51
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Faskowitz J, Puxeddu MG, van den Heuvel MP, Mišić B, Yovel Y, Assaf Y, Betzel RF, Sporns O. Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny. Front Neurosci 2023; 16:1044372. [PMID: 36711139 PMCID: PMC9874302 DOI: 10.3389/fnins.2022.1044372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.
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Affiliation(s)
- Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
| | - Martijn P. van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bratislav Mišić
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
- Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, United States
- Program in Cognitive Science, Indiana University Bloomington, Bloomington, IN, United States
- Indiana University Network Science Institute, Indiana University Bloomington, Bloomington, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
- Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, United States
- Program in Cognitive Science, Indiana University Bloomington, Bloomington, IN, United States
- Indiana University Network Science Institute, Indiana University Bloomington, Bloomington, IN, United States
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52
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Angular gyrus: an anatomical case study for association cortex. Brain Struct Funct 2023; 228:131-143. [PMID: 35906433 DOI: 10.1007/s00429-022-02537-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 07/05/2022] [Indexed: 01/07/2023]
Abstract
The angular gyrus is associated with a spectrum of higher order cognitive functions. This mini-review undertakes a broad survey of putative neuroanatomical substrates, guided by the premise that area-specific specializations derive from a combination of extrinsic connections and intrinsic area properties. Three levels of spatial resolution are discussed: cellular, supracellular connectivity, and synaptic micro-scale, with examples necessarily drawn mainly from experimental work with nonhuman primates. A significant factor in the functional specialization of the human parietal cortex is the pronounced enlargement. In addition to "more" cells, synapses, and connections, however, the heterogeneity itself can be considered an important property. Multiple anatomical features support the idea of overlapping and temporally dynamic membership in several brain wide subnetworks, but how these features operate in the context of higher cognitive functions remains for continued investigations.
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53
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Zhang Y, Shao J, Wang X, Pei C, Zhang S, Yao Z, Lu Q. Partly recovery and compensation in anterior cingulate cortex after SSRI treatment-evidence from multi-voxel pattern analysis over resting state fMRI in depression. J Affect Disord 2023; 320:404-412. [PMID: 36179779 DOI: 10.1016/j.jad.2022.09.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 05/23/2022] [Accepted: 09/20/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Anterior cingulate cortex (ACC) plays an essential role in the pathophysiology of major depressive disorder (MDD) and its treatment. However, it's still unclear whether the effects of disease and antidepressant treatment on ACC perform diversely in neural mechanisms. METHODS Fifty-nine MDD patients completed resting-state fMRI scanning twice at baseline and after 12-week selective serotonin reuptake inhibitor (SSRI) treatment, respectively in acute state and remission state. Fifty-nine demographically matched healthy controls were enrolled. Using fractional amplitude of low-frequency fluctuation (fALFF) in ACC as features, we performed multi-voxel pattern analysis over pretreatment MDD patients vs health control (HC), and over pretreatment MDD patients vs posttreatment MDD patients. RESULTS Discriminative regions in ACC for MDD impairment and changes after antidepressants were obtained. The intersection set and difference set were calculated to form ACC subregions of recovered, unrecovered and compensative, respectively. The recovered ACC subregion mainly distributed in rostral ACC (80 %) and the other two subregions had nearly equal distribution over dorsal ACC and rostral ACC. Furthermore, only the compensative subregion had significant changed functional connectivity with cingulo-opercular control network (CON) after antidepressant treatment. LIMITATIONS The number of subjects was relatively small. The results need to be validated with larger sample sizes and multisite data. CONCLUSIONS This finding suggested that the local function of ACC was partly recovered on regulating emotion after antidepressant by detecting the common subregional targets of depression impairment and antidepressive effect. Besides, changed fALFF in the compensative ACC subregion and its connectivity with CON may partly compensate for the cognition deficits.
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Affiliation(s)
- Yujie Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Cong Pei
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Shuqiang Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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54
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Lin J, Zhang L, Guo R, Jiao S, Song X, Feng S, Wang K, Li M, Luo Y, Han Z. The influence of visual deprivation on the development of the thalamocortical network: Evidence from congenitally blind children and adults. Neuroimage 2022; 264:119722. [PMID: 36323383 DOI: 10.1016/j.neuroimage.2022.119722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/23/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The thalamus is heavily involved in relaying sensory signals to the cerebral cortex. A relevant issue is how the deprivation of congenital visual sensory information modulates the development of the thalamocortical network. The answer is unclear because previous studies on this topic did not investigate network development, structure-function combinations, and cognition-related behaviors in the same study. To overcome these limitations, we recruited 30 congenitally blind subjects (8 children, 22 adults) and 31 sighted subjects (10 children, 21 adults), and conducted multiple analyses [i.e., gray matter volume (GMV) analysis using the voxel-based morphometry (VBM) method, resting-state functional connectivity (FC), and brain-behavior correlation]. We found that congenital blindness elicited significant changes in the development of GMV in visual and somatosensory thalamic regions. Blindness also resulted in significant changes in the development of FC between somatosensory thalamic regions and visual cortical regions as well as advanced information processing regions. Moreover, the somatosensory thalamic regions and their FCs with visual cortical regions were reorganized to process high-level tactile language information in blind individuals. These findings provide a refined understanding of the neuroanatomical and functional plasticity of the thalamocortical network.
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Affiliation(s)
- Junfeng Lin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Linjun Zhang
- School of Chinese as a Second Language, Peking University, Beijing 100091, China
| | - Runhua Guo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Saiyi Jiao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaomeng Song
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Suting Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ke Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingyang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yudan Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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55
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Do we understand the prefrontal cortex? Brain Struct Funct 2022:10.1007/s00429-022-02587-7. [DOI: 10.1007/s00429-022-02587-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022]
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56
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Suarez LE, Yovel Y, van den Heuvel MP, Sporns O, Assaf Y, Lajoie G, Misic B. A connectomics-based taxonomy of mammals. eLife 2022; 11:e78635. [PMID: 36342363 PMCID: PMC9681214 DOI: 10.7554/elife.78635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle to the comparison of neural architectures has been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyse the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.
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Affiliation(s)
- Laura E Suarez
- Montréal Neurological Institute, McGill UniversityMontrealCanada
- Mila - Quebec Artificial Intelligence InstituteMontrealCanada
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana UniversityBloomingtonUnited States
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | | | - Bratislav Misic
- Montréal Neurological Institute, McGill UniversityMontrealCanada
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57
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Reveley C, Ye FQ, Mars RB, Matrov D, Chudasama Y, Leopold DA. Diffusion MRI anisotropy in the cerebral cortex is determined by unmyelinated tissue features. Nat Commun 2022; 13:6702. [PMID: 36335105 PMCID: PMC9637141 DOI: 10.1038/s41467-022-34328-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is commonly used to assess the tissue and cellular substructure of the human brain. In the white matter, myelinated axons are the principal neural elements that shape dMRI through the restriction of water diffusion; however, in the gray matter the relative contributions of myelinated axons and other tissue features to dMRI are poorly understood. Here we investigate the determinants of diffusion in the cerebral cortex. Specifically, we ask whether myelinated axons significantly shape dMRI fractional anisotropy (dMRI-FA), a measure commonly used to characterize tissue properties in humans. We compared ultra-high resolution ex vivo dMRI data from the brain of a marmoset monkey with both myelin- and Nissl-stained histological sections obtained from the same brain after scanning. We found that the dMRI-FA did not match the spatial distribution of myelin in the gray matter. Instead dMRI-FA was more closely related to the anisotropy of stained tissue features, most prominently those revealed by Nissl staining and to a lesser extent those revealed by myelin staining. Our results suggest that unmyelinated neurites such as large caliber apical dendrites are the primary features shaping dMRI measures in the cerebral cortex.
