1
|
Wang Y, Cheng L, Li D, Lu Y, Wang C, Wang Y, Gao C, Wang H, Vanduffel W, Hopkins WD, Sherwood CC, Jiang T, Chu C, Fan L. Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597252. [PMID: 38895242 PMCID: PMC11185649 DOI: 10.1101/2024.06.03.597252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.
Collapse
Affiliation(s)
- Yufan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yaping Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Chaohong Gao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium
| | - Wim Vanduffel
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium
- Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
| | - William D. Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX 78602, USA
| | - Chet C. Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC 20052, USA
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266000, China
| |
Collapse
|
2
|
Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024:S2095-9273(24)00187-7. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
Collapse
Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| |
Collapse
|
3
|
Zhang Y, Shen SX, Bibic A, Wang X. Evolutionary continuity and divergence of auditory dorsal and ventral pathways in primates revealed by ultra-high field diffusion MRI. Proc Natl Acad Sci U S A 2024; 121:e2313831121. [PMID: 38377216 PMCID: PMC10907247 DOI: 10.1073/pnas.2313831121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Auditory dorsal and ventral pathways in the human brain play important roles in supporting speech and language processing. However, the evolutionary root of the dual auditory pathways in the primate brain is unclear. By parcellating the auditory cortex of marmosets (a New World monkey species), macaques (an Old World monkey species), and humans using the same individual-based analysis method and tracking the pathways from the auditory cortex based on multi-shell diffusion-weighted MRI (dMRI), homologous auditory dorsal and ventral fiber tracks were identified in these primate species. The ventral pathway was found to be well conserved in all three primate species analyzed but extend to more anterior temporal regions in humans. In contrast, the dorsal pathway showed a divergence between monkey and human brains. First, frontal regions in the human brain have stronger connections to the higher-level auditory regions than to the lower-level auditory regions along the dorsal pathway, while frontal regions in the monkey brain show opposite connection patterns along the dorsal pathway. Second, the left lateralization of the dorsal pathway is only found in humans. Moreover, the connectivity strength of the dorsal pathway in marmosets is more similar to that of humans than macaques. These results demonstrate the continuity and divergence of the dual auditory pathways in the primate brains along the evolutionary path, suggesting that the putative neural networks supporting human speech and language processing might have emerged early in primate evolution.
Collapse
Affiliation(s)
- Yang Zhang
- Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - Sherry Xinyi Shen
- Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - Adnan Bibic
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD21205
- Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, F. M. Kirby Center, Baltimore, MD21205
| | - Xiaoqin Wang
- Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD21205
| |
Collapse
|
4
|
Fan C, Xu D, Mei H, Zhong X, Ren J, Ma J, Ruan Z, Lv J, Liu X, Wang H, Gao L, Xu H. Hemispheric coupling between structural and functional asymmetries in clinically asymptomatic carotid stenosis with cognitive impairment. Brain Imaging Behav 2024; 18:192-206. [PMID: 37985612 DOI: 10.1007/s11682-023-00823-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2023] [Indexed: 11/22/2023]
Abstract
Advanced carotid stenosis is a known risk factor for ischemic stroke and vascular dementia, and it is associated with multidomain cognitive impairment as well as asymmetric alterations in hemispheric structure and function. Here we introduced a novel measure-the asymmetry index of amplitude of low-frequency fluctuations (ALFF_AI)-derived from resting-state functional magnetic resonance imaging. This measure captures the hemispheric asymmetry of intrinsic brain activity using high-dimensional registration. We aimed to investigate functional brain asymmetric alterations in patients with severe asymptomatic carotid stenosis (SACS). Furthermore, we extended the analyses of ALFF_AI to different frequencies to detect frequency-specific alterations. Finally, we examined the coupling between hemispheric asymmetric structure and function and the relationship between these results and cognitive tests, as well as the white matter hyperintensity burden. SACS patients presented significantly decreased ALFF_AI in several clusters, including the visual, auditory, parahippocampal, Rolandic, and superior parietal regions. At low frequencies (0.01-0.25 Hz), the ALFF_AI exhibited prominent group differences as frequency increased. Further structure-function coupling analysis indicated that SACS patients had lower coupling in the lateral prefrontal, superior medial frontal, middle temporal, superior parietal, and striatum regions but higher coupling in the lateral occipital regions. These findings suggest that, under potential hemodynamic burden, SACS patients demonstrate asymmetric hemispheric configurations of intrinsic activity patterns and a decoupling between structural and functional asymmetries.
