1
|
Seusing N, Strauss S, Fleischmann R, Nafz C, Groppa S, Muthuraman M, Ding H, Byblow WD, Lotze M, Grothe M. The excitability of ipsilateral motor evoked potentials is not task-specific and spatially distinct from the contralateral motor hotspot. Exp Brain Res 2024; 242:1851-1859. [PMID: 38842754 PMCID: PMC11252234 DOI: 10.1007/s00221-024-06851-6] [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: 02/04/2023] [Accepted: 05/10/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVE The role of ipsilateral descending motor pathways in voluntary movement of humans is still a matter of debate, with partly contradictory results. The aim of our study therefore was to examine the excitability of ipsilateral motor evoked potentials (iMEPs) regarding site and the specificity for unilateral and bilateral elbow flexion extension tasks. METHODS MR-navigated transcranial magnetic stimulation mapping of the dominant hemisphere was performed in twenty healthy participants during tonic unilateral (iBB), bilateral homologous (bBB) or bilateral antagonistic elbow flexion-extension (iBB-cAE), the map center of gravity (CoG) and iMEP area from BB were obtained. RESULTS The map CoG of the ipsilateral BB was located more anterior-laterally than the hotspot of the contralateral BB within the primary motor cortex, with a significant difference in CoG in iBB and iBB-cAE, but not bBB compared to the hotspot for the contralateral BB (each p < 0.05). However, different tasks had no effect on the size of the iMEPs. CONCLUSION Our data demonstrated that excitability of ipsilateral and contralateral MEP differ spatially in a task-specific manner suggesting the involvement of different motor networks within the motor cortex.
Collapse
Affiliation(s)
- Nelly Seusing
- Department of Neurology, University Medicine of Greifswald, Greifswald, Germany
| | - Sebastian Strauss
- Department of Neurology, University Medicine of Greifswald, Greifswald, Germany
| | - Robert Fleischmann
- Department of Neurology, University Medicine of Greifswald, Greifswald, Germany
| | - Christina Nafz
- Department of Neurology, University Medicine of Greifswald, Greifswald, Germany
| | - Sergiu Groppa
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
- Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Department of Neurology, University Medicine of Würzburg, Würzburg, Germany
| | - Hao Ding
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
- Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Department of Neurology, University Medicine of Würzburg, Würzburg, Germany
| | - Winston D Byblow
- Movement Neuroscience Laboratory, Department of Exercise Sciences, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Martin Lotze
- Functional Imaging Unit, Center for Diagnostic Radiology, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Grothe
- Department of Neurology, University Medicine of Greifswald, Greifswald, Germany.
| |
Collapse
|
2
|
Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024; 69:2241-2259. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [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: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
Collapse
Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| |
Collapse
|
3
|
Wen X, Yang M, Qi S, Wu X, Zhang D. Automated individual cortical parcellation via consensus graph representation learning. Neuroimage 2024; 293:120616. [PMID: 38697587 DOI: 10.1016/j.neuroimage.2024.120616] [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: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.
Collapse
Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China.
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China
| | - Xia Wu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China.
| |
Collapse
|
4
|
Lazari A, Tachrount M, Valverde JM, Papp D, Beauchamp A, McCarthy P, Ellegood J, Grandjean J, Johansen-Berg H, Zerbi V, Lerch JP, Mars RB. The mouse motor system contains multiple premotor areas and partially follows human organizational principles. Cell Rep 2024; 43:114191. [PMID: 38717901 DOI: 10.1016/j.celrep.2024.114191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/10/2023] [Accepted: 04/17/2024] [Indexed: 06/01/2024] Open
Abstract
While humans are known to have several premotor cortical areas, secondary motor cortex (M2) is often considered to be the only higher-order motor area of the mouse brain and is thought to combine properties of various human premotor cortices. Here, we show that axonal tracer, functional connectivity, myelin mapping, gene expression, and optogenetics data contradict this notion. Our analyses reveal three premotor areas in the mouse, anterior-lateral motor cortex (ALM), anterior-lateral M2 (aM2), and posterior-medial M2 (pM2), with distinct structural, functional, and behavioral properties. By using the same techniques across mice and humans, we show that ALM has strikingly similar functional and microstructural properties to human anterior ventral premotor areas and that aM2 and pM2 amalgamate properties of human pre-SMA and cingulate cortex. These results provide evidence for the existence of multiple premotor areas in the mouse and chart a comparative map between the motor systems of humans and mice.
Collapse
Affiliation(s)
- Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Mohamed Tachrount
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juan Miguel Valverde
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland
| | - Daniel Papp
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Antoine Beauchamp
- Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Joanes Grandjean
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFL, 1015 Lausanne, Switzerland; CIBM Center for Biomedical Imaging, 1015 Lausanne, Switzerland
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| |
Collapse
|
5
|
Tagliaferri M, Amorosino G, Voltolini L, Giampiccolo D, Avesani P, Cattaneo L. A revision of the dorsal origin of the frontal aslant tract (FAT) in the superior frontal gyrus: a DWI-tractographic study. Brain Struct Funct 2024; 229:987-999. [PMID: 38502328 DOI: 10.1007/s00429-024-02778-4] [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: 08/08/2023] [Accepted: 02/19/2024] [Indexed: 03/21/2024]
Abstract
The frontal aslant tract (FAT) is a white matter tract connecting the superior frontal gyrus (SFG) to the inferior frontal gyrus (IFG). Its dorsal origin is identified in humans in the medial wall of the SFG, in the supplementary motor complex (SM-complex). However, empirical observation shows that many FAT fibres appear to originate from the dorsal, rather than medial, portion of the SFG. We quantitatively investigated the actual origin of FAT fibres in the SFG, specifically discriminating between terminations in the medial wall and in the convexity of the SFG. We analysed data from 105 subjects obtained from the Human Connectome Project (HCP) database. We parcelled the cortex of the IFG, dorsal SFG and medial SFG in several regions of interest (ROIs) ordered in a caudal-rostral direction, which served as seed locations for the generation of streamlines. Diffusion imaging data (DWI) was processed using a multi-shell multi-tissue CSD-based algorithm. Results showed that the number of streamlines originating from the dorsal wall of the SFG significantly exceeds those from the medial wall of the SFG. Connectivity patterns between ROIs indicated that FAT sub-bundles are segregated in parallel circuits ordered in a caudal-rostral direction. Such high degree of coherence in the streamline trajectory allows to establish pairs of homologous cortical parcels in the SFG and IFG. We conclude that the frontal origin of the FAT is found in both dorsal and medial surfaces of the superior frontal gyrus.
Collapse
Affiliation(s)
- Marco Tagliaferri
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
| | - Gabriele Amorosino
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
- Neuroinformatics Laboratory, Center for Digital Health & Well Being, Fondazione Bruno Kessler, Trento, Italy
| | - Linda Voltolini
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Institute of Neuroscience, Cleveland Clinic London, Grosvenor Place, London, UK
| | - Paolo Avesani
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
- Neuroinformatics Laboratory, Center for Digital Health & Well Being, Fondazione Bruno Kessler, Trento, Italy
| | - Luigi Cattaneo
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy.
- Centro Interdipartimentale di Scienze Mediche (CISMed) - University of Trento, Trento, Italy.
- Center for Mind/Brain Sciences (CIMeC) - Center for Medical Sciences (CISMed), University of Trento Center for Medical Sciences (CISMed), Via delle Regole 101, Trento, 38123, Italy.
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Hehl M, Van Malderen S, Geraerts M, Meesen RLJ, Rothwell JC, Swinnen SP, Cuypers K. Probing intrahemispheric interactions with a novel dual-site TMS setup. Clin Neurophysiol 2024; 158:180-195. [PMID: 38232610 DOI: 10.1016/j.clinph.2023.12.128] [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: 10/24/2023] [Revised: 12/02/2023] [Accepted: 12/19/2023] [Indexed: 01/19/2024]
Abstract
OBJECTIVE Using dual-site transcranial magnetic stimulation (dsTMS), the effective connectivity between the primary motor cortex (M1) and adjacent brain areas such as the dorsal premotor cortex (PMd) can be investigated. However, stimulating two brain regions in close proximity (e.g., ±2.3 cm for intrahemispheric PMd-M1) is subject to considerable spatial restrictions that potentially can be overcome by combining two standard figure-of-eight coils in a novel dsTMS setup. METHODS After a technical evaluation of its induced electric fields, the dsTMS setup was tested in vivo (n = 23) by applying a short-interval intracortical inhibition (SICI) protocol. Additionally, the intrahemispheric PMd-M1 interaction was probed. E-field modelling was performed using SimNIBS. RESULTS The technical evaluation yielded no major alterations of the induced electric fields due to coil overlap. In vivo, the setup reliably elicited SICI. Investigating intrahemispheric PMd-M1 interactions was feasible (inter-stimulus interval 6 ms), resulting in modulation of M1 output. CONCLUSIONS The presented dsTMS setup provides a novel way to stimulate two adjacent brain regions with fewer technical and spatial limitations than previous attempts. SIGNIFICANCE This dsTMS setup enables more accurate and repeatable targeting of brain regions in close proximity and can facilitate innovation in the field of effective connectivity.