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Affiliation(s)
- Colin Reveley
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.12082.390000 0004 1936 7590Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ UK
| | - Frank Q. Ye
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA
| | - Rogier B. Mars
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Denis Matrov
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Yogita Chudasama
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - David A. Leopold
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA ,grid.94365.3d0000 0001 2297 5165Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
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58
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Thiebaut de Schotten M, Forkel SJ. The emergent properties of the connected brain. Science 2022; 378:505-510. [DOI: 10.1126/science.abq2591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
There is more to brain connections than the mere transfer of signals between brain regions. Behavior and cognition emerge through cortical area interaction. This requires integration between local and distant areas orchestrated by densely connected networks. Brain connections determine the brain’s functional organization. The imaging of connections in the living brain has provided an opportunity to identify the driving factors behind the neurobiology of cognition. Connectivity differences between species and among humans have furthered the understanding of brain evolution and of diverging cognitive profiles. Brain pathologies amplify this variability through disconnections and, consequently, the disintegration of cognitive functions. The prediction of long-term symptoms is now preferentially based on brain disconnections. This paradigm shift will reshape our brain maps and challenge current brain models.
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Affiliation(s)
- Michel Thiebaut de Schotten
- Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
| | - Stephanie J. Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
- Donders Centre for Brain Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- Department of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
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59
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Misconfigured striatal connectivity profiles in smokers. Neuropsychopharmacology 2022; 47:2081-2089. [PMID: 35752682 PMCID: PMC9556661 DOI: 10.1038/s41386-022-01366-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/19/2022] [Accepted: 06/14/2022] [Indexed: 11/09/2022]
Abstract
Dysregulation of frontal cortical inputs to the striatum is foundational in the neural basis of substance use disorder (SUD). Neuroanatomical and electrophysiological data increasingly show that striatal nodes receive appreciable input from numerous cortical areas, and that the combinational properties of these multivariate "connectivity profiles" play a predominant role in shaping striatal activity and function. Yet, how abnormal configuration of striatal connectivity profiles might contribute to SUD is unknown. Here, we implemented a novel "connectivity profile analysis" (CPA) approach using resting-state functional connectivity data to facilitate detection of different types of connectivity profile "misconfiguration" that may reflect distinct forms of aberrant circuit plasticity in SUD. We examined 46 nicotine-dependent smokers and 33 non-smokers and showed that both dorsal striatum (DS) and ventral striatum (VS) connectivity profiles with frontal cortex were misconfigured in smokers-but in doubly distinct fashions. DS misconfigurations were stable across sated and acute abstinent states (indicative of a "trait" circuit adaptation) whereas VS misconfigurations emerged only during acute abstinence (indicative of a "state" circuit adaptation). Moreover, DS misconfigurations involved abnormal connection strength rank order arrangement, whereas VS misconfigurations involved abnormal aggregate strength. We found that caudal ventral putamen in smokers uniquely displayed multiple types of connectivity profile misconfiguration, whose interactive magnitude was linked to dependence severity, and that VS misconfiguration magnitude correlated positively with withdrawal severity during acute abstinence. Findings underscore the potential for approaches that more aptly model the neurobiological composition of corticostriatal circuits to yield deeper insights into the neural basis of SUD.
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60
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Warrington S, Thompson E, Bastiani M, Dubois J, Baxter L, Slater R, Jbabdi S, Mars RB, Sotiropoulos SN. Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications. SCIENCE ADVANCES 2022; 8:eabq2022. [PMID: 36260675 PMCID: PMC9581484 DOI: 10.1126/sciadv.abq2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.
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Affiliation(s)
- Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Elinor Thompson
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jessica Dubois
- Université Paris Cité, Inserm, NeuroDiderot Unit, Paris, France
- University Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
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61
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Li W, Fan L, Shi W, Lu Y, Li J, Luo N, Wang H, Chu C, Ma L, Song M, Li K, Cheng L, Cao L, Jiang T. Brainnetome atlas of preadolescent children based on anatomical connectivity profiles. Cereb Cortex 2022; 33:5264-5275. [PMID: 36255322 PMCID: PMC10151881 DOI: 10.1093/cercor/bhac415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/17/2022] [Accepted: 09/18/2022] [Indexed: 11/13/2022] Open
Abstract
During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.
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Affiliation(s)
- Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, No.1 Jinji Road, Qixing District, Guilin 541004, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
| | - Long Cao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
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Hermens DF, Jamieson D, Fitzpatrick L, Sacks DD, Iorfino F, Crouse JJ, Guastella AJ, Scott EM, Hickie IB, Lagopoulos J. Sex differences in fronto-limbic white matter tracts in youth with mood disorders. Psychiatry Clin Neurosci 2022; 76:481-489. [PMID: 35730893 DOI: 10.1111/pcn.13440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/22/2022] [Accepted: 06/14/2022] [Indexed: 11/29/2022]
Abstract
AIMS Patients with depression and bipolar disorder have previously been shown to have impaired white matter (WM) integrity compared with healthy controls. This study aimed to investigate potential sex differences that may provide further insight into the pathophysiology of these highly debilitating mood disorders. METHODS Participants aged 17 to 30 years (168 with depression [60% females], 107 with bipolar disorder [74% females], and 61 controls [64% females]) completed clinical assessment, self-report measures, and a neuropsychological assessment battery. Participants also underwent magnetic resonance imaging from which diffusion tensor imaging data were collected among five fronto-limbic WM tracts: cingulum bundle (cingulate gyrus and hippocampus subsections), fornix, stria terminalis, and the uncinate fasciculus. Mean fractional anisotropy (FA) scores were compared between groups using analyses of variance with sex and diagnosis as fixed factors. RESULTS Among the nine WM tracts analyzed, one revealed a significant interaction between sex and diagnosis, controlling for age. Male patients with bipolar disorder had significantly lower FA scores in the fornix compared with the other groups. Furthermore, partial correlations revealed a significant positive association between FA scores for the fornix and psychomotor speed. CONCLUSIONS Our findings suggest that males with bipolar disorder may be at increased risk of disruptions in WM integrity, especially in the fornix, which is thought to be responsible for a range of cognitive functions. More broadly, our findings suggest that sex differences may exist in WM integrity and thereby alter our understanding of the pathophysiology of mood disorders.