Collapse
Affiliation(s)
- Chenhong Fan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
- The Interventional Diagnostic and Therapeutic Center, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Dan Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Hao Mei
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Jiaojiao Ma
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Zhao Ruan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Jinfeng Lv
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Xitong Liu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Huan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China.
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, 430071, Wuhan City, Hubei Province, China.
| |
Collapse
|
5
|
Wang H, Yu M, Ren J, Zhong X, Xu D, Gao L, Xu H. Neuroanatomical correlates of cognitive impairment following basal ganglia-thalamic post-hemorrhagic stroke: Uncovering network-wide alterations in hemispheric gray matter asymmetry. Brain Res 2023; 1820:148559. [PMID: 37652090 DOI: 10.1016/j.brainres.2023.148559] [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: 04/12/2023] [Revised: 08/16/2023] [Accepted: 08/26/2023] [Indexed: 09/02/2023]
Abstract
Cognitive impairment and recovery are central issues in hemorrhagic stroke. This study aimed to investigate whether post-hemorrhagic stroke cognitive impairment (PhSCI) is associated with cortical gray matter (GM) loss and hemispheric asymmetry changes and whether these changes could predict improvements in cognitive function during the recovery. Nineteen patients with PhSCI, comprising 10 with basal ganglia hemorrhage and 9 with thalamic hemorrhage, were recruited. Among them, 9 completed a course of repetitive transcranial magnetic stimulation (rTMS). Additionally, 19 demographically and comorbidity-matched healthy controls were also included. Structural brain MRI and cognitive assessments were performed. Voxel-wise GM volume and hemispheric asymmetry were analyzed. The PhSCI patients exhibited bilateral, yet asymmetric, GM losses in the hippocampus, fusiform, lateral temporal, prefrontal, somatomotor, and inferior parietal regions. The analysis of GM asymmetry revealed that patients showed rightward GM in the lateral temporal, somatomotor, and inferior parietal regions. Among the 9 PhSCI patients who completed rTMS, there was a marginal trend of regional GM increase and leftward GM, and these changes were in parallel with the improvements in cognitive tests. Further lesion connectivity and metanalytic mapping identified two interconnected systems linked to the lesions, which were anchored in the default mode, somatomotor, and salience/cognitive control networks and in the cognitive domains of memory, language, decision-making, and executive function. In conclusion, PhSCI patients exhibited network-wide cortical GM losses, distal to subcortical hemorrhagic lesions, and hemisphere asymmetry changes. These changes appear to predict rTMS-related cognitive improvements, suggesting that even subcortical focal lesions can lead to alterations in distal cortical neuroanatomical architecture. Our preliminary findings provide new insights into the neuroanatomical basis of PhSCI.
Collapse
Affiliation(s)
- Huan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Minhua Yu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Dan Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China.
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China.
| |
Collapse
|
6
|
Amiez C, Sallet J, Giacometti C, Verstraete C, Gandaux C, Morel-Latour V, Meguerditchian A, Hadj-Bouziane F, Ben Hamed S, Hopkins WD, Procyk E, Wilson CRE, Petrides M. A revised perspective on the evolution of the lateral frontal cortex in primates. SCIENCE ADVANCES 2023; 9:eadf9445. [PMID: 37205762 DOI: 10.1126/sciadv.adf9445] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/14/2023] [Indexed: 05/21/2023]
Abstract
Detailed neuroscientific data from macaque monkeys have been essential in advancing understanding of human frontal cortex function, particularly for regions of frontal cortex without homologs in other model species. However, precise transfer of this knowledge for direct use in human applications requires an understanding of monkey to hominid homologies, particularly whether and how sulci and cytoarchitectonic regions in the frontal cortex of macaques relate to those in hominids. We combine sulcal pattern analysis with resting-state functional magnetic resonance imaging and cytoarchitectonic analysis to show that old-world monkey brains have the same principles of organization as hominid brains, with the notable exception of sulci in the frontopolar cortex. This essential comparative framework provides insights into primate brain evolution and a key tool to drive translation from invasive research in monkeys to human applications.