Collapse
Affiliation(s)
- Melina Hehl
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Shanti Van Malderen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Marc Geraerts
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Raf L J Meesen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - John C Rothwell
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, United Kingdom
| | - Stephan P Swinnen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium
| | - Koen Cuypers
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium.
| |
Collapse
|
8
|
Gajwani M, Oldham S, Pang JC, Arnatkevičiūtė A, Tiego J, Bellgrove MA, Fornito A. Can hubs of the human connectome be identified consistently with diffusion MRI? Netw Neurosci 2023; 7:1326-1350. [PMID: 38144690 PMCID: PMC10631793 DOI: 10.1162/netn_a_00324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/17/2023] [Indexed: 12/26/2023] Open
Abstract
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
Collapse
Affiliation(s)
- Mehul Gajwani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - James C. Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| |
Collapse
|
9
|
Boeken OJ, Cieslik EC, Langner R, Markett S. Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding. Brain Struct Funct 2023; 228:1811-1834. [PMID: 36547707 PMCID: PMC10516793 DOI: 10.1007/s00429-022-02603-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: 08/31/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.
Collapse
Affiliation(s)
- Ole J Boeken
- Faculty of Life Sciences, Department of Molecular Psychology, Humboldt-Universität Zu Berlin, Rudower Chaussee 18, 12489, Berlin, Germany.
| | - Edna C Cieslik
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Sebastian Markett
- Faculty of Life Sciences, Department of Molecular Psychology, Humboldt-Universität Zu Berlin, Rudower Chaussee 18, 12489, Berlin, Germany
| |
Collapse
|
10
|
Balajoo SM, Eickhoff SB, Masouleh SK, Plachti A, Waite L, Saberi A, Bahri MA, Bastin C, Salmon E, Hoffstaedter F, Palomero-Gallagher N, Genon S. Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles. Alzheimers Dement 2023; 19:4787-4804. [PMID: 37014937 PMCID: PMC10698199 DOI: 10.1002/alz.13056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 04/06/2023]
Abstract
INTRODUCTION Hippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD). METHODS We characterized the spatial patterns of hippocampus differentiation based on brain co-metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging. RESULTS The hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co-metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object-centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior-posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved. DISCUSSION Future studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.
Collapse
Affiliation(s)
- Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Shahrzad Kharabian Masouleh
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Laura Waite
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Amin Saberi
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM‑1), Research Centre Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| |
Collapse
|
11
|
Takada K, Yamaguchi T, Hyuga Y, Mitsuno Y, Horiguchi S, Kinoshita M, Satow T. Impairment of bimanual in-phase movement during recovery from frontal lobe tumor surgery: a case report. Front Neurosci 2023; 17:1217430. [PMID: 37841682 PMCID: PMC10568456 DOI: 10.3389/fnins.2023.1217430] [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: 05/05/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
The mechanisms underlying bimanual coordination have not yet been fully elucidated. Here, we evaluated the clinical features of bimanual movement impairment in a patient following surgery for a frontal lobe tumor. The patient was an 80-year-old man who had undergone subtotal tumor resection for a tumor in the right superior frontal gyrus. Histological examination of the resected specimen led to the diagnosis of malignant lymphoma of the diffuse large B-cell type, and the patient subsequently received high-dose methotrexate-based chemotherapy. Postoperatively, the patient had difficulty with bimanual movement, and on the 5th postoperative day we found that the impairment could not be attributed to weakness. Temporal changes in the characteristics of manual movements were analyzed. Bimanual diadochokinesis (opening/closing of the hands, pronation/supination of the forearms, and sequential finger movements) was more disturbed than unilateral movements; in-phase movements were more severely impaired than anti-phase movements. Bimanual movement performance was better when cued using an auditory metronome. On the 15th postoperative day, movements improved. The present observations show that in addition to the disturbance of anti-phase bimanual movements, resection of the frontal lobe involving the supplementary motor area (SMA) and premotor cortex (PMC) can cause transient impairment of in-phase bimanual diadochokinesis, which can be more severe than the impairment of anti-phase movements. The effect of auditory cueing on bimanual skills may be useful in the diagnosis of anatomical localization of the superior frontal gyrus and functional localization of the SMA and PMC and in rehabilitation of patients with brain tumors, as in the case of degenerative movement disorders.
Collapse
Affiliation(s)
- Kozue Takada
- Department of Neurology, National Hospital Organization Utano National Hospital, Kyoto, Japan
| | - Takuya Yamaguchi
- Department of Rehabilitation, Nagahama City Hospital, Nagahama, Shiga, Japan
| | - Yuko Hyuga
- Department of Rehabilitation, Nagahama City Hospital, Nagahama, Shiga, Japan
| | - Yuto Mitsuno
- Department of Neurosurgery, Nagahama City Hospital, Nagahama, Shiga, Japan
| | - Satoshi Horiguchi
- Department of Neurosurgery, Nagahama City Hospital, Nagahama, Shiga, Japan
| | - Masako Kinoshita
- Department of Neurology, National Hospital Organization Utano National Hospital, Kyoto, Japan
| | - Takeshi Satow
- Department of Neurosurgery, Nagahama City Hospital, Nagahama, Shiga, Japan
- Department of Neurosurgery, National Hospital Organization Utano National Hospital, Kyoto, Japan
| |
Collapse
|
12
|
Guo Y, Jiang X, Jia L, Zhu Y, Han X, Wu Y, Liu W, Zhao W, Zhu H, Wang D, Tu Z, Zhou Y, Sun Q, Kong L, Wu F, Tang Y. Altered gray matter volumes and plasma IL-6 level in major depressive disorder patients with suicidal ideation. Neuroimage Clin 2023; 38:103403. [PMID: 37079937 PMCID: PMC10148078 DOI: 10.1016/j.nicl.2023.103403] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/07/2023] [Accepted: 04/08/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUNDS Suicidal ideation (SI) is one of the most serious consequences of major depressive disorder (MDD). Understanding the unique mechanism of MDD with SI (MDD + S) is crucial for treatment development. While abundant research has studied MDD, past studies have not reached a consensus on the mechanism of MDD + S. The study aimed to investigate the abnormalities of the gray matter volumes (GMVs) and plasma IL-6 level in MDD + S to further reveal the mechanism of MDD + S. METHODS We tested the plasma IL-6 level using Luminex multifactor assays and collected the Structural Magnetic Resonance Imaging (SMRI) data from 34 healthy controls (HCs), 36 MDD patients without SI (MDD - S) and 34 MDD + S patients. We performed a partial correlation between the GMVs of the brain regions with significant differences and plasma IL-6 level with age, sex, medication, scores of HAMD-17 and HAMA as the covariates. RESULTS Compared with HCs and MDD - S, MDD + S had significantly decreased GMVs in the left cerebellum Crus I/II and significantly increased plasma IL-6 level; compared with HCs, both the MDD + S and MDD - S had significantly decreased GMVs in right precentral and postcentral gyri. No significant correlation was found between the GMVs and the plasma IL-6 level in the MDD + S and MDD - S, respectively. While the GMVs of the right precentral and postcentral gyri negatively correlated with the level of IL-6 in the whole MDD (r = -0.28, P = 0.03). The GMVs of the left cerebellum Crus I/II (r = -0.47, P = 0.02), and the right precentral and postcentral gyri (r = -0.42, P = 0.04) negatively correlated with the level of IL-6 in HCs. CONCLUSION The altered GMVs and the plasma IL-6 level may provide a scientific basis to understand the pathophysiological mechanisms of MDD + S.
Collapse
Affiliation(s)
- Yingrui Guo
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Xiaowei Jiang
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China; Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Linna Jia
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yue Zhu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Xinyu Han
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yifan Wu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Wen Liu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Wenhui Zhao
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Huaqian Zhu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Dahai Wang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Zhaoyuan Tu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yifang Zhou
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Qikun Sun
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Lingtao Kong
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Feng Wu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Department of Geriatric Medicine, The First Hospital of China Medical University, Shenyang, China.
| |
Collapse
|
13
|
Silva AB, Liu JR, Zhao L, Levy DF, Scott TL, Chang EF. A Neurosurgical Functional Dissection of the Middle Precentral Gyrus during Speech Production. J Neurosci 2022; 42:8416-8426. [PMID: 36351829 PMCID: PMC9665919 DOI: 10.1523/jneurosci.1614-22.2022] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022] Open
Abstract
Classical models have traditionally focused on the left posterior inferior frontal gyrus (Broca's area) as a key region for motor planning of speech production. However, converging evidence suggests that it is not critical for either speech motor planning or execution. Alternative cortical areas supporting high-level speech motor planning have yet to be defined. In this review, we focus on the precentral gyrus, whose role in speech production is often thought to be limited to lower-level articulatory muscle control. In particular, we highlight neurosurgical investigations that have shed light on a cortical region anatomically located near the midpoint of the precentral gyrus, hence called the middle precentral gyrus (midPrCG). The midPrCG is functionally located between dorsal hand and ventral orofacial cortical representations and exhibits unique sensorimotor and multisensory functions relevant for speech processing. This includes motor control of the larynx, auditory processing, as well as a role in reading and writing. Furthermore, direct electrical stimulation of midPrCG can evoke complex movements, such as vocalization, and selective injury can cause deficits in verbal fluency, such as pure apraxia of speech. Based on these findings, we propose that midPrCG is essential to phonological-motoric aspects of speech production, especially syllabic-level speech sequencing, a role traditionally ascribed to Broca's area. The midPrCG is a cortical brain area that should be included in contemporary models of speech production with a unique role in speech motor planning and execution.