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Affiliation(s)
- Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
| | - Daniel Jamieson
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
| | - Lauren Fitzpatrick
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Dashiell D Sacks
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
| | - Frank Iorfino
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Jacob J Crouse
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Adam J Guastella
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Elizabeth M Scott
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Ian B Hickie
- Youth Mental Health & Technology Team, Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Jim Lagopoulos
- Thompson Institute, University of the Sunshine Coast, Birtinya, Queensland, Australia
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63
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Korponay C, Stein EA, Ross TJ. Laterality Hotspots in the Striatum. Cereb Cortex 2022; 32:2943-2956. [PMID: 34727171 PMCID: PMC9290552 DOI: 10.1093/cercor/bhab392] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 11/14/2022] Open
Abstract
Striatal loci are connected to both the ipsilateral and contralateral frontal cortex. Normative quantitation of the dissimilarity between striatal loci's hemispheric connection profiles and its spatial variance across the striatum, and assessment of how interindividual differences relate to function, stands to further the understanding of the role of corticostriatal circuits in lateralized functions and the role of abnormal corticostriatal laterality in neurodevelopmental and other neuropsychiatric disorders. A resting-state functional connectivity fingerprinting approach (n = 261) identified "laterality hotspots"-loci whose profiles of connectivity with ipsilateral and contralateral frontal cortex were disproportionately dissimilar-in the right rostral ventral putamen, left rostral central caudate, and bilateral caudal ventral caudate. Findings were replicated in an independent sample and were robust to both preprocessing choices and the choice of cortical atlas used for parcellation definitions. Across subjects, greater rightward connectional laterality at the right ventral putamen hotspot and greater leftward connectional laterality at the left rostral caudate hotspot were associated with higher performance on tasks engaging lateralized functions (i.e., response inhibition and language, respectively). In sum, we find robust and reproducible evidence for striatal loci with disproportionately lateralized connectivity profiles where interindividual differences in laterality magnitude are associated with behavioral capacities on lateralized functions.
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Affiliation(s)
- Cole Korponay
- Basic Neuroscience Division, McLean Hospital, Belmont, MA 02478, USA
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD 21224, USA
| | - Thomas J Ross
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD 21224, USA
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64
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Park BY, Paquola C, Bethlehem RAI, Benkarim O, Mišić B, Smallwood J, Bullmore ET, Bernhardt BC. Adolescent development of multiscale structural wiring and functional interactions in the human connectome. Proc Natl Acad Sci U S A 2022; 119:e2116673119. [PMID: 35776541 PMCID: PMC9271154 DOI: 10.1073/pnas.2116673119] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 04/30/2022] [Indexed: 01/03/2023] Open
Abstract
Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure-function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.
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Affiliation(s)
- Bo-yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
- Department of Data Science, Inha University, Incheon, 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, Republic of Korea
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
- Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, 52428, Germany
| | - Richard A. I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | | | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | - Edward T. Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
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65
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Predicting brain structural network using functional connectivity. Med Image Anal 2022; 79:102463. [PMID: 35490597 DOI: 10.1016/j.media.2022.102463] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/06/2022] [Accepted: 04/15/2022] [Indexed: 12/13/2022]
Abstract
Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and function, e.g., the relations between individual brain structural connectivity (SC) and functional connectivity (FC). Brain structure-function displays a distributed and heterogeneous pattern, that is, many functional relationships arise from non-overlapping sets of anatomical connections. This complex relation can be interwoven with widely existed individual structural and functional variations. Motivated by the advances of generative adversarial network (GAN) and graph convolutional network (GCN) in the deep learning field, in this work, we proposed a multi-GCN based GAN (MGCN-GAN) to infer individual SC based on corresponding FC by automatically learning the complex associations between individual brain structural and functional networks. The generator of MGCN-GAN is composed of multiple multi-layer GCNs which are designed to model complex indirect connections in brain network. The discriminator of MGCN-GAN is a single multi-layer GCN which aims to distinguish the predicted SC from real SC. To overcome the inherent unstable behavior of GAN, we designed a new structure-preserving (SP) loss function to guide the generator to learn the intrinsic SC patterns more effectively. Using Human Connectome Project (HCP) dataset and Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset as test beds, our MGCN-GAN model can generate reliable individual SC from FC. This result implies that there may exist a common regulation between specific brain structural and functional architectures across different individuals.
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66
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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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67
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A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Curr Biol 2022; 32:3443-3459.e8. [PMID: 35809568 DOI: 10.1016/j.cub.2022.06.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/17/2022] [Accepted: 06/13/2022] [Indexed: 11/20/2022]
Abstract
The wiring architecture of neuronal networks is assumed to be a strong determinant of their dynamical computations. An ongoing effort in neuroscience is therefore to generate comprehensive synapse-resolution connectomes alongside brain-wide activity maps. However, the structure-function relationship, i.e., how the anatomical connectome and neuronal dynamics relate to each other on a global scale, remains unsolved. Systematically, comparing graph features in the C. elegans connectome with correlations in nervous system-wide neuronal dynamics, we found that few local connectivity motifs and mostly other non-local features such as triplet motifs and input similarities can predict functional relationships between neurons. Surprisingly, quantities such as connection strength and amount of common inputs do not improve these predictions, suggesting that the network's topology is sufficient. We demonstrate that hub neurons in the connectome are key to these relevant graph features. Consistently, inhibition of multiple hub neurons specifically disrupts brain-wide correlations. Thus, we propose that a set of hub neurons and non-local connectivity features provide an anatomical substrate for global brain dynamics.
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68
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Ainsworth M, Wu Z, Browncross H, Mitchell AS, Bell AH, Buckley MJ. Frontopolar cortex shapes brain network structure across prefrontal and posterior cingulate cortex. Prog Neurobiol 2022; 217:102314. [DOI: 10.1016/j.pneurobio.2022.102314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 04/08/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022]
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69
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Gerloff C, Konrad K, Bzdok D, Büsing C, Reindl V. Interacting brains revisited: A cross-brain network neuroscience perspective. Hum Brain Mapp 2022; 43:4458-4474. [PMID: 35661477 PMCID: PMC9435014 DOI: 10.1002/hbm.25966] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 03/25/2022] [Accepted: 05/02/2022] [Indexed: 12/14/2022] Open
Abstract
Elucidating the neural basis of social behavior is a long‐standing challenge in neuroscience. Such endeavors are driven by attempts to extend the isolated perspective on the human brain by considering interacting persons' brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current hyperscanning analyses mostly rely on global metrics, we encode the regions' roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.
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Affiliation(s)
- Christian Gerloff
- JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen & Research Centre Juelich, Aachen, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Chair II of Mathematics, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - Kerstin Konrad
- JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen & Research Centre Juelich, Aachen, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Christina Büsing
- Chair II of Mathematics, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Aachen, Germany
| | - Vanessa Reindl
- JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen & Research Centre Juelich, Aachen, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore
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70
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Abstract
Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.
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71
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Wen H, Xu T, Wang X, Yu X, Bi Y. Brain intrinsic connection patterns underlying tool processing in human adults are present in neonates and not in macaques. Neuroimage 2022; 258:119339. [PMID: 35649467 PMCID: PMC9520606 DOI: 10.1016/j.neuroimage.2022.119339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 05/23/2022] [Accepted: 05/28/2022] [Indexed: 11/25/2022] Open
Abstract
Tool understanding and use are supported by a dedicated left-lateralized, intrinsically connected network in the human adult brain. To examine this network’s phylogenetic and ontogenetic origins, we compared resting-state functional connectivity (rsFC) among regions subserving tool processing in human adults to rsFC among homologous regions in human neonates and macaque monkeys (adolescent and mature). These homologous regions formed an intrinsic network in human neonates, but not in macaques. Network topological patterns were highly similar between human adults and neonates, and significantly less so between humans and macaques. The premotor-parietal rsFC had most significant contribution to the formation of the neonatal tool network. These results suggest that an intrinsic brain network potentially supporting tool processing exists in the human brain prior to individual tool use experiences, and that the premotor-parietal functional connection in particular offers a brain basis for complex tool behaviors specific to humans.