Collapse
Affiliation(s)
- Céline Amiez
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Jérôme Sallet
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
- Wellcome Integrative Neuroimaging Centre, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
| | - Camille Giacometti
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Charles Verstraete
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Clémence Gandaux
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Valentine Morel-Latour
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille, CNRS, 13331 Marseille, France
- Station de Primatologie CNRS, UPS846, 13790 Rousset, France
- Brain and Language Research Institute, Université Aix-Marseille, CNRS, 13604 Aix-en-Provence, France
| | - Fadila Hadj-Bouziane
- Integrative Multisensory Perception Action and Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), Lyon, France; University of Lyon 1, Lyon, France
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229, CNRS-Université Claude Bernard Lyon I, Bron, France
| | - William D Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, 78602, USA
| | - Emmanuel Procyk
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Charles R E Wilson
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Michael Petrides
- Department of Neurology and Neurosurgery and Department of Psychology, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| |
Collapse
|
7
|
Li B, Solanas MP, Marrazzo G, Raman R, Taubert N, Giese M, Vogels R, de Gelder B. A large-scale brain network of species-specific dynamic human body perception. Prog Neurobiol 2023; 221:102398. [PMID: 36565985 DOI: 10.1016/j.pneurobio.2022.102398] [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: 07/27/2022] [Revised: 11/25/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
This ultrahigh field 7 T fMRI study addressed the question of whether there exists a core network of brain areas at the service of different aspects of body perception. Participants viewed naturalistic videos of monkey and human faces, bodies, and objects along with mosaic-scrambled videos for control of low-level features. Independent component analysis (ICA) based network analysis was conducted to find body and species modulations at both the voxel and the network levels. Among the body areas, the highest species selectivity was found in the middle frontal gyrus and amygdala. Two large-scale networks were highly selective to bodies, dominated by the lateral occipital cortex and right superior temporal sulcus (STS) respectively. The right STS network showed high species selectivity, and its significant human body-induced node connectivity was focused around the extrastriate body area (EBA), STS, temporoparietal junction (TPJ), premotor cortex, and inferior frontal gyrus (IFG). The human body-specific network discovered here may serve as a brain-wide internal model of the human body serving as an entry point for a variety of processes relying on body descriptions as part of their more specific categorization, action, or expression recognition functions.
Collapse
Affiliation(s)
- Baichen Li
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6200 MD, the Netherlands
| | - Marta Poyo Solanas
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6200 MD, the Netherlands
| | - Giuseppe Marrazzo
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6200 MD, the Netherlands
| | - Rajani Raman
- Laboratory for Neuro, and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Nick Taubert
- Section for Computational Sensomotorics, Centre for Integrative Neuroscience & Hertie Institute for Clinical Brain Research, University Clinic Tübingen, Tübingen 72076, Germany
| | - Martin Giese
- Section for Computational Sensomotorics, Centre for Integrative Neuroscience & Hertie Institute for Clinical Brain Research, University Clinic Tübingen, Tübingen 72076, Germany
| | - Rufin Vogels
- Laboratory for Neuro, and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Beatrice de Gelder
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6200 MD, the Netherlands; Department of Computer Science, University College London, London WC1E 6BT, UK.