Collapse
Affiliation(s)
- Alexander B Silva
- Department of Neurological Surgery, University of California, San Francisco, California, 94158
- Weill Institute for Neurosciences, University of California, San Francisco, California, 94158
- Medical Scientist Training Program, University of California, San Francisco, California, 94158
- Graduate Program in Bioengineering, University of California, Berkeley, California 94720, & University of California, San Francisco, California, 94158
| | - Jessie R Liu
- Department of Neurological Surgery, University of California, San Francisco, California, 94158
- Weill Institute for Neurosciences, University of California, San Francisco, California, 94158
- Graduate Program in Bioengineering, University of California, Berkeley, California 94720, & University of California, San Francisco, California, 94158
| | - Lingyun Zhao
- Department of Neurological Surgery, University of California, San Francisco, California, 94158
- Weill Institute for Neurosciences, University of California, San Francisco, California, 94158
| | - Deborah F Levy
- Department of Neurological Surgery, University of California, San Francisco, California, 94158
- Weill Institute for Neurosciences, University of California, San Francisco, California, 94158
| | - Terri L Scott
- Department of Neurological Surgery, University of California, San Francisco, California, 94158
- Weill Institute for Neurosciences, University of California, San Francisco, California, 94158
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, California, 94158
- Weill Institute for Neurosciences, University of California, San Francisco, California, 94158
- Graduate Program in Bioengineering, University of California, Berkeley, California 94720, & University of California, San Francisco, California, 94158
| |
Collapse
|
14
|
Siebner HR, Funke K, Aberra AS, Antal A, Bestmann S, Chen R, Classen J, Davare M, Di Lazzaro V, Fox PT, Hallett M, Karabanov AN, Kesselheim J, Beck MM, Koch G, Liebetanz D, Meunier S, Miniussi C, Paulus W, Peterchev AV, Popa T, Ridding MC, Thielscher A, Ziemann U, Rothwell JC, Ugawa Y. Transcranial magnetic stimulation of the brain: What is stimulated? - A consensus and critical position paper. Clin Neurophysiol 2022; 140:59-97. [PMID: 35738037 PMCID: PMC9753778 DOI: 10.1016/j.clinph.2022.04.022] [Citation(s) in RCA: 139] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 03/14/2022] [Accepted: 04/15/2022] [Indexed: 12/11/2022]
Abstract
Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.
Collapse
Affiliation(s)
- Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark; Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Klaus Funke
- Department of Neurophysiology, Medical Faculty, Ruhr-University Bochum, Bochum, Germany
| | - Aman S Aberra
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Andrea Antal
- Department of Clinical Neurophysiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - Sven Bestmann
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Robert Chen
- Krembil Brain Institute, University Health Network and Division of Neurology, University of Toronto, Toronto, Ontario, Canada
| | - Joseph Classen
- Department of Neurology, University of Leipzig, Leipzig, Germany
| | - Marco Davare
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Anke N Karabanov
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Nutrition and Exercise, University of Copenhagen, Copenhagen, Denmark
| | - Janine Kesselheim
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Mikkel M Beck
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy; Non-invasive Brain Stimulation Unit, Laboratorio di NeurologiaClinica e Comportamentale, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - David Liebetanz
- Department of Clinical Neurophysiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - Sabine Meunier
- Sorbonne Université, Faculté de Médecine, INSERM U 1127, CNRS 4 UMR 7225, Institut du Cerveau, F-75013, Paris, France
| | - Carlo Miniussi
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy; Cognitive Neuroscience Section, IRCCS Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
| | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - Angel V Peterchev
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Psychiatry & Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, USA
| | - Traian Popa
- Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Michael C Ridding
- University of South Australia, IIMPACT in Health, Adelaide, Australia
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Ulf Ziemann
- Department of Neurology & Stroke, University Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University Tübingen, Tübingen, Germany
| | - John C Rothwell
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yoshikazu Ugawa
- Department of Neurology, Fukushima Medical University, Fukushima, Japan; Fukushima Global Medical Science Centre, Advanced Clinical Research Centre, Fukushima Medical University, Fukushima, Japan
| |
Collapse
|
15
|
Schuler AL, Ferrazzi G, Colenbier N, Arcara G, Piccione F, Ferreri F, Marinazzo D, Pellegrino G. Auditory driven gamma synchrony is associated with cortical thickness in widespread cortical areas. Neuroimage 2022; 255:119175. [PMID: 35390460 PMCID: PMC9168448 DOI: 10.1016/j.neuroimage.2022.119175] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 02/20/2022] [Accepted: 04/02/2022] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Gamma synchrony is a fundamental functional property of the cerebral cortex, impaired in multiple neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.). Auditory stimulation in the gamma range allows to drive gamma synchrony of the entire cortical mantle and to estimate the efficiency of the mechanisms sustaining it. As gamma synchrony depends strongly on the interplay between parvalbumin-positive interneurons and pyramidal neurons, we hypothesize an association between cortical thickness and gamma synchrony. To test this hypothesis, we employed a combined magnetoencephalography (MEG) - Magnetic Resonance Imaging (MRI) study. METHODS Cortical thickness was estimated from anatomical MRI scans. MEG measurements related to exposure of 40 Hz amplitude modulated tones were projected onto the cortical surface. Two measures of cortical synchrony were considered: (a) inter-trial phase consistency at 40 Hz, providing a vertex-wise estimation of gamma synchronization, and (b) phase-locking values between primary auditory cortices and whole cortical mantle, providing a measure of long-range cortical synchrony. A correlation between cortical thickness and synchronization measures was then calculated for 72 MRI-MEG scans. RESULTS Both inter-trial phase consistency and phase locking values showed a significant positive correlation with cortical thickness. For inter-trial phase consistency, clusters of strong associations were found in the temporal and frontal lobes, especially in the bilateral auditory and pre-motor cortices. Higher phase-locking values corresponded to higher cortical thickness in the frontal, temporal, occipital and parietal lobes. DISCUSSION AND CONCLUSIONS In healthy subjects, a thicker cortex corresponds to higher gamma synchrony and connectivity in the primary auditory cortex and beyond, likely reflecting underlying cell density involved in gamma circuitries. This result hints towards an involvement of gamma synchrony together with underlying brain structure in brain areas for higher order cognitive functions. This study contributes to the understanding of inherent cortical functional and structural brain properties, which might in turn constitute the basis for the definition of useful biomarkers in patients showing aberrant gamma synchronization.
Collapse
Affiliation(s)
| | - Giulio Ferrazzi
- IRCCS San Camillo Hospital, Via Alberoni 70, Venice 30126, Italy
| | - Nigel Colenbier
- IRCCS San Camillo Hospital, Via Alberoni 70, Venice 30126, Italy
| | - Giorgio Arcara
- IRCCS San Camillo Hospital, Via Alberoni 70, Venice 30126, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy; Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University
| | | |
Collapse
|
16
|
Yan J, Chen L, Yu Y, Xu H, Xu Z, Sheng Y, Chen J. Neuroimaging-ITM: A Text Mining Pipeline Combining Deep Adversarial Learning with Interaction Based Topic Modeling for Enabling the FAIR Neuroimaging Study. Neuroinformatics 2022; 20:701-726. [PMID: 35235184 DOI: 10.1007/s12021-022-09571-w] [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: 02/04/2022] [Indexed: 12/31/2022]
Abstract
Sharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to support a comprehensive understanding of brain cognition, neuroimaging provenance should be constructed to characterize both research processes and results, and integrates various digital resources for quick replication and open cooperation. This brings new challenges to neuroimaging text mining, including fragmented information, lack of labelled corpora, and vague topics. This paper proposes a text mining pipeline for enabling the FAIR neuroimaging study. In order to avoid fragmented information, the Brain Informatics provenance model is redesigned based on NIDM (Neuroimaging Data Model) and FAIR facets. It can systematically capture the provenance requests from the FAIR neuroimaging study and then transform them into a group of text mining tasks. A neuroimaging text mining pipeline combining deep adversarial learning with interaction based topic modeling, called neuroimaging interaction topic model (Neuroimaging-ITM), is proposed to automatically extract neuroimaging provenance and identify research topics in the few-shot scenario. Finally, a group of experiments is completed by using real data from the journal PloS One. The experimental results show that Neuroimaging-ITM can systematically and accurately extract provenance information and obtain high-quality research topics from the full text of neuroimaging articles. Most of the mean F1 values of provenance extraction exceed 0.9. The topic coherence and KL (Kullback-Leibler) divergence reach 9.95 and 0.96 respectively. The results are obviously better than baseline methods.
Collapse
Affiliation(s)
- Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Lihong Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Yongchuan Yu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Hongxia Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Zhe Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Ying Sheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China. .,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing, 100124, China. .,Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing, 100124, China.
| |
Collapse
|
17
|
Nakayama Y, Sugawara SK, Fukunaga M, Hamano YH, Sadato N, Nishimura Y. The dorsal premotor cortex encodes the step-by-step planning processes for goal-directed motor behavior in humans. Neuroimage 2022; 256:119221. [PMID: 35447355 DOI: 10.1016/j.neuroimage.2022.119221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 10/18/2022] Open
Abstract
The dorsal premotor cortex (PMd) plays an essential role in visually guided goal-directed motor behavior. Although there are several planning processes for achieving goal-directed behavior, the separate neural processes are largely unknown. Here, we created a new visuo-goal task to investigate the step-by-step planning processes for visuomotor and visuo-goal behavior in humans. Using functional magnetic resonance imaging, we found activation in different portions of the bilateral PMd during each processing step. In particular, the activated area for rule-based visuomotor and visuo-goal mapping was located at the ventrorostral portion of the bilateral PMd, that for action plan specification was at the dorsocaudal portion of the left PMd, that for transformation was at the rostral portion of the left PMd, and that for action preparation was at the caudal portion of the bilateral PMd. Thus, the left PMd was involved throughout all of the processes, but the right PMd was involved only in rule-based visuomotor and visuo-goal mapping and action preparation. The locations related to each process were generally spatially separated from each other, but they overlapped partially. These findings revealed that there are functional subregions in the bilateral PMd in humans and these subregions form a functional gradient to achieve goal-directed behavior.