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72
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Donaldson PD, Swisher SL. Transparent, Low-Impedance Inkjet-Printed PEDOT:PSS Microelectrodes for Multi-modal Neuroscience. PHYSICA STATUS SOLIDI. A, APPLICATIONS AND MATERIALS SCIENCE : PSS 2022; 219:2100683. [PMID: 37641661 PMCID: PMC10461862 DOI: 10.1002/pssa.202100683] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Indexed: 08/31/2023]
Abstract
Transparent microelectrodes that facilitate simultaneous optical and electrophysiological interfacing are desirable tools for neuroscience. Electrodes made from transparent conductors such as graphene and indium tin oxide (ITO) show promise but are often limited by poor interfacial charge-transfer properties. Here, microelectrodes are demonstrated that take advantage of the transparency and volumetric capacitance of the mixed ion-electron conductor Poly(3,4- ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). Ring-shaped microelectrodes are fabricated by inkjet-printing PEDOT:PSS, encapsulating with Parylene-C, and then exposing a contact site that is much smaller than the microelectrode outer diameter. This unique structure allows the encapsulated portion of the microelectrode volume surrounding the contact site to participate in signal transduction, which reduces impedance and enhances charge storage capacity. While using the same 100 μm diameter contact site, increasing the outer diameter of the encapsulated electrode from 300 to 550 μm reduces the impedance from 294±21 to 98±2 kΩ, respectively, at 1 Hz. Similarly, the charge storage capacity is enhanced from 6 to 21 mC cm-2. The PEDOT:PSS microelectrodes provide a low-haze, high-transmittance optical interface, demonstrating their suitability for optical neuroscience applications. They remain functional after a million 1 V stimulation cycles, up to 600 μA of stimulation current, and more than 1000 mechanical bending cycles.
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Affiliation(s)
- Preston D Donaldson
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sarah L Swisher
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
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73
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An evolutionary gap in primate default mode network organization. Cell Rep 2022; 39:110669. [PMID: 35417698 PMCID: PMC9088817 DOI: 10.1016/j.celrep.2022.110669] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 09/21/2021] [Accepted: 03/21/2022] [Indexed: 12/03/2022] Open
Abstract
The human default mode network (DMN) is engaged at rest and in cognitive states such as self-directed thoughts. Interconnected homologous cortical areas in primates constitute a network considered as the equivalent. Here, based on a cross-species comparison of the DMN between humans and non-hominoid primates (macaques, marmosets, and mouse lemurs), we report major dissimilarities in connectivity profiles. Most importantly, the medial prefrontal cortex (mPFC) of non-hominoid primates is poorly engaged with the posterior cingulate cortex (PCC), though strong correlated activity between the human PCC and the mPFC is a key feature of the human DMN. Instead, a fronto-temporal resting-state network involving the mPFC was detected consistently across non-hominoid primate species. These common functional features shared between non-hominoid primates but not with humans suggest a substantial gap in the organization of the primate’s DMN and its associated cognitive functions. By comparing resting-state networks in humans, macaques, marmosets, and mouse lemurs, Garin et al. identify two networks in non-hominoid primates that include homolog areas of the human default mode network. The mPFC and PCC are tightly connected in the human DMN but poorly connected to each other across non-hominoid primates.
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74
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Abnormal white matter structure in hoarding disorder. J Psychiatr Res 2022; 148:1-8. [PMID: 35081485 DOI: 10.1016/j.jpsychires.2022.01.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/23/2021] [Accepted: 01/13/2022] [Indexed: 11/23/2022]
Abstract
Although preliminary neuroimaging research suggests that patients with hoarding disorder (HD) show widespread abnormal task-related activity in the brain, there has been no research on alterations in the white matter tracts in these patients. The aim of this study was to investigate the characteristics of the major white matter tracts in patients with HD. Tract-based spatial statistics were used to search for white matter tract abnormalities throughout the brain in 25 patients with HD and 36 healthy controls. Post hoc analysis of regions of interest was performed to detect correlations with clinical features. Compared with the controls, patients with HD showed decreased fractional anisotropy and increased radial diffusivity in anatomically widespread white matter tracts. Post hoc analysis of regions of interest revealed a significant negative correlation between the severity of hoarding symptoms and fractional anisotropy in the left anterior limb of the internal capsule and a positive correlation between the severity of these symptoms and radial diffusivity in the right anterior thalamic radiation. Patients with HD showed a broad range of alterations in the frontal white matter tracts, including the frontothalamic circuit, frontoparietal network, and frontolimbic pathway. The findings of this study indicate associations between frontal white matter abnormalities related to the severity of hoarding symptoms in HD and the cortical regions involved in cognitive dysfunction. The insights provided would be useful for understanding the neurobiological basis of HD.
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75
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Mapelli L, Soda T, D’Angelo E, Prestori F. The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models. Int J Mol Sci 2022; 23:ijms23073894. [PMID: 35409253 PMCID: PMC8998980 DOI: 10.3390/ijms23073894] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 02/04/2023] Open
Abstract
Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders that include a variety of forms and clinical phenotypes. This heterogeneity complicates the clinical and experimental approaches to ASD etiology and pathophysiology. To date, a unifying theory of these diseases is still missing. Nevertheless, the intense work of researchers and clinicians in the last decades has identified some ASD hallmarks and the primary brain areas involved. Not surprisingly, the areas that are part of the so-called “social brain”, and those strictly connected to them, were found to be crucial, such as the prefrontal cortex, amygdala, hippocampus, limbic system, and dopaminergic pathways. With the recent acknowledgment of the cerebellar contribution to cognitive functions and the social brain, its involvement in ASD has become unmistakable, though its extent is still to be elucidated. In most cases, significant advances were made possible by recent technological developments in structural/functional assessment of the human brain and by using mouse models of ASD. Mouse models are an invaluable tool to get insights into the molecular and cellular counterparts of the disease, acting on the specific genetic background generating ASD-like phenotype. Given the multifaceted nature of ASD and related studies, it is often difficult to navigate the literature and limit the huge content to specific questions. This review fulfills the need for an organized, clear, and state-of-the-art perspective on cerebellar involvement in ASD, from its connections to the social brain areas (which are the primary sites of ASD impairments) to the use of monogenic mouse models.
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Affiliation(s)
- Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
- Correspondence: (L.M.); (F.P.)
| | - Teresa Soda
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
- Brain Connectivity Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
- Correspondence: (L.M.); (F.P.)
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76
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Lam K, Nguyen PT, Anh LV, Lien T. Blended Motor-Sensory Nerve Bundles on Diffused Tensor Imaging: Evidence of Brain Plasticity in a Patient with 36-year Sequelae from Encephalitis. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.8643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: Brain plasticity refers to the extraordinary ability of the brain to modify its structure and function following changes within the body or in the external environment. However, it is not easy to find it on non-invasive imaging modality.
CASE REPORT: In this article, we report the case of a 36-year-old male patient with sequelae of encephalitis. The patient had general epilepsy with multiple hospital admissions. MRI 3.0 Tesla showed his cerebral hemispheres were asymmetrical both morphologically and tractographically; there was a scar at the right temporo-occipital region, and an atrophy of the right temporal lobe, hippocampus and pontine. DTI reconstruction showed asymmetrical cortico-spinal and thalamo-cortical tracts with posterior thalamo-cortical tract was partly damaged by the scar. Blended motor-sensory nerve bundles were observed only on the left side of the patient’s brain but not on the right or healthy subjects. DTI quantification showed the lower line number, lower FA and higher ADC in the patient compared to healthy subjects and within the patient with decreased functionality on the side of the scar.