| |
Collapse
|
8
|
Pulvinar Response Profiles and Connectivity Patterns to Object Domains. J Neurosci 2023; 43:812-826. [PMID: 36596697 PMCID: PMC9899088 DOI: 10.1523/jneurosci.0613-22.2022] [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: 03/28/2022] [Revised: 11/30/2022] [Accepted: 12/10/2022] [Indexed: 01/05/2023] Open
Abstract
Distributed cortical regions show differential responses to visual objects belonging to different domains varying by animacy (e.g., animals vs tools), yet it remains unclear whether this is an organization principle also applying to the subcortical structures. Combining multiple fMRI activation experiments (two main experiments and six validation datasets; 12 females and 9 males in the main Experiment 1; 10 females and 10 males in the main Experiment 2), resting-state functional connectivity, and task-based dynamic causal modeling analysis in human subjects, we found that visual processing of images of animals and tools elicited different patterns of response in the pulvinar, with robust left lateralization for tools, and distinct, bilateral (with rightward tendency) clusters for animals. Such domain-preferring activity distribution in the pulvinar was associated with the magnitude with which the voxels were intrinsically connected with the corresponding domain-preferring regions in the cortex. The pulvinar-to-right-amygdala path showed a one-way shortcut supporting the perception of animals, and the modulation connection from pulvinar to parietal showed an advantage to the perception of tools. These results incorporate the subcortical regions into the object processing network and highlight that domain organization appears to be an overarching principle across various processing stages in the brain.SIGNIFICANCE STATEMENT Viewing objects belonging to different domains elicited different cortical regions, but whether the domain organization applied to the subcortical structures (e.g., pulvinar) was unknown. Multiple fMRI activation experiments revealed that object pictures belonging to different domains elicited differential patterns of response in the pulvinar, with robust left lateralization for tool pictures, and distinct, bilateral (with rightward tendency) clusters for animals. Combining the resting-state functional connectivity and dynamic causal modeling analysis on task-based fMRI data, we found domain-preferring activity distribution in the pulvinar aligned with that in cortical regions. These results highlight the need for coherent visual theories that explain the mechanisms underlying the domain organization across various processing stages.
Collapse
|
9
|
Bruner E, Battaglia-Mayer A, Caminiti R. The parietal lobe evolution and the emergence of material culture in the human genus. Brain Struct Funct 2023; 228:145-167. [PMID: 35451642 DOI: 10.1007/s00429-022-02487-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/24/2022] [Indexed: 02/07/2023]
Abstract
Traditional and new disciplines converge in suggesting that the parietal lobe underwent a considerable expansion during human evolution. Through the study of endocasts and shape analysis, paleoneurology has shown an increased globularity of the braincase and bulging of the parietal region in modern humans, as compared to other human species, including Neandertals. Cortical complexity increased in both the superior and inferior parietal lobules. Emerging fields bridging archaeology and neuroscience supply further evidence of the involvement of the parietal cortex in human-specific behaviors related to visuospatial capacity, technological integration, self-awareness, numerosity, mathematical reasoning and language. Here, we complement these inferences on the parietal lobe evolution, with results from more classical neuroscience disciplines, such as behavioral neurophysiology, functional neuroimaging, and brain lesions; and apply these to define the neural substrates and the role of the parietal lobes in the emergence of functions at the core of material culture, such as tool-making, tool use and constructional abilities.
Collapse
Affiliation(s)
- Emiliano Bruner
- Centro Nacional de Investigación Sobre la Evolución Humana, Burgos, Spain
| | | | - Roberto Caminiti
- Neuroscience and Behavior Laboratory, Istituto Italiano di Tecnologia (IIT), Roma, Italy.
| |
Collapse
|
10
|
Cytoarchitecture, myeloarchitecture, and parcellation of the chimpanzee inferior parietal lobe. Brain Struct Funct 2023; 228:63-82. [PMID: 35676436 DOI: 10.1007/s00429-022-02514-w] [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: 01/08/2022] [Accepted: 05/22/2022] [Indexed: 01/07/2023]
Abstract
The parietal lobe is a region of especially pronounced change in human brain evolution. Based on comparative neuroanatomical studies, the inferior parietal lobe (IPL) has been shown to be disproportionately larger in humans relative to chimpanzees and macaques. However, it remains unclear whether the underlying histological architecture of IPL cortical areas displays human-specific organization. Chimpanzees are among the closest living relatives of humans, making them an ideal comparative species to investigate potential evolutionary changes in the IPL. We parcellated the chimpanzee IPL using cytoarchitecture and myeloarchitecture, in combination with quantitative comparison of cellular features between the identified cortical areas. Four major areas on the lateral convexity of the chimpanzee IPL (PF, PFG, PG, OPT) and two opercular areas (PFOP, PGOP) were identified, similar to what has been observed in macaques. Analysis of the quantitative profiles of cytoarchitecture showed that cell profile density was significantly different in a combination of layers III, IV, and V between bordering cortical areas, and that the density profiles of these six areas supports their classification as distinct. The similarity to macaque IPL cytoarchitecture suggests that chimpanzees share homologous IPL areas. In comparison, human rostral IPL is reported to differ in its anatomical organization and to contain additional subdivisions, such as areas PFt and PFm. These changes in human brain evolution might have been important as tool making capacities became more complex.