Collapse
Affiliation(s)
- Yoshihisa Nakayama
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Kamikitazawa 2-1-6, Setagaya, Tokyo 156-8506, Japan; Frontal Lobe Function Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan.
| | - Sho K Sugawara
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Kamikitazawa 2-1-6, Setagaya, Tokyo 156-8506, Japan; Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan; Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan
| | - Yuki H Hamano
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan; Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan
| | - Yukio Nishimura
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Kamikitazawa 2-1-6, Setagaya, Tokyo 156-8506, Japan
| |
Collapse
|
18
|
Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
Collapse
Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
| |
Collapse
|
19
|
Lin S, Xu Z, Sheng Y, Chen L, Chen J. AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction. Front Neurosci 2022; 15:739535. [PMID: 35321479 PMCID: PMC8936590 DOI: 10.3389/fnins.2021.739535] [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: 07/11/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.
Collapse
Affiliation(s)
- Shaofu Lin
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing Institute of Smart City, Beijing University of Technology, Beijing, China
| | - Zhe Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Ying Sheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Lihong Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging (MRI) and Brain Informatics, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing, China
- *Correspondence: Jianhui Chen,
| |
Collapse
|
20
|
Faskowitz J, Betzel RF, Sporns O. Edges in brain networks: Contributions to models of structure and function. Netw Neurosci 2022; 6:1-28. [PMID: 35350585 PMCID: PMC8942607 DOI: 10.1162/netn_a_00204] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.
Collapse
Affiliation(s)
- Joshua Faskowitz
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F. Betzel
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| |
Collapse
|
21
|
Ekert JO, Lorca-Puls DL, Gajardo-Vidal A, Crinion JT, Hope TMH, Green DW, Price CJ. A functional dissociation of the left frontal regions that contribute to single word production tasks. Neuroimage 2021; 245:118734. [PMID: 34793955 PMCID: PMC8752962 DOI: 10.1016/j.neuroimage.2021.118734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/06/2021] [Accepted: 11/14/2021] [Indexed: 11/02/2022] Open
Abstract
Controversy surrounds the interpretation of higher activation for pseudoword compared to word reading in the left precentral gyrus and pars opercularis. Specifically, does activation in these regions reflect: (1) the demands on sublexical assembly of articulatory codes, or (2) retrieval effort because the combinations of articulatory codes are unfamiliar? Using fMRI, in 84 neurologically intact participants, we addressed this issue by comparing reading and repetition of words (W) and pseudowords (P) to naming objects (O) from pictures or sounds. As objects do not provide sublexical articulatory cues, we hypothesis that retrieval effort will be greater for object naming than word repetition/reading (which benefits from both lexical and sublexical cues); while the demands on sublexical assembly will be higher for pseudoword production than object naming. We found that activation was: (i) highest for pseudoword reading [P>O&W in the visual modality] in the anterior part of the ventral precentral gyrus bordering the precentral sulcus (vPCg/vPCs), consistent with the sublexical assembly of articulatory codes; but (ii) as high for object naming as pseudoword production [P&O>W] in dorsal precentral gyrus (dPCg) and the left inferior frontal junction (IFJ), consistent with retrieval demands and cognitive control. In addition, we dissociate the response properties of vPCg/vPCs, dPCg and IFJ from other left frontal lobe regions that are activated during single word speech production. Specifically, in both auditory and visual modalities: a central part of vPCg (head and face area) was more activated for verbal than nonverbal stimuli [P&W>O]; and the pars orbitalis and inferior frontal sulcus were most activated during object naming [O>W&P]. Our findings help to resolve a previous discrepancy in the literature, dissociate three functionally distinct parts of the precentral gyrus, and refine our knowledge of the functional anatomy of speech production in the left frontal lobe.
Collapse
Affiliation(s)
- Justyna O Ekert
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, United Kingdom.
| | - Diego L Lorca-Puls
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, United Kingdom; Department of Speech, Language and Hearing Sciences, Faculty of Medicine, Universidad de Concepcion, Concepcion, Chile
| | - Andrea Gajardo-Vidal
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, United Kingdom; Faculty of Health Sciences, Universidad del Desarrollo, Concepcion, Chile
| | - Jennifer T Crinion
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Thomas M H Hope
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, United Kingdom
| | - David W Green
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London WC1N 3AR, United Kingdom
| |
Collapse
|
22
|
Markett S, Nothdurfter D, Focsa A, Reuter M, Jawinski P. Attention networks and the intrinsic network structure of the human brain. Hum Brain Mapp 2021; 43:1431-1448. [PMID: 34882908 PMCID: PMC8837576 DOI: 10.1002/hbm.25734] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large‐scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti‐grayordinates. Resulting group maps were compared to the group‐level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto‐parietal and midcingulo‐insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.
Collapse
|
23
|
Liu P, Tu H, Zhang A, Yang C, Liu Z, Lei L, Wu P, Sun N, Zhang K. Brain functional alterations in MDD patients with somatic symptoms: A resting-state fMRI study. J Affect Disord 2021; 295:788-796. [PMID: 34517253 DOI: 10.1016/j.jad.2021.08.143] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/22/2021] [Accepted: 08/27/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE It has been established that major depressive disorder (MDD) is accompanied by various somatic symptoms that are related to the clinical course and severity of depression. However, the mechanisms of somatic symptoms in MDD have rarely been studied. In this study, we sought to investigate the functional neurological changes in MDD patients with somatic symptoms based off the regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF). METHOD Study participants included 74 first-episode, drug naïve MDD patients as well as 70 healthy subjects (HCs). Patients diagnosed with MDD were separated into two groups based on the presence (n=50) or absence (n=24) of somatic symptoms. Functional images were obtained and analyzed. Alterations in ReHo/ALFF and the severity of clinical symptoms were investigated using correlation analysis. RESULTS More severe depressive symptoms were observed in the somatic depression group than that of the pure depression group (P< 0.001). Furthermore, there was a significant reduction in ReHo and ALFF in the bilateral precentral gyrus, bilateral postcentral gyrus, and left paracentral gyrus in the somatic MDD group as compared to the pure depression group (GRF correction, voxel-P< 0.001, cluster-P < 0.01). Pearson correlation analysis revealed a negative correlation between ReHo and ALFF values in these abnomal regions with the severity of somatic and depressive symptoms (P< 0.01). CONCLUSION Somatic depression is more severe than pure depression. The ReHo and ALFF changes in the precentral gyrus, postcentral gyrus, and paracentral gyrus may serve a significant role in the pathophysiology of somatic symptoms in MDD.
Collapse
Affiliation(s)
- Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Shanxi Medical University, Taiyuan 030001, PR China
| | - Hongwei Tu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Shanxi Medical University, Taiyuan 030001, PR China
| | - Aixia Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China
| | - Chunxia Yang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China
| | - Zhifen Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China
| | - Lei Lei
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Department of Psychiatry, Second Hospital of Shanxi Medical University, Taiyuan 030001, PR China
| | - Peiyi Wu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Shanxi Medical University, Taiyuan 030001, PR China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China.
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China.
| |
Collapse
|
24
|
Calvert GHM, Carson RG. Neural mechanisms mediating cross education: With additional considerations for the ageing brain. Neurosci Biobehav Rev 2021; 132:260-288. [PMID: 34801578 DOI: 10.1016/j.neubiorev.2021.11.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/03/2021] [Accepted: 11/16/2021] [Indexed: 12/14/2022]
Abstract
CALVERT, G.H.M., and CARSON, R.G. Neural mechanisms mediating cross education: With additional considerations for the ageing brain. NEUROSCI BIOBEHAV REV 21(1) XXX-XXX, 2021. - Cross education (CE) is the process whereby a regimen of unilateral limb training engenders bilateral improvements in motor function. The contralateral gains thus derived may impart therapeutic benefits for patients with unilateral deficits arising from orthopaedic injury or stroke. Despite this prospective therapeutic utility, there is little consensus concerning its mechanistic basis. The precise means through which the neuroanatomical structures and cellular processes that mediate CE may be influenced by age-related neurodegeneration are also almost entirely unknown. Notwithstanding the increased incidence of unilateral impairment in later life, age-related variations in the expression of CE have been examined only infrequently. In this narrative review, we consider several mechanisms which may mediate the expression of CE with specific reference to the ageing CNS. We focus on the adaptive potential of cellular processes that are subserved by a specific set of neuroanatomical pathways including: the corticospinal tract, corticoreticulospinal projections, transcallosal fibres, and thalamocortical radiations. This analysis may inform the development of interventions that exploit the therapeutic utility of CE training in older persons.