CONCLUSION: Non-invasive DTI with 3D image reconstruction on the patient showed evidence of brain plasticity appeared on cortico-spinal and thalamo-cortical tracts and can inform diagnosis and treatment strategies.
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Cocuzza CV, Sanchez-Romero R, Cole MW. Protocol for activity flow mapping of neurocognitive computations using the Brain Activity Flow Toolbox. STAR Protoc 2022; 3:101094. [PMID: 35128473 PMCID: PMC8808261 DOI: 10.1016/j.xpro.2021.101094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Traditional cognitive neuroscience uses task-evoked activations to map neurocognitive processes (and information) to brain regions; however, how those processes are generated is unknown. We developed activity flow mapping to identify and empirically validate network mechanisms underlying the generation of neurocognitive processes. This approach models the movement of task-evoked activity over brain connections to predict task-evoked activations. We present a protocol for using the Brain Activity Flow Toolbox (https://colelab.github.io/ActflowToolbox/) to identify network mechanisms underlying neurocognitive processes of interest. For complete details on the use and execution of this protocol, please refer to Cole et al., 2021.
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Affiliation(s)
- Carrisa V. Cocuzza
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
- Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ 07102, USA
| | - Ruben Sanchez-Romero
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
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You P, Li X, Zhang F, Li Q. Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution. BME FRONTIERS 2022; 2022:9814824. [PMID: 37850179 PMCID: PMC10521716 DOI: 10.34133/2022/9814824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/08/2022] [Indexed: 10/19/2023] Open
Abstract
Objective. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. Impact Statement. The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation, an important medical image analysis and neuroscientific problem. Introduction. The concept of "connectional fingerprint" has motivated many investigations on the connectivity-based cortical parcellation, especially with the technical advancement of diffusion imaging. Previous studies on multiple brain regions have been conducted with promising results. However, performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data. Methods. We propose the Spatial-graph Convolution Parcellation (SGCP) framework, a two-stage deep learning-based modeling for the graph representation brain imaging. In the first stage, SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network. In the second stage, SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region. Results. SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset. Performance comparisons between SGCP, traditional parcellation methods, and other deep learning-based methods show that SGCP can achieve superior performance in all the cases. Conclusion. Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.
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Affiliation(s)
- Peiting You
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Beijing International Center for Mathematical Research (BICMR), Peking University, Beijing, China
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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79
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Cai B, Zhou Z, Zhang A, Zhang G, Xiao L, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences. J Neurosci Methods 2022; 372:109539. [PMID: 35219769 DOI: 10.1016/j.jneumeth.2022.109539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. METHODS In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. RESULTS We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. COMPARISON WITH EXISTING METHOD The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. CONCLUSIONS Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.
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80
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Zeng H, Chen S, Fink GR, Weidner R. Information Exchange between Cortical Areas: The Visual System as a Model. Neuroscientist 2022; 29:370-384. [PMID: 35057664 DOI: 10.1177/10738584211069061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As nearly all brain functions, perception, motion, and higher-order cognitive functions require coordinated neural information processing within distributed cortical networks. Over the past decades, new theories and techniques emerged that advanced our understanding of how information is transferred between cortical areas. This review surveys critical aspects of interareal information exchange. We begin by examining the brain’s structural connectivity, which provides the basic framework for interareal communication. We then illustrate information exchange between cortical areas using the visual system as an example. Next, well-studied and newly proposed theories that may underlie principles of neural communication are reviewed, highlighting recent work that offers new perspectives on interareal information exchange. We finally discuss open questions in the study of the neural mechanisms underlying interareal information exchange.
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Affiliation(s)
- Hang Zeng
- Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany
| | - Siyi Chen
- Ludwig-Maximilians-Universität München, München, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany
- Department of Neurology, University Hospital Cologne, Cologne University, Cologne, Germany
| | - Ralph Weidner
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany
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81
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The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology 2022; 47:90-103. [PMID: 34408276 PMCID: PMC8616903 DOI: 10.1038/s41386-021-01152-w] [Citation(s) in RCA: 155] [Impact Index Per Article: 77.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 01/03/2023]
Abstract
Systems neuroscience approaches with a focus on large-scale brain organization and network analysis are advancing foundational knowledge of how cognitive control processes are implemented in the brain. Over the past decade, technological and computational innovations in the study of brain connectivity have led to advances in our understanding of how brain networks function, inspiring new conceptualizations of the role of prefrontal cortex (PFC) networks in the coordination of cognitive control. In this review, we describe six key PFC networks involved in cognitive control and elucidate key principles relevant for understanding how these networks implement cognitive control. Implementation of cognitive control in a constantly changing environment depends on the dynamic and flexible organization of PFC networks. In this context, we describe major empirical and theoretical models that have emerged in recent years and describe how their functional architecture and dynamic organization supports flexible cognitive control. We take an overarching view of advances made in the past few decades and consider fundamental issues regarding PFC network function, global brain dynamics, and cognition that still need to be resolved. We conclude by clarifying important future directions for research on cognitive control and their implications for advancing our understanding of PFC networks in brain disorders.
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82
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Felix C, Folloni D, Chen H, Sallet J, Jerusalem A. White matter tract transcranial ultrasound stimulation, a computational study. Comput Biol Med 2022; 140:105094. [PMID: 34920363 DOI: 10.1016/j.compbiomed.2021.105094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 01/16/2023]
Abstract
Low-intensity transcranial ultrasound stimulation (TUS) is poised to become one of the most promising treatments for neurological disorders. However, while recent animal model experiments have successfully quantified the alterations of the functional activity coupling between a sonicated target cortical region and other cortical regions of interest (ROIs), the varying degree of alteration between these different connections remains unexplained. We hypothesise here that the incidental sonication of the tracts leaving the target region towards the different ROIs could participate in explaining these differences. To this end, we propose a tissue level phenomenological numerical model of the coupling between the ultrasound waves and the white matter electrical activity. The model is then used to reproduce in silico the sonication of the anterior cingulate cortex (ACC) of a macaque monkey and measure the neuromodulation power within the white matter tracts leaving the ACC for five cortical ROIs. The results show that the more induced power a white matter tract proximal to the ACC and connected to a secondary ROI receives, the more altered the connectivity fingerprint of the ACC to this region will be after sonication. These results point towards the need to isolate the sonication to the cortical region and minimise the spillage on the neighbouring tracts when aiming at modulating the target region without losing the functional connectivity with other ROIs. Those results further emphasise the potential role of the white matter in TUS and the need to account for white matter topology when designing TUS protocols.
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Affiliation(s)
- Ciara Felix
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Davide Folloni
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK; Currently: Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Haoyu Chen
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK; Currently: Inserm, Stem Cell and Brain Research Institute, Université Lyon 1, Bron, France
| | - Antoine Jerusalem
- Department of Engineering Science, University of Oxford, Oxford, UK.
