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023]
Abstract
The paraventricular nucleus of the hypothalamus (PVN) is uniquely capable of proximal control over autonomic and neuroendocrine stress responses, and the bed nucleus of the stria terminalis (BNST) directly modulates PVN function, as well as playing an important role in stress control itself. The dorsal BNST (dBNST) is predominantly preautonomic, while the ventral BNST (vBNST) is predominantly viscerosensory, receiving dense noradrenergic signaling. Distinguishing the dBNST and vBNST, along with the PVN, may facilitate our understanding of dynamic interactions among these regions. T1-weighted MPRAGE and high resolution gradient echo (GRE) modalities were acquired at 7T. GRE was coregistered to MPRAGE and segmentations were performed in MRIcroGL based on their Atlas of the Human Brain depictions. The dBNST, vBNST and PVN were manually segmented in 25 participants; 10 images were rated by 2 raters. These segmentations were normalized and probabilistic atlases for each region were generated in MNI space, now available as resources for future research. We found moderate-high inter-rater reliability [n = 10; Mean Dice (SD); PVN = 0.69 (0.04); dBNST = 0.77 (0.04); vBNST = 0.62 (0.04)]. Probabilistic atlases were reverse normalized into native space for six additional participants that were segmented but not included in the original 25. We also found moderate to moderate-high reliability between the probabilistic atlases and manual segmentations [n = 6; Mean Dice (SD); PVN = 0.55 (0.12); dBNST = 0.60 (0.10); vBNST = 0.47 (0.12 SD)]. By isolating these hypothalamic and BNST subregions using ultra-high field MRI modalities, more specific delineations of these regions can facilitate greater understanding of mechanisms underlying stress-related function and psychopathology.
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Affiliation(s)
- Brandon M Sibbach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Daniel Lo
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Nithya Kasibhatla
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Tales Santini
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Jessica C Weber
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Tamer S Ibrahim
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Layla Banihashemi
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
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Khanna AR, Muñoz W, Kim YJ, Kfir Y, Paulk AC, Jamali M, Cai J, Mustroph ML, Caprara I, Hardstone R, Mejdell M, Meszéna D, Zuckerman A, Schweitzer J, Cash S, Williams ZM. Single-neuronal elements of speech production in humans. Nature 2024; 626:603-610. [PMID: 38297120 PMCID: PMC10866697 DOI: 10.1038/s41586-023-06982-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/14/2023] [Indexed: 02/02/2024]
Abstract
Humans are capable of generating extraordinarily diverse articulatory movement combinations to produce meaningful speech. This ability to orchestrate specific phonetic sequences, and their syllabification and inflection over subsecond timescales allows us to produce thousands of word sounds and is a core component of language1,2. The fundamental cellular units and constructs by which we plan and produce words during speech, however, remain largely unknown. Here, using acute ultrahigh-density Neuropixels recordings capable of sampling across the cortical column in humans, we discover neurons in the language-dominant prefrontal cortex that encoded detailed information about the phonetic arrangement and composition of planned words during the production of natural speech. These neurons represented the specific order and structure of articulatory events before utterance and reflected the segmentation of phonetic sequences into distinct syllables. They also accurately predicted the phonetic, syllabic and morphological components of upcoming words and showed a temporally ordered dynamic. Collectively, we show how these mixtures of cells are broadly organized along the cortical column and how their activity patterns transition from articulation planning to production. We also demonstrate how these cells reliably track the detailed composition of consonant and vowel sounds during perception and how they distinguish processes specifically related to speaking from those related to listening. Together, these findings reveal a remarkably structured organization and encoding cascade of phonetic representations by prefrontal neurons in humans and demonstrate a cellular process that can support the production of speech.
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Affiliation(s)
- Arjun R Khanna
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - William Muñoz
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Yoav Kfir
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mohsen Jamali
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jing Cai
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Irene Caprara
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Richard Hardstone
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mackenna Mejdell
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Domokos Meszéna
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jeffrey Schweitzer
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.
- Harvard Medical School, Program in Neuroscience, Boston, MA, USA.
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Peterson M, Braga RM, Floris DL, Nielsen JA. Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.08.570817. [PMID: 38106130 PMCID: PMC10723397 DOI: 10.1101/2023.12.08.570817] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of Investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.
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Affiliation(s)
- Madeline Peterson
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
| | - Rodrigo M. Braga
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Dorothea L. Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Jared A. Nielsen
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
- Neuroscience Center, Brigham Young University, Provo, UT, 84604, USA
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Karpychev V, Malyutina S, Zhuravleva A, Bronov O, Kuzin V, Marinets A, Dragoy O. Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis. Epilepsy Behav 2023; 147:109407. [PMID: 37688840 DOI: 10.1016/j.yebeh.2023.109407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/03/2023] [Accepted: 08/19/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE. METHODS We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity. RESULTS The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE. SIGNIFICANCE Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.
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Affiliation(s)
- Victor Karpychev
- Center for Language and Brain, HSE University, Moscow, Russian Federation.
| | - Svetlana Malyutina
- Center for Language and Brain, HSE University, Moscow, Russian Federation
| | - Anna Zhuravleva
- Center for Language and Brain, HSE University, Moscow, Russian Federation
| | - Oleg Bronov
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Vasiliy Kuzin
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Aleksei Marinets
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russian Federation; Institute of Linguistics, Russian Academy of Sciences, Moscow, Russian Federation
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Karpychev V, Bolgina T, Malytina S, Zinchenko V, Ushakov V, Ignatyev G, Dragoy O. Greater volumes of a callosal sub-region terminating in posterior language-related areas predict a stronger degree of language lateralization: A tractography study. PLoS One 2022; 17:e0276721. [PMID: 36520829 PMCID: PMC9754228 DOI: 10.1371/journal.pone.0276721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/13/2022] [Indexed: 12/23/2022] Open
Abstract
Language lateralization is the most intriguing trait of functional asymmetry for cognitive functions. Nowadays, ontogenetic determinants of this trait are largely unknown, but there are efforts to find its anatomical correlates. In particular, a white matter interhemispheric connection-the corpus callosum-has been proposed as such. In the present study, we aimed to find the association between the degree of language lateralization and metrics of the callosal sub-regions. We applied a sentence completion fMRI task to measure the degree of language lateralization in a group of healthy participants balanced for handedness. We obtained the volumes and microstructural properties of callosal sub-regions with two tractography techniques, diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD). The analysis of DTI-based metrics did not reveal any significant associations with language lateralization. In contrast, CSD-based analysis revealed that the volumes of a callosal sub-region terminating in the core posterior language-related areas predict a stronger degree of language lateralization. This finding supports the specific inhibitory model implemented through the callosal fibers projecting into the core posterior language-related areas in the degree of language lateralization, with no relevant contribution of other callosal sub-regions.
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Affiliation(s)
| | | | | | - Victoria Zinchenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Department of Health, Moscow, Russia
| | - Vadim Ushakov
- National Research Center “Kurchatov Institute”, Moscow, Russia
- Institute for Advanced Brain Studies, Lomonosov Moscow State University, Moscow, Russia
| | | | - Olga Dragoy
- HSE University, Moscow, Russia
- Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
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