Collapse
Affiliation(s)
- Glenn H M Calvert
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Richard G Carson
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland; School of Psychology, Queen's University Belfast, Belfast, Northern Ireland, UK; School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.
| |
Collapse
|
25
|
Si X, Li S, Xiang S, Yu J, Ming D. Imagined speech increases the hemodynamic response and functional connectivity of the dorsal motor cortex. J Neural Eng 2021; 18. [PMID: 34507311 DOI: 10.1088/1741-2552/ac25d9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/10/2021] [Indexed: 11/12/2022]
Abstract
Objective. Decoding imagined speech from brain signals could provide a more natural, user-friendly way for developing the next generation of the brain-computer interface (BCI). With the advantages of non-invasive, portable, relatively high spatial resolution and insensitivity to motion artifacts, the functional near-infrared spectroscopy (fNIRS) shows great potential for developing the non-invasive speech BCI. However, there is a lack of fNIRS evidence in uncovering the neural mechanism of imagined speech. Our goal is to investigate the specific brain regions and the corresponding cortico-cortical functional connectivity features during imagined speech with fNIRS.Approach. fNIRS signals were recorded from 13 subjects' bilateral motor and prefrontal cortex during overtly and covertly repeating words. Cortical activation was determined through the mean oxygen-hemoglobin concentration changes, and functional connectivity was calculated by Pearson's correlation coefficient.Main results. (a) The bilateral dorsal motor cortex was significantly activated during the covert speech, whereas the bilateral ventral motor cortex was significantly activated during the overt speech. (b) As a subregion of the motor cortex, sensorimotor cortex (SMC) showed a dominant dorsal response to covert speech condition, whereas a dominant ventral response to overt speech condition. (c) Broca's area was deactivated during the covert speech but activated during the overt speech. (d) Compared to overt speech, dorsal SMC(dSMC)-related functional connections were enhanced during the covert speech.Significance. We provide fNIRS evidence for the involvement of dSMC in speech imagery. dSMC is the speech imagery network's key hub and is probably involved in the sensorimotor information processing during the covert speech. This study could inspire the BCI community to focus on the potential contribution of dSMC during speech imagery.
Collapse
Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, People's Republic of China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, People's Republic of China
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Shaoxin Xiang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, People's Republic of China
| | - Jiayue Yu
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| |
Collapse
|
26
|
Lega C, Chelazzi L, Cattaneo L. Two Distinct Systems Represent Contralateral and Ipsilateral Sensorimotor Processes in the Human Premotor Cortex: A Dense TMS Mapping Study. Cereb Cortex 2021; 30:2250-2266. [PMID: 31828296 DOI: 10.1093/cercor/bhz237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 08/19/2019] [Accepted: 09/13/2019] [Indexed: 11/12/2022] Open
Abstract
Animal brains contain behaviorally committed representations of the surrounding world, which integrate sensory and motor information. In primates, sensorimotor mechanisms reside in part in the premotor cortex (PM), where sensorimotor neurons are topographically clustered according to functional specialization. Detailed functional cartography of the human PM is still under investigation. We explored the topographic distribution of spatially dependent sensorimotor functions in healthy volunteers performing left or right, hand or foot, responses to visual cues presented in the left or right hemispace, thus combining independently stimulus side, effector side, and effector type. Event-related transcranial magnetic stimulation was applied to single spots of a dense grid of 10 points on the participants' left hemiscalp, covering the whole PM. Results showed: (1) spatially segregated hand and foot representations, (2) focal representations of contralateral cues and movements in the dorsal PM, and (3) distributed representations of ipsilateral cues and movements in the ventral and dorso-medial PM. The present novel causal information indicates that (1) the human PM is somatotopically organized and (2) the left PM contains sensory-motor representations of both hemispaces and of both hemibodies, but the hemispace and hemibody contralateral to the PM are mapped on a distinct, nonoverlapping cortical region compared to the ipsilateral ones.
Collapse
Affiliation(s)
- Carlotta Lega
- Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Leonardo Chelazzi
- Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy.,Italian Institute of Neuroscience, Section of Verona, Verona, Italy
| | - Luigi Cattaneo
- Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy.,Italian Institute of Neuroscience, Section of Verona, Verona, Italy
| |
Collapse
|
27
|
Liu X, Eickhoff SB, Caspers S, Wu J, Genon S, Hoffstaedter F, Mars RB, Sommer IE, Eickhoff CR, Chen J, Jardri R, Reetz K, Dogan I, Aleman A, Kogler L, Gruber O, Caspers J, Mathys C, Patil KR. Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate. Neuroimage 2021; 235:118006. [PMID: 33819611 PMCID: PMC8214073 DOI: 10.1016/j.neuroimage.2021.118006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 02/10/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.
Collapse
Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Renaud Jardri
- Division of Psychiatry, University of Lille, CNRS UMR9193, SCALab & CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Kathrin Reetz
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany; Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany.
| |
Collapse
|
28
|
Kusano T, Kurashige H, Nambu I, Moriguchi Y, Hanakawa T, Wada Y, Osu R. Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation. Brain Struct Funct 2021; 226:2307-2319. [PMID: 34236531 PMCID: PMC8354910 DOI: 10.1007/s00429-021-02330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 06/22/2021] [Indexed: 11/02/2022]
Abstract
Several functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.
Collapse
Affiliation(s)
- Toshiki Kusano
- Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
| | - Hiroki Kurashige
- Research and Information Center, Tokai University, 2-3-23 Takanawa, Minato-ku, Tokyo, 108-8619, Japan.
| | - Isao Nambu
- Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
| | - Yoshiya Moriguchi
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8551, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8551, Japan.,Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Yasuhiro Wada
- Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Rieko Osu
- The Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai Seika, Soraku, Kyoto, 619-0288, Japan.,Faculty of Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan
| |
Collapse
|
29
|
Koelsch S, Cheung VKM, Jentschke S, Haynes JD. Neocortical substrates of feelings evoked with music in the ACC, insula, and somatosensory cortex. Sci Rep 2021; 11:10119. [PMID: 33980876 PMCID: PMC8115666 DOI: 10.1038/s41598-021-89405-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 04/21/2021] [Indexed: 12/01/2022] Open
Abstract
Neurobiological models of emotion focus traditionally on limbic/paralimbic regions as neural substrates of emotion generation, and insular cortex (in conjunction with isocortical anterior cingulate cortex, ACC) as the neural substrate of feelings. An emerging view, however, highlights the importance of isocortical regions beyond insula and ACC for the subjective feeling of emotions. We used music to evoke feelings of joy and fear, and multivariate pattern analysis (MVPA) to decode representations of feeling states in functional magnetic resonance (fMRI) data of n = 24 participants. Most of the brain regions providing information about feeling representations were neocortical regions. These included, in addition to granular insula and cingulate cortex, primary and secondary somatosensory cortex, premotor cortex, frontal operculum, and auditory cortex. The multivoxel activity patterns corresponding to feeling representations emerged within a few seconds, gained in strength with increasing stimulus duration, and replicated results of a hypothesis-generating decoding analysis from an independent experiment. Our results indicate that several neocortical regions (including insula, cingulate, somatosensory and premotor cortices) are important for the generation and modulation of feeling states. We propose that secondary somatosensory cortex, which covers the parietal operculum and encroaches on the posterior insula, is of particular importance for the encoding of emotion percepts, i.e., preverbal representations of subjective feeling.
Collapse
Affiliation(s)
- Stefan Koelsch
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway. .,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Vincent K M Cheung
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | | | - John-Dylan Haynes
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany
| |
Collapse
|
30
|
Van Hoornweder S, Debeuf R, Verstraelen S, Meesen R, Cuypers K. Unravelling Ipsilateral Interactions Between Left Dorsal Premotor and Primary Motor Cortex: A Proof of Concept Study. Neuroscience 2021; 466:36-46. [PMID: 33971265 DOI: 10.1016/j.neuroscience.2021.04.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022]
Abstract
Few studies have identified the intrahemispheric functional connectivity between the ipsilateral dorsal premotor cortex (PMd) and the primary motor hand area (M1hand) due to technical limitations. In this proof-of-concept study, a novel neuronavigated dsTMS set-up was employed, combining stimulation over left PMd and left M1hand using the edge of a butterfly coil and a small cooled-coil. This arrangement was warranted because coil (over)heating and inter coil distance are limiting factors when investigating connectivity between stimulation targets in close proximity and over a longer duration. The proposed set-up was designed to deal with these limitations. Specifically, the effect of four dual-site transcranial magnetic stimulation (dsTMS) protocols on twenty-eight right-handed participants (12 males) was evaluated. These protocols differed in stimulus order, interstimulus interval and current direction induced in PMd. A structural scan with electric (E-)field modeling was obtained from seven participants prior to dsTMS, demonstrating that PMd and M1hand were effectively stimulated. Results indicate that one protocol, in which a latero-medial current was induced in PMd 2.8 ms prior to stimulation over M1hand, induced a sex-mediated effect. In males, significant inhibition of motor-evoked potentials was identified, whereas females demonstrated a facilitatory effect that did not survive correction for multiple comparisons. E-field simulations revealed that the E-field induced by the coil targeting PMd was maximal in PMd, with weaker E-field strengths extending to regions beyond PMd. Summarizing, the current dsTMS set-up enabled stimulating at an inter-target distance of 35 mm without any indications of coil-overheating.