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83
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Gray matter volume reduction with different disease duration in trigeminal neuralgia. Neuroradiology 2022; 64:301-311. [PMID: 34453181 PMCID: PMC8397610 DOI: 10.1007/s00234-021-02783-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Structural magnetic resonance imaging is widely used to explore brain gray and white matter structure in trigeminal neuralgia (TN) but has yielded conflicting findings. This study investigated the relationship between disease duration as a clinical feature of TN and changes in brain structure. METHODS We divided 49 TN patients into three groups (TN1-TN3) based on disease duration (TN1 = 1.1 ± 0.7 (0-2) years, TN2 = 4.8 ± 1.5 (3-7) years, TN3 = 15.1 ± 5.5 (10-30) years). We used voxel-based morphometry (VBM) to compare the gray matter volume (GMV) across groups and between TN patients and 18 matched healthy control subjects. RESULTS The TN1 group showed reduced GMV of pain-related regions in the cerebellum; the TN2 group showed reduced GMV in the thalamus and the motor/sensory cortex; and the TN3 group showed reduced GMV in the emotional and reward circuits compared with healthy controls. Similar brain regions, including bilateral hippocampi, caudate, left insular cortex, and medial superior frontal cortex, were affected in TN2 and TN3 compared with TN1. CONCLUSION Disease duration can explain differences in structural alterations-especially in pain-related brain regions-in TN. These results highlight the advanced structural neuroimaging method that are valuable tools to assess the trigeminal system in TN and may further our current understanding of TN pathology.
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84
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Bianco R, Novembre G, Ringer H, Kohler N, Keller PE, Villringer A, Sammler D. Lateral Prefrontal Cortex Is a Hub for Music Production from Structural Rules to Movements. Cereb Cortex 2021; 32:3878-3895. [PMID: 34965579 PMCID: PMC9476625 DOI: 10.1093/cercor/bhab454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
Complex sequential behaviors, such as speaking or playing music, entail flexible rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into movements. We combined music production with multimodal neuroimaging to dissociate high-level structural and low-level motor planning. Pianists played novel musical chord sequences on a muted MR-compatible piano by imitating a model hand on screen. Chord sequences were manipulated in terms of musical harmony and context length to assess structural planning, and in terms of fingers used for playing to assess motor planning. A model of probabilistic sequence processing confirmed temporally extended dependencies between chords, as opposed to local dependencies between movements. Violations of structural plans activated the left inferior frontal and middle temporal gyrus, and the fractional anisotropy of the ventral pathway connecting these two regions positively predicted behavioral measures of structural planning. A bilateral frontoparietal network was instead activated by violations of motor plans. Both structural and motor networks converged in lateral prefrontal cortex, with anterior regions contributing to musical structure building, and posterior areas to movement planning. These results establish a promising approach to study sequence production at different levels of action representation.
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Affiliation(s)
- Roberta Bianco
- UCL Ear Institute, University College London, London WC1X 8EE, UK.,Otto Hahn Research Group Neural Bases of Intonation in Speech and Music, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Giacomo Novembre
- Neuroscience of Perception and Action Lab, Italian Institute of Technology (IIT), Rome 00161, Italy
| | - Hanna Ringer
- Otto Hahn Research Group Neural Bases of Intonation in Speech and Music, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.,Institute of Psychology, University of Leipzig, Leipzig 04109, Germany
| | - Natalie Kohler
- Otto Hahn Research Group Neural Bases of Intonation in Speech and Music, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.,Research Group Neurocognition of Music and Language, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main 60322, Germany
| | - Peter E Keller
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Aarhus 8000, Denmark.,The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW 2751, Australia
| | - Arno Villringer
- Otto Hahn Research Group Neural Bases of Intonation in Speech and Music, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Daniela Sammler
- Otto Hahn Research Group Neural Bases of Intonation in Speech and Music, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.,Research Group Neurocognition of Music and Language, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main 60322, Germany
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85
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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86
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Ge R, Hassel S, Arnott SR, Davis AD, Harris JK, Zamyadi M, Milev R, Frey BN, Strother SC, Müller DJ, Rotzinger S, MacQueen GM, Kennedy SH, Lam RW, Vila-Rodriguez F. Structural covariance pattern abnormalities of insula in major depressive disorder: A CAN-BIND study report. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110194. [PMID: 33296696 DOI: 10.1016/j.pnpbp.2020.110194] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/25/2020] [Accepted: 11/30/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND METHODS Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs. RESULTS The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network. CONCLUSIONS Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.
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Affiliation(s)
- Ruiyang Ge
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | | | - Andrew D Davis
- Department of Psychology, Neuroscience & Behaviour, McMaster University, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | | | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Roumen Milev
- Department of Psychiatry, Queen's University and Providence Care Hospital, Kingston, ON, Canada; Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada
| | | | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
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Blazquez Freches G, Haak KV, Beckmann CF, Mars RB. Connectivity gradients on tractography data: Pipeline and example applications. Hum Brain Mapp 2021; 42:5827-5845. [PMID: 34559432 PMCID: PMC8596970 DOI: 10.1002/hbm.25623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 07/03/2021] [Accepted: 07/30/2021] [Indexed: 11/08/2022] Open
Abstract
Gray matter connectivity can be described in terms of its topographical organization, but the differential role of white matter connections underlying that organization is often unknown. In this study, we propose a method for unveiling principles of organization of both gray and white matter based on white matter connectivity as assessed using diffusion magnetic ressonance imaging (MRI) tractography with spectral embedding gradient mapping. A key feature of the proposed approach is its capacity to project the individual connectivity gradients it reveals back onto its input data in the form of projection images, allowing one to assess the contributions of specific white matter tracts to the observed gradients. We demonstrate the ability of our proposed pipeline to identify connectivity gradients in prefrontal and occipital gray matter. Finally, leveraging the use of tractography, we demonstrate that it is possible to observe gradients within the white matter bundles themselves. Together, the proposed framework presents a generalized way to assess both the topographical organization of structural brain connectivity and the anatomical features driving it.
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Affiliation(s)
- Guilherme Blazquez Freches
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenThe Netherlands
| | - Koen V. Haak
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nufeld Department of Clinical NeurosciencesJohn Radclife Hospital, University of OxfordOxfordUK
| | - Rogier B. Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenThe Netherlands
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Species and individual differences and connectional asymmetry of Broca's area in humans and macaques. Neuroimage 2021; 244:118583. [PMID: 34562577 DOI: 10.1016/j.neuroimage.2021.118583] [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: 07/05/2021] [Revised: 09/11/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023] Open
Abstract
To reveal the connectional specialization of the Broca's area (or its homologue), voxel-wise inter-species and individual differences, and inter-hemispheric asymmetry were respectively inspected in humans and macaques at both whole-brain connectivity and single tract levels. It was discovered that the developed connectivity blueprint approach is able to localize connectionally comparable voxels between the two species in Broca's area, whereas the quantitative differences between blueprints of locationally or connectionally corresponding voxels enable us to generate inter-hemispheric, inter-subject, and inter-species connectional variabilities, respectively. More importantly, the inter-species and inter-subject variabilities exhibited positive correlation in both two primates, and relatively higher variabilities were detected in the anatomically defined pars triangularis. By contrast, negative relationship was identified between the inter-species variability and hemispheric asymmetry in human brain. In particular, relatively higher asymmetry was revealed in the anatomically defined pars opercularis. Therefore, our novel findings demonstrated that pars triangularis, as compared to pars opercularis, might be a more active area during primate evolution, in which the brain connectivity and possible functions of pars triangularis show relatively higher degree in species specialization, yet lower in hemispheric specialization. Meanwhile, brain connectivity and possible functions of pars opercularis manifested an opposite pattern. At the tract level, functional roles related to the ventral stream in speech comprehension were relatively conservative and bilaterally organized, while those related to the dorsal stream in speech production show relatively higher species and hemispheric specializations.