Collapse
Affiliation(s)
- Sybren Van Hoornweder
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Heverlee, Belgium
| | - Ruben Debeuf
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Heverlee, Belgium; Rehabilitation Research Group, Vrije Universiteit Brussel, Brussels, Belgium
| | - Stefanie Verstraelen
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Raf Meesen
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Koen Cuypers
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Heverlee, Belgium.
| |
Collapse
|
31
|
Wu J, Eickhoff SB, Hoffstaedter F, Patil KR, Schwender H, Yeo BTT, Genon S. A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies. Cereb Cortex 2021; 31:3732-3751. [PMID: 33884421 DOI: 10.1093/cercor/bhab044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 01/01/2023] Open
Abstract
The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
Collapse
Affiliation(s)
- Jianxiao Wu
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Simon B Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Kaustubh R Patil
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City 117575, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City 117597, Singapore.,N. 1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City 117597, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City 117575, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sarah Genon
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| |
Collapse
|
32
|
Steinbach R, Prell T, Gaur N, Roediger A, Gaser C, Mayer TE, Witte OW, Grosskreutz J. Patterns of grey and white matter changes differ between bulbar and limb onset amyotrophic lateral sclerosis. Neuroimage Clin 2021; 30:102674. [PMID: 33901988 PMCID: PMC8099783 DOI: 10.1016/j.nicl.2021.102674] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 12/18/2022]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that is characterized by a high heterogeneity in patients' disease course. Patients with bulbar onset of symptoms (b-ALS) have a poorer prognosis than patients with limb onset (l-ALS). However, neuroimaging correlates of the assumed biological difference between b-ALS and l-ALS may have been obfuscated by patients' diversity in the disease course. We conducted Voxel-Based-Morphometry (VBM) and Tract-Based-Spatial-Statistics (TBSS) in a group of 76 ALS patients without clinically relevant cognitive deficits. The subgroups of 26 b-ALS and 52 l-ALS patients did not differ in terms of disease Phase or disease aggressiveness according to the D50 progression model. VBM analyses showed widespread ALS-related changes in grey and white matter, that were more pronounced for b-ALS. TBSS analyses revealed that b-ALS was predominantly characterized by frontal fractional anisotropy decreases. This demonstrates a higher degree of neurodegenerative burden for the group of b-ALS patients in comparison to l-ALS. Correspondingly, higher bulbar symptom burden was associated with right-temporal and inferior-frontal grey matter density decreases as well as fractional anisotropy decreases in inter-hemispheric and long association tracts. Contrasts between patients in Phase I and Phase II further revealed that b-ALS was characterized by an early cortical pathology and showed a spread only outside primary motor regions to frontal and temporal areas. In contrast, l-ALS showed ongoing structural integrity loss within primary motor-regions until Phase II. We therefore provide a strong rationale to treat both onset types of disease separately in ALS studies.
Collapse
Affiliation(s)
- Robert Steinbach
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany.
| | - Tino Prell
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany; Center for Healthy Ageing, Jena University Hospital, Jena
| | - Nayana Gaur
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | | | - Christian Gaser
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany; Center for Healthy Ageing, Jena University Hospital, Jena; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Thomas E Mayer
- Department of Neuroradiology, Jena University Hospital, Jena, Germany
| | - Otto W Witte
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany; Center for Healthy Ageing, Jena University Hospital, Jena
| | - Julian Grosskreutz
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany; Center for Healthy Ageing, Jena University Hospital, Jena
| |
Collapse
|
33
|
Schmidt TT, Schröder P, Reinhardt P, Blankenburg F. Rehearsal of tactile working memory: Premotor cortex recruits two dissociable neuronal content representations. Hum Brain Mapp 2021; 42:245-258. [PMID: 33009881 PMCID: PMC7721226 DOI: 10.1002/hbm.25220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 09/04/2020] [Accepted: 09/20/2020] [Indexed: 12/17/2022] Open
Abstract
Recent working memory (WM) research has focused on identifying brain regions that retain different types of mental content. Only few neuroimaging studies have explored the mechanism of attention-based refreshing, which is a type of rehearsal and is thought to implement the dynamic components of WM allowing for update of WM contents. Here, we took advantage of the distinct coding properties of the superior parietal lobe (SPL), which retains spatial layout information, and the right inferior frontal gyrus (IFG), which retains frequency information of vibrotactile stimuli during tactile WM. In an fMRI delayed match-to-sample task, participants had to internally rehearse sequences of spatial layouts or vibratory frequencies. Our results replicate the dissociation of SPL and IFG for the retention of layout and frequency information in terms of activation differences between conditions. Additionally, we found strong premotor cortex (PMC) activation during rehearsal of either stimulus type. To explore interactions between these regions we used dynamic causal modeling and found that activation within the network was best explained by a model that allows the PMC to drive activity in the SPL and IFG during rehearsal. This effect was content-specific, meaning that the PMC showed stronger influence on the SPL during pattern rehearsal and stronger influence on the IFG during frequency rehearsal. In line with previously established PMC contributions to sequence processing, our results suggest that it acts as a content-independent area that flexibly recruits content-specific regions to bring a WM item into the focus of attention during the rehearsal of tactile stimulus sequences.
Collapse
Affiliation(s)
- Timo Torsten Schmidt
- Neurocomputation and Neuroimaging Unit (NNU), Department of Education and PsychologyFreie Universität BerlinBerlinGermany
| | - Pia Schröder
- Neurocomputation and Neuroimaging Unit (NNU), Department of Education and PsychologyFreie Universität BerlinBerlinGermany
| | - Pablo Reinhardt
- Neurocomputation and Neuroimaging Unit (NNU), Department of Education and PsychologyFreie Universität BerlinBerlinGermany
| | - Felix Blankenburg
- Neurocomputation and Neuroimaging Unit (NNU), Department of Education and PsychologyFreie Universität BerlinBerlinGermany
| |
Collapse
|
34
|
Säisänen L, Könönen M, Niskanen E, Lakka T, Lintu N, Vanninen R, Julkunen P, Määttä S. Primary hand motor representation areas in healthy children, preadolescents, adolescents, and adults. Neuroimage 2020; 228:117702. [PMID: 33385558 DOI: 10.1016/j.neuroimage.2020.117702] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/16/2020] [Accepted: 12/19/2020] [Indexed: 01/28/2023] Open
Abstract
The development of the organization of the motor representation areas in children and adolescents is not well-known. This cross-sectional study aimed to provide an understanding for the development of the functional motor areas of the upper extremity muscles by studying healthy right-handed children (6-9 years, n = 10), preadolescents (10-12 years, n = 13), adolescents (15-17 years, n = 12), and adults (22-34 years, n = 12). The optimal representation site and resting motor threshold (rMT) for the abductor pollicis brevis (APB) were assessed in both hemispheres using navigated transcranial magnetic stimulation (nTMS). Motor mapping was performed at 110% of the rMT while recording the EMG of six upper limb muscles in the hand and forearm. The association between the motor map and manual dexterity (box and block test, BBT) was examined. The mapping was well-tolerated and feasible in all but the youngest participant whose rMT exceeded the maximum stimulator output. The centers-of-gravity (CoG) for individual muscles were scattered to the greatest extent in the group of preadolescents and centered and became more focused with age. In preadolescents, the CoGs in the left hemisphere were located more laterally, and they shifted medially with age. The proportion of hand compared to arm representation increased with age (p = 0.001); in the right hemisphere, this was associated with greater fine motor ability. Similarly, there was less overlap between hand and forearm muscles representations in children compared to adults (p<0.001). There was a posterior-anterior shift in the APB hotspot coordinate with age, and the APB coordinate in the left hemisphere exhibited a lateral to medial shift with age from adolescence to adulthood (p = 0.006). Our results contribute to the elucidation of the developmental course in the organization of the motor cortex and its associations with fine motor skills. It was shown that nTMS motor mapping in relaxed muscles is feasible in developmental studies in children older than seven years of age.
Collapse
Affiliation(s)
- Laura Säisänen
- Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 100, 70029 KYS, Kuopio, Finland; Institute of Clinical Medicine, University of Eastern Finland, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - Mervi Könönen
- Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 100, 70029 KYS, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Eini Niskanen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Timo Lakka
- Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Finland; Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Niina Lintu
- Institute of Biomedicine, Faculty of Health Sciences, University of Eastern Finland, Finland
| | - Ritva Vanninen
- Institute of Clinical Medicine, University of Eastern Finland, Finland; Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Petro Julkunen
- Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 100, 70029 KYS, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Sara Määttä
- Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 100, 70029 KYS, Kuopio, Finland
| |
Collapse
|
35
|
He Y, Wu S, Chen C, Fan L, Li K, Wang G, Wang H, Zhou Y. Organized Resting-state Functional Dysconnectivity of the Prefrontal Cortex in Patients with Schizophrenia. Neuroscience 2020; 446:14-27. [PMID: 32858143 DOI: 10.1016/j.neuroscience.2020.08.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 07/23/2020] [Accepted: 08/16/2020] [Indexed: 12/25/2022]
Abstract
Schizophrenia has prominent functional dysconnectivity, especially in the prefrontal cortex (PFC). However, it is unclear whether in the same group of patients with schizophrenia, PFC functional dysconnectivity appears in an organized manner or is stochastically located in different subregions. By investigating the resting-state functional connectivity (rsFC) of each PFC subregion from the Brainnetome atlas in 40 schizophrenia patients and 40 healthy subjects, we found 24 altered connections in schizophrenia, and the connections were divided into four categories by a clustering analysis: increased connections within the PFC, increased connections between the inferior PFC and the thalamus/striatum, reduced connections between the PFC and the motor control areas, and reduced connections between the orbital PFC and the emotional perception regions. In addition, the four categories of rsFC showed distinct cognitive engagement patterns. Our findings suggest that PFC subregions have specific functional dysconnectivity patterns in schizophrenia and may reflect heterogeneous symptoms and cognitive deficits in schizophrenia.
Collapse
Affiliation(s)
- Yuwen He
- CAS Key Laboratory of Behavioral Science & Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shihao Wu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Cheng Chen
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Harbin University of Science and Technology, Harbin 150080, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yuan Zhou
- CAS Key Laboratory of Behavioral Science & Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
36
|
Cona G, Wiener M, Scarpazza C. From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies. Neuroimage 2020; 224:117407. [PMID: 32992001 DOI: 10.1016/j.neuroimage.2020.117407] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 08/31/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal cortex, shared by space, time and other magnitudes. The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively. We clearly identified the presence of a system of brain regions commonly recruited in both space and time that includes: bilateral insula, the pre-supplementary motor area (pre-SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating more-posterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions. Our study supports but also expands the ATOM theory. Therefore, we here re-named it the 'GradiATOM' theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.
Collapse
Affiliation(s)
- Giorgia Cona
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy; Padova Neuroscience Center, University of Padua, Italy.
| | - Martin Wiener
- Department of Psychology, George Mason University, Fairfax, VA.
| | - Cristina Scarpazza
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy.
| |
Collapse
|
37
|
Sheets JR, Briggs RG, Bai MY, Poologaindran A, Young IM, Conner AK, Baker CM, Glenn CA, Sughrue ME. Parcellation-based modeling of the dorsal premotor area. J Neurol Sci 2020; 415:116907. [DOI: 10.1016/j.jns.2020.116907] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/05/2020] [Accepted: 05/11/2020] [Indexed: 10/24/2022]
|
38
|
Liu X, Eickhoff SB, Hoffstaedter F, Genon S, Caspers S, Reetz K, Dogan I, Eickhoff CR, Chen J, Caspers J, Reuter N, Mathys C, Aleman A, Jardri R, Riedl V, Sommer IE, Patil KR. Joint Multi-modal Parcellation of the Human Striatum: Functions and Clinical Relevance. Neurosci Bull 2020; 36:1123-1136. [PMID: 32700142 DOI: 10.1007/s12264-020-00543-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 04/10/2020] [Indexed: 12/20/2022] Open
Abstract
The human striatum is essential for both low- and high-level functions and has been implicated in the pathophysiology of various prevalent disorders, including Parkinson's disease (PD) and schizophrenia (SCZ). It is known to consist of structurally and functionally divergent subdivisions. However, previous parcellations are based on a single neuroimaging modality, leaving the extent of the multi-modal organization of the striatum unknown. Here, we investigated the organization of the striatum across three modalities-resting-state functional connectivity, probabilistic diffusion tractography, and structural covariance-to provide a holistic convergent view of its structure and function. We found convergent clusters in the dorsal, dorsolateral, rostral, ventral, and caudal striatum. Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions, while the caudal striatum was related to action execution. Interestingly, significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ, but atrophy in the dorsolateral striatum was specifically attributable to PD. Our study revealed a cross-modal convergent organization of the striatum, representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.
Collapse
Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Kathrin Reetz
- Department of Neurology, Rheinisch Westfällische Technische Hochschule (RWTH) Aachen University, 52074, Aachen, Germany
| | - Imis Dogan
- Jülich Aachen Research Alliance-BRAIN (JARA) Institute of Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, Rheinisch Westfällische Technische Hochschule (RWTH) Aachen University, 52074, Aachen, Germany.,Department of Neurology, Rheinisch Westfällische Technische Hochschule (RWTH) Aachen University, 52074, Aachen, Germany
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, 40225, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany.,Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, 40225, Düsseldorf, Germany
| | - Niels Reuter
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, 40225, Düsseldorf, Germany.,Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, 26129, Oldenburg, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, 9713 AV, Groningen, The Netherlands
| | - Renaud Jardri
- SCALab (CNRS UMR9193) & CHU de Lille, Hôpital Fontan, Pôle de Psychiatrie (CURE), Université de Lille, 59037, Lille, France
| | - Valentin Riedl
- Departments of Neuroradiology, Nuclear Medicine and Neuroimaging Center, Technische Universität München, 80333, Munich, Germany
| | - Iris E Sommer
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, 26129, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany. .,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| |
Collapse
|
39
|
Characterizing the gradients of structural covariance in the human hippocampus. Neuroimage 2020; 218:116972. [PMID: 32454206 DOI: 10.1016/j.neuroimage.2020.116972] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/29/2020] [Accepted: 05/17/2020] [Indexed: 11/23/2022] Open
Abstract
The hippocampus is a plastic brain structure that has been associated with a range of behavioral aspects but also shows vulnerability to the most frequent neurocognitive diseases. Different aspects of its organization have been revealed by studies probing its different neurobiological properties. In particular, histological work has shown a pattern of differentiation along the proximal-distal dimension, while studies examining functional properties and large-scale functional integration have primarily highlighted a pattern of differentiation along the anterior-posterior dimension. To better understand how these organizational dimensions underlie the pattern of structural covariance (SC) in the human hippocampus, we here applied a non-linear decomposition approach, disentangling the major modes of variation, to the pattern of gray matter volume correlation of hippocampus voxels with the rest of the brain in a sample of 377 healthy young adults. We additionally investigated the consistency of the derived gradients in an independent sample of life-span adults and also examined the relationships between these major modes of variations and the patterns derived from microstructure and functional connectivity mapping. Our results showed that similar major modes of SC-variability are identified across the two independent datasets. The major dimension of variation found in SC runs along the hippocampal anterior-posterior axis and followed closely the principal dimension of functional differentiation, suggesting an influence of network level interaction in this major mode of morphological variability. The second main mode of variability in the SC showed a gradient along the dorsal-ventral axis, and was moderately related to variability in hippocampal microstructural properties. Thus our results depicting relatively reliable patterns of SC-variability within the hippocampus show an interplay between the already known organizational principles on the pattern of variability in hippocampus' macrostructural properties. This study hence provides a first insight on the underlying organizational forces generating different co-plastic modes within the human hippocampus that may, in turn, help to better understand different vulnerability patterns of this crucial structure in different neurological and psychiatric diseases.
Collapse
|
40
|
Dinkelbach L, Südmeyer M, Hartmann CJ, Roeber S, Arzberger T, Felsberg J, Ferrea S, Moldovan AS, Amunts K, Schnitzler A, Caspers S. Somatosensory area 3b is selectively unaffected in corticobasal syndrome: combining MRI and histology. Neurobiol Aging 2020; 94:89-100. [PMID: 32593032 DOI: 10.1016/j.neurobiolaging.2020.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/04/2020] [Accepted: 05/14/2020] [Indexed: 10/24/2022]
Abstract
An increasing number of neuroimaging studies addressing patients with corticobasal syndrome use macroscopic definitions of brain regions. As a closer link to functionally relevant units, we aimed at identifying magnetic resonance-based atrophy patterns in regions defined by probability maps of cortical microstructure. For this purpose, three analyses were conducted: (1) Whole-brain cortical thickness was compared between 36 patients with corticobasal syndrome and 24 controls. A pattern of pericentral atrophy was found, covering primary motor area 4, premotor area 6, and primary somatosensory areas 1, 2, and 3a. Within the central region, only area 3b was without atrophy. (2) In 18 patients, longitudinal measures with follow-ups of up to 59 months (mean 21.3 ± 15.4) were analyzed. Areas 1, 2, and 6 showed significantly faster atrophy rates than primary somatosensory area 3b. (3) In an individual autopsy case, longitudinal in vivo morphometry and postmortem pathohistology were conducted. The rate of magnetic resonance-based atrophy was significantly correlated with tufted-astrocyte load in those cytoarchitectonically defined regions also seen in the group study, with area 3b being selectively unaffected.
Collapse
Affiliation(s)
- Lars Dinkelbach
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Martin Südmeyer
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Ernst von Bergmann Klinikum, Potsdam, Germany
| | - Christian Johannes Hartmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Sigrun Roeber
- Center for Neuropathology and Prion Research, Ludwig Maximilian University of Munich, Munich, Germany
| | - Thomas Arzberger
- Center for Neuropathology and Prion Research, Ludwig Maximilian University of Munich, Munich, Germany; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Jörg Felsberg
- Department of Neuropathology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Stefano Ferrea
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Alexia-Sabine Moldovan
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Department of Neurology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Research Centre Jülich, Jülich, Germany.
| |
Collapse
|
41
|
Hand Knob Area of Premotor Cortex Represents the Whole Body in a Compositional Way. Cell 2020; 181:396-409.e26. [PMID: 32220308 DOI: 10.1016/j.cell.2020.02.043] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/12/2019] [Accepted: 02/18/2020] [Indexed: 02/08/2023]
Abstract
Decades after the motor homunculus was first proposed, it is still unknown how different body parts are intermixed and interrelated in human motor cortical areas at single-neuron resolution. Using multi-unit recordings, we studied how face, head, arm, and leg movements are represented in the hand knob area of premotor cortex (precentral gyrus) in people with tetraplegia. Contrary to traditional expectations, we found strong representation of all movements and a partially "compositional" neural code that linked together all four limbs. The code consisted of (1) a limb-coding component representing the limb to be moved and (2) a movement-coding component where analogous movements from each limb (e.g., hand grasp and toe curl) were represented similarly. Compositional coding might facilitate skill transfer across limbs, and it provides a useful framework for thinking about how the motor system constructs movement. Finally, we leveraged these results to create a whole-body intracortical brain-computer interface that spreads targets across all limbs.