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Parcellation-Free prediction of task fMRI activations from dMRI tractography. Med Image Anal 2021; 76:102317. [PMID: 34871930 DOI: 10.1016/j.media.2021.102317] [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: 02/09/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 11/22/2022]
Abstract
The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.
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Kurokawa R, Kamiya K, Koike S, Nakaya M, Uematsu A, Tanaka SC, Kamagata K, Okada N, Morita K, Kasai K, Abe O. Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition. Neuroimage 2021; 245:118675. [PMID: 34710585 DOI: 10.1016/j.neuroimage.2021.118675] [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: 05/17/2021] [Revised: 09/26/2021] [Accepted: 10/21/2021] [Indexed: 01/18/2023] Open
Abstract
Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.
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Affiliation(s)
- Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan; University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
| | - Moto Nakaya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan.
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Naohiro Okada
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kentaro Morita
- Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kiyoto Kasai
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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91
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Roumazeilles L, Schurz M, Lojkiewiez M, Verhagen L, Schüffelgen U, Marche K, Mahmoodi A, Emberton A, Simpson K, Joly O, Khamassi M, Rushworth MFS, Mars RB, Sallet J. Social prediction modulates activity of macaque superior temporal cortex. SCIENCE ADVANCES 2021; 7:eabh2392. [PMID: 34524842 PMCID: PMC8443173 DOI: 10.1126/sciadv.abh2392] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
The ability to attribute thoughts to others, also called theory of mind (TOM), has been extensively studied in humans; however, its evolutionary origins have been challenged. Computationally, the basis of TOM has been interpreted within the predictive coding framework and associated with activity in the temporoparietal junction (TPJ). Here, we revealed, using a nonlinguistic task and functional magnetic resonance imaging, that activity in a region of the macaque middle superior temporal cortex was specifically modulated by the predictability of social situations. As in human TPJ, this region could be distinguished from other temporal regions involved in face processing. Our result suggests the existence of a precursor for the TOM ability in the last common ancestor of human and Old World monkeys.
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Affiliation(s)
- Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Matthias Schurz
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Institute of Psychology, University of Innsbruck, Innsbruck, Austria
| | - Mathilde Lojkiewiez
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lennart Verhagen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- 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, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Urs Schüffelgen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Kevin Marche
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Ali Mahmoodi
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Andrew Emberton
- Biomedical Sciences Services, University of Oxford, Oxford, UK
| | - Kelly Simpson
- Biomedical Sciences Services, University of Oxford, Oxford, UK
| | - Olivier Joly
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Mehdi Khamassi
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Institute of Intelligent Systems and Robotics, Sorbonne Université, CNRS, Paris, France
| | - Matthew F. S. Rushworth
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- 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, UK
| | - 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, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
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92
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Bazinet V, Vos de Wael R, Hagmann P, Bernhardt BC, Misic B. Multiscale communication in cortico-cortical networks. Neuroimage 2021; 243:118546. [PMID: 34478823 DOI: 10.1016/j.neuroimage.2021.118546] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/27/2021] [Accepted: 08/31/2021] [Indexed: 11/25/2022] Open
Abstract
Signaling in brain networks unfolds over multiple topological scales. Areas may exchange information over local circuits, encompassing direct neighbours and areas with similar functions, or over global circuits, encompassing distant neighbours with dissimilar functions. Here we study how the organization of cortico-cortical networks mediate localized and global communication by parametrically tuning the range at which signals are transmitted on the white matter connectome. We show that brain regions vary in their preferred communication scale. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity: unimodal regions show preference for local communication and multimodal regions show preferences for global communication. We show that these preferences manifest as region- and scale-specific structure-function coupling. Namely, the functional connectivity of unimodal regions emerges from monosynaptic communication in small-scale circuits, while the functional connectivity of transmodal regions emerges from polysynaptic communication in large-scale circuits. Altogether, the present findings reveal that communication preferences are highly heterogeneous across the cortex, shaping regional differences in structure-function coupling.
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Affiliation(s)
- Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital (CHUV-UNIL), Lausanne, Switzerland
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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93
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Braunsdorf M, Blazquez Freches G, Roumazeilles L, Eichert N, Schurz M, Uithol S, Bryant KL, Mars RB. Does the temporal cortex make us human? A review of structural and functional diversity of the primate temporal lobe. Neurosci Biobehav Rev 2021; 131:400-410. [PMID: 34480913 DOI: 10.1016/j.neubiorev.2021.08.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 08/03/2021] [Accepted: 08/30/2021] [Indexed: 10/20/2022]
Abstract
Temporal cortex is a primate specialization that shows considerable variation in size, morphology, and connectivity across species. Human temporal cortex is involved in many behaviors that are considered especially well developed in humans, including semantic processing, language, and theory of mind. Here, we ask whether the involvement of temporal cortex in these behaviors can be explained in the context of the 'general' primate organization of the temporal lobe or whether the human temporal lobe contains unique specializations indicative of a 'step change' in the lineage leading to modern humans. We propose that many human behaviors can be explained as elaborations of temporal cortex functions observed in other primates. However, changes in temporal lobe white matter suggest increased integration of information within temporal cortex and between posterior temporal cortex and other association areas, which likely enable behaviors not possible in other species.
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Affiliation(s)
- Marius Braunsdorf
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands.
| | - Guilherme Blazquez Freches
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Nicole Eichert
- 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
| | - Matthias Schurz
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands; Institute of Psychology, University of Innsbruck, Innsbruck, Austria
| | - Sebo Uithol
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Katherine L Bryant
- 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
| | - Rogier B Mars
- Donders Institute for Brain Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands; 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
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94
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Song L, Yang H, Yang M, Liu D, Ge Y, Long J, Dong P. Professional chess expertise modulates whole brain functional connectivity pattern homogeneity and couplings. Brain Imaging Behav 2021; 16:587-595. [PMID: 34453664 DOI: 10.1007/s11682-021-00537-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 11/26/2022]
Abstract
Previous studies have revealed changed functional connectivity patterns between brain areas in chess players using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in chess players remains unclear. It could provide more convincing evidence for establishing the relationship between long-term chess practice and brain function changes. In this study, we employed newly developed whole brain functional connectivity pattern homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 28 chess master players and 27 healthy novices. Seed-based functional connectivity analysis was used to identify the alteration of corresponding functional couplings. FcHo analysis revealed significantly increased whole brain functional connectivity pattern similarity in anterior cingulate cortex (ACC), anterior middle temporal gyrus (aMTG), primary visual cortex (V1), and decreased FcHo in thalamus and precentral gyrus in chess players. Resting-state functional connectivity analyses identified chess players showing decreased functional connections between V1 and precentral gyrus. Besides, a linear support vector machine (SVM) based classification achieved an accuracy of 85.45%, a sensitivity of 85.71% and a specificity of 85.19% to differentiate chess players from novices by leave-one-out cross-validation. Finally, correlation analyses revealed that the mean FcHo values of thalamus were significantly negatively correlated with the training time. Our findings provide new evidences for the important roles of ACC, aMTG, V1, thalamus and precentral gyrus in chess players. The findings also indicate that long-term professional chess training may enhance the semantic and episodic processing, efficiency of visual-motor transformation, and cognitive ability.
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Affiliation(s)
- Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China.
| | - Huadong Yang
- Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Mingdong Yang
- Shouguang People's Hospital, Shouguang, 262700, China
| | - Dianmei Liu
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, 261031, China
| | - Yanming Ge
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, 261031, China
| | - Jinfeng Long
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University, Weifang, 261053, Shandong, China.
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95
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Asymmetric alterations of white matter integrity in patients with insomnia disorder. Brain Imaging Behav 2021; 16:389-396. [PMID: 34427878 DOI: 10.1007/s11682-021-00512-w] [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] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Despite the adverse consequences of insomnia disorder for both individuals and society, the underlying neurobiological processes are poorly understood. The purpose was to further understand the alterations of white matter tracts in patients with insomnia and their association with sleep variables and also to determine if diffusion tensor imaging measures would be a useful disease marker. Twenty-six patients with insomnia and 26 age-matched healthy volunteers underwent diffusion tensor imaging. We employed an automated probabilistic tractography analysis approach using TRActs Constrained by UnderLying Anatomy (TRACULA) to quantify diffusion measures in major white matter tracts. We found significantly increased fractional anisotropy in the right cingulum-angular bundle and uncinate fasciculus in patients group compared to controls. Moreover, the mean diffusivity and radial diffusivity were reduced in the right cingulum-angular bundle in patients group in comparison with controls. We also found significantly increased fractional anisotropy along the bilateral cingulum-angular bundle and right uncinate fasciculus in patients. Also, mean and radial diffusivity were reduced along the right cingulum-angular bundle in patients group compared to controls. There is a significant positive correlation between fractional anisotropy and Epworth Sleepiness Scale scores. Moreover, there are negative correlations between mean, radial and axial diffusivity and total sleep time and sleep efficiency and also positive correlations between mean, radial and axial diffusivity and duration of disease and Pittsburgh Sleep Quality Index scores. This study showed the importance of examining whole-tract and waypoint white matter integrity in insomnia disorder. We found asymmetric widespread white matter integrity changes in patients with insomnia.
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96
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Russ BE, Petkov CI, Kwok SC, Zhu Q, Belin P, Vanduffel W, Hamed SB. Common functional localizers to enhance NHP & cross-species neuroscience imaging research. Neuroimage 2021; 237:118203. [PMID: 34048898 PMCID: PMC8529529 DOI: 10.1016/j.neuroimage.2021.118203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 11/25/2022] Open
Abstract
Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.
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Affiliation(s)
- Brian E Russ
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States; Department of Psychiatry, New York University at Langone, New York City, NY, United States.
| | - Christopher I Petkov
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, United Kingdom
| | - Sze Chai Kwok
- Shanghai Key Laboratory of Brain Functional Genomics, Key Laboratory of Brain Functional Genomics Ministry of Education, Shanghai Key Laboratory of Magnetic Resonance, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu, China; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China
| | - Qi Zhu
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Laboratory for Neuro-and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium
| | - Pascal Belin
- Institut de Neurosciences de La Timone, Aix-Marseille Université et CNRS, Marseille, 13005, France
| | - Wim Vanduffel
- Laboratory for Neuro-and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA 02144, United States.
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France.
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97
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Levakov G, Faskowitz J, Avidan G, Sporns O. Mapping individual differences across brain network structure to function and behavior with connectome embedding. Neuroimage 2021; 242:118469. [PMID: 34390875 PMCID: PMC8464439 DOI: 10.1016/j.neuroimage.2021.118469] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/29/2021] [Accepted: 08/10/2021] [Indexed: 01/21/2023] Open
Abstract
The connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
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Affiliation(s)
- Gidon Levakov
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Israel; Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel.
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, USA; Program in Neuroscience, Indiana University, USA
| | - Galia Avidan
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Israel; Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel; Department of Psychology, Ben-Gurion University of the Negev, Israel
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, USA; Program in Neuroscience, Indiana University, USA
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98
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Giarrocco F, Averbeck B. Organization of Parieto-Prefrontal and Temporo-Prefrontal Networks in the Macaque. J Neurophysiol 2021; 126:1289-1309. [PMID: 34379536 DOI: 10.1152/jn.00092.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The connectivity among architectonically defined areas of the frontal, parietal, and temporal cortex of the macaque has been extensively mapped through tract tracing methods. To investigate the statistical organization underlying this connectivity, and identify its underlying architecture, we performed a hierarchical cluster analysis on 69 cortical areas based on their anatomically defined inputs. We identified 10 frontal, 4 parietal, and 5 temporal hierarchically related sets of areas (clusters), defined by unique sets of inputs and typically composed of anatomically contiguous areas. Across cortex, clusters that share functional properties were linked by dominant information processing circuits in a topographically organized manner that reflects the organization of the main fiber bundles in the cortex. This led to a dorsal-ventral subdivision of the frontal cortex, where dorsal and ventral clusters showed privileged connectivity with parietal and temporal areas, respectively. Ventrally, temporo-frontal circuits encode information to discriminate objects in the environment, their value, emotional properties, and functions such as memory and spatial navigation. Dorsal parieto-frontal circuits encode information for selecting, generating, and monitoring appropriate actions based on visual-spatial and somatosensory information. This organization may reflect evolutionary antecedents, in which the vertebrate pallium, which is the ancestral cortex, was defined by a ventral and lateral olfactory region and a medial hippocampal region.
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Affiliation(s)
- Franco Giarrocco
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States
| | - Bruno Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States
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99
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The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood. Neuroimage 2021; 242:118448. [PMID: 34358659 DOI: 10.1016/j.neuroimage.2021.118448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 07/03/2021] [Accepted: 08/02/2021] [Indexed: 10/20/2022] Open
Abstract
Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.
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100
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Somers DC, Michalka SW, Tobyne SM, Noyce AL. Individual Subject Approaches to Mapping Sensory-Biased and Multiple-Demand Regions in Human Frontal Cortex. Curr Opin Behav Sci 2021; 40:169-177. [PMID: 34307791 PMCID: PMC8294130 DOI: 10.1016/j.cobeha.2021.05.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Sensory modality, widely accepted as a key factor in the functional organization of posterior cortical areas, also shapes the organization of human frontal lobes. 'Deep imaging,' or the practice of collecting a sizable amount of data on individual subjects, offers significant advantages in revealing fine-scale aspects of functional organization of the human brain. Here, we review deep imaging approaches to mapping multiple sensory-biased and multiple-demand regions within human lateral frontal cortex. In addition, we discuss how deep imaging methods can be transferred to large public data sets to further extend functional mapping at the group level. We also review how 'connectome fingerprinting' approaches, combined with deep imaging, can be used to localize fine-grained functional organization in individual subjects using resting-state data. Finally, we summarize current 'best practices' for deep imaging.
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Affiliation(s)
- David C. Somers
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - Samantha W. Michalka
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
- Olin College of Engineering, Needham, MA, US
| | - Sean M. Tobyne
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
- Physiological Systems – Sensing, Perception and Applied Robotics Division, Charles River Analytics, Inc., Cambridge, MA, USA
| | - Abigail L. Noyce
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
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