Collapse
|
42
|
Reuter N, Genon S, Kharabian Masouleh S, Hoffstaedter F, Liu X, Kalenscher T, Eickhoff SB, Patil KR. CBPtools: a Python package for regional connectivity-based parcellation. Brain Struct Funct 2020; 225:1261-1275. [PMID: 32144496 PMCID: PMC7271019 DOI: 10.1007/s00429-020-02046-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 02/08/2020] [Indexed: 02/02/2023]
Abstract
Regional connectivity-based parcellation (rCBP) is a widely used procedure for investigating the structural and functional differentiation within a region of interest (ROI) based on its long-range connectivity. No standardized software or guidelines currently exist for applying rCBP, making the method only accessible to those who develop their own tools. As such, there exists a discrepancy between the laboratories applying the procedure each with their own software solutions, making it difficult to compare and interpret the results. Here, we outline an rCBP procedure accompanied by an open source software package called CBPtools. CBPtools is a Python (version 3.5+) package that allows users to run an extensively evaluated rCBP analysis workflow on a given ROI. It currently supports two modalities: resting-state functional connectivity and structural connectivity based on diffusion-weighted imaging, along with support for custom connectivity matrices. Analysis parameters are customizable and the workflow can be scaled to a large number of subjects using a parallel processing environment. Parcellation results with corresponding validity metrics are provided as textual and graphical output. Thus, CBPtools provides a simple plug-and-play, yet customizable way to conduct rCBP analyses. By providing an open-source software we hope to promote reproducible and comparable rCBP analyses and, importantly, make the rCBP procedure readily available. Here, we demonstrate the utility of CBPtools using a voluminous data set on an average compute-cluster infrastructure by performing rCBP on three ROIs prominently featured in parcellation literature.
Collapse
Affiliation(s)
- Niels Reuter
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Shahrzad Kharabian Masouleh
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Xiaojin Liu
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Tobias Kalenscher
- Comparative Psychology, Institute of Experimental Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| |
Collapse
|
43
|
Plachti A, Eickhoff SB, Hoffstaedter F, Patil KR, Laird AR, Fox PT, Amunts K, Genon S. Multimodal Parcellations and Extensive Behavioral Profiling Tackling the Hippocampus Gradient. Cereb Cortex 2019; 29:4595-4612. [PMID: 30721944 PMCID: PMC6917521 DOI: 10.1093/cercor/bhy336] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 03/12/2018] [Accepted: 12/11/2018] [Indexed: 12/16/2022] Open
Abstract
The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view. We applied connectivity-based parcellation to 1) intrinsic connectivity 2) task-based connectivity, and 3) structural covariance, as complementary windows into structural and functional differentiation of the hippocampus. Strikingly, while functional properties (i.e., intrinsic and task-based) revealed similar partitions dominated by an anterior-posterior organization, structural covariance exhibited a hybrid pattern reflecting both functional and cytoarchitectonic subdivision. Capitalizing on the consistency of functional parcellations, we defined robust functional maps at different levels of partitions, which are openly available for the scientific community. Our functional maps demonstrated a head-body and tail partition, subdivided along the anterior-posterior and medial-lateral axis. Behavioral profiling of these fine partitions based on activation data indicated an emotion-cognition gradient along the anterior-posterior axis and additionally suggested a self-world-centric gradient supporting the role of the hippocampus in the construction of abstract representations for spatial navigation and episodic memory.
Collapse
Affiliation(s)
- Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf. Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
- GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium
| |
Collapse
|
44
|
The comparative anatomy of frontal eye fields in primates. Cortex 2019; 118:51-64. [DOI: 10.1016/j.cortex.2019.02.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 01/24/2019] [Accepted: 02/22/2019] [Indexed: 12/25/2022]
|
45
|
Artificial bee colony clustering with self-adaptive crossover and stepwise search for brain functional parcellation in fMRI data. Soft comput 2019. [DOI: 10.1007/s00500-018-3467-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
46
|
Abstract
A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation - defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions - is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.
Collapse
|
47
|
Labache L, Joliot M, Saracco J, Jobard G, Hesling I, Zago L, Mellet E, Petit L, Crivello F, Mazoyer B, Tzourio-Mazoyer N. A SENtence Supramodal Areas AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic connectivity in 144 healthy right-handers. Brain Struct Funct 2019; 224:859-882. [PMID: 30535758 PMCID: PMC6420474 DOI: 10.1007/s00429-018-1810-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 12/01/2018] [Indexed: 12/13/2022]
Abstract
We herein propose an atlas of 32 sentence-related areas based on a 3-step method combining the analysis of activation and asymmetry during multiple language tasks with hierarchical clustering of resting-state connectivity and graph analyses. 144 healthy right-handers performed fMRI runs based on language production, reading and listening, both with sentences and lists of over-learned words. Sentence minus word-list BOLD contrast and left-minus-right BOLD asymmetry for each task were computed in pairs of homotopic regions of interest (hROIs) from the AICHA atlas. Thirty-two hROIs were identified that were conjointly activated and leftward asymmetrical in each of the three language contrasts. Analysis of resting-state temporal correlations of BOLD variations between these 32 hROIs allowed the segregation of a core network, SENT_CORE including 18 hROIs. Resting-state graph analysis applied to SENT_CORE hROIs revealed that the pars triangularis of the inferior frontal gyrus and the superior temporal sulcus were hubs based on their degree centrality (DC), betweenness, and participation values corresponding to epicentres of sentence processing. Positive correlations between DC and BOLD activation values for SENT_CORE hROIs were observed across individuals and across regions regardless of the task: the more a SENT_CORE area is connected at rest the stronger it is activated during sentence processing. DC measurements in SENT_CORE may thus be a valuable index for the evaluation of inter-individual variations in language areas functional activity in relation to anatomical or clinical patterns in large populations. SENSAAS (SENtence Supramodal Areas AtlaS), comprising the 32 supramodal sentence areas, including SENT_CORE network, can be downloaded at http://www.gin.cnrs.fr/en/tools/ .
Collapse
Affiliation(s)
- L Labache
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
- Univ. Bordeaux, IMB, UMR 5251, 33405, Talence, France
- INRIA Bordeaux Sud-Ouest, CQFD, UMR 5251, 33405, Talence, France
| | - M Joliot
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - J Saracco
- INRIA Bordeaux Sud-Ouest, CQFD, UMR 5251, 33405, Talence, France
- Bordeaux INP, IMB, UMR 5251, 33405, Talence, France
| | - G Jobard
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - I Hesling
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - L Zago
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - E Mellet
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - L Petit
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - F Crivello
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - B Mazoyer
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France
| | - Nathalie Tzourio-Mazoyer
- Univ. Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France.
- CNRS, IMN, UMR 5293, 33000, Bordeaux, France.
- CEA, GIN, IMN, UMR 5293, 33000, Bordeaux, France.
| |
Collapse
|
48
|
Lotze M, Langner R. Editorial for the special issue "Resting-state fMRI and cognition" in Brain and Cognition. Brain Cogn 2019; 131:1-3. [PMID: 30712965 DOI: 10.1016/j.bandc.2019.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Martin Lotze
- Functional Imaging Unit, Center for Diagnostic Radiology, University of Greifswald, Germany.
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| |
Collapse
|
49
|
Varikuti DP, Genon S, Sotiras A, Schwender H, Hoffstaedter F, Patil KR, Jockwitz C, Caspers S, Moebus S, Amunts K, Davatzikos C, Eickhoff SB. Evaluation of non-negative matrix factorization of grey matter in age prediction. Neuroimage 2018; 173:394-410. [PMID: 29518572 PMCID: PMC5911196 DOI: 10.1016/j.neuroimage.2018.03.007] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 02/28/2018] [Accepted: 03/03/2018] [Indexed: 11/24/2022] Open
Abstract
The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging.
Collapse
Affiliation(s)
- Deepthi P Varikuti
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Aristeidis Sotiras
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Holger Schwender
- Mathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; JARA-BRAIN, Juelich-Aachen Research Alliance, Juelich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; JARA-BRAIN, Juelich-Aachen Research Alliance, Juelich, Germany
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Christos Davatzikos
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| |
Collapse
|
50
|
Brich LFM, Bächle C, Hermsdörfer J, Stadler W. Real-Time Prediction of Observed Action Requires Integrity of the Dorsal Premotor Cortex: Evidence From Repetitive Transcranial Magnetic Stimulation. Front Hum Neurosci 2018; 12:101. [PMID: 29628880 PMCID: PMC5876293 DOI: 10.3389/fnhum.2018.00101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 03/05/2018] [Indexed: 12/12/2022] Open
Abstract
Studying brain mechanisms underlying the prediction of observed action, the dorsal premotor cortex (PMd) has been suggested a key area. The present study probed this notion using repetitive transcranial magnetic stimulation (rTMS) to test whether interference in this area would affect the accuracy in predicting the time course of object directed actions performed with the right hand. Young and healthy participants observed actions in short videos. These were briefly occluded from view for 600 ms and resumed immediately afterwards. The task was to continue the action mentally and to indicate after each occlusion, whether the action was resumed at the right moment (condition in-time) or shifted. In a first run, single-pulse transcranial magnetic stimulation (sTMS) was delivered over the left primary hand-area during occlusion. In the second run, rTMS over the left PMd was applied during occlusion in half of the participants [experimental group (EG)]. The control group (CG) received sham-rTMS over the same area. Under rTMS, the EG predicted less trials correctly than in the sTMS run. Sham-rTMS in the CG had no effects on prediction. The interference in PMd interacted with the type of manipulation applied to the action’s time course occasionally during occlusion. The performance decrease of the EG was most pronounced in conditions in which the continuations after occlusions were too late in the action’s course. The present results extend earlier findings suggesting that real-time action prediction requires the integrity of the PMd. Different functional roles of this area are discussed. Alternative interpretations consider either simulation of specific motor programming functions or the involvement of a feature-unspecific predictor.
Collapse
Affiliation(s)
- Louisa F M Brich
- Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Christine Bächle
- Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Joachim Hermsdörfer
- Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Waltraud Stadler
- Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| |
Collapse
|