Collapse
|
11
|
Cui Z, Pines AR, Larsen B, Sydnor VJ, Li H, Adebimpe A, Alexander-Bloch AF, Bassett DS, Bertolero M, Calkins ME, Davatzikos C, Fair DA, Gur RC, Gur RE, Moore TM, Shanmugan S, Shinohara RT, Vogel JW, Xia CH, Fan Y, Satterthwaite TD. Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth. Biol Psychiatry 2022; 92:973-983. [PMID: 35927072 PMCID: PMC10040299 DOI: 10.1016/j.biopsych.2022.05.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/30/2022] [Accepted: 05/04/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. METHODS The authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology. RESULTS Personalized functional network topography significantly predicted unseen individuals' major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p < .001), which drove prediction of the 4 correlated dimensions. CONCLUSIONS These results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.
Collapse
Affiliation(s)
- Zaixu Cui
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Chinese Institute for Brain Research, Beijing, China.
| | - Adam R Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Dani S Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Max Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jacob W Vogel
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cedric H Xia
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
| |
Collapse
|
12
|
Hopkins WD. Neuroanatomical asymmetries in nonhuman primates in the homologs to Broca's and Wernicke's areas: a mini-review. Emerg Top Life Sci 2022; 6:ETLS20210279. [PMID: 36073786 PMCID: PMC9472819 DOI: 10.1042/etls20210279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/09/2022] [Accepted: 07/12/2022] [Indexed: 01/01/2023]
Abstract
Population-level lateralization in structure and function is a fundamental measure of the human nervous system. To what extent nonhuman primates exhibit similar patterns of asymmetry remains a topic of considerable scientific interest. In this mini-review, a brief summary of findings on brain asymmetries in nonhuman primates in brain regions considered to the homolog's to Broca's and Wernicke's area are presented. Limitations of existing and directions for future studies are discussed in the context of facilitating comparative investigations in primates.
Collapse
Affiliation(s)
- William D. Hopkins
- Department of Comparative Medicine, Michale E Keeling Center for Comparative Medicine and Research, M D Anderson Cancer Center, Bastrop, TX 78602, U.S.A
| |
Collapse
|
13
|
Mulholland MM, Schapiro SJ, Sherwood CC, Hopkins WD. Phenotypic and genetic associations between gray matter covariation and tool use skill in chimpanzees (Pan troglodytes): Repeatability in two genetically isolated populations. Neuroimage 2022; 257:119292. [PMID: 35551989 PMCID: PMC9351395 DOI: 10.1016/j.neuroimage.2022.119292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/13/2022] [Accepted: 05/08/2022] [Indexed: 11/19/2022] Open
Abstract
Humans and chimpanzees both exhibit a diverse set of tool use skills which suggests selection for tool manufacture and use occurred in the common ancestors of the two species. Our group has previously reported phenotypic and genetic associations between tool use skill and gray matter covariation, as quantified by source-based morphometry (SBM), in chimpanzees. As a follow up study, here we evaluated repeatability in heritability in SBM components and their phenotypic association with tool use skill in two genetically independent chimpanzee cohorts. Within the two independent cohorts of chimpanzees, we identified 8 and 16 SBM components, respectively. Significant heritability was evident for multiple SBM components within both cohorts. Further, phenotypic associations between tool use performance and the SBM components were largely consistent between the two cohorts; the most consistent finding being an association between tool use performance and an SBM component including the posterior superior temporal sulcus (STS) and superior temporal gyrus (STG), and the interior and superior parietal regions (p< 0.05). These findings indicate that the STS, STG, and parietal cortices are phenotypically and genetically implicated in chimpanzee tool use abilities.
Collapse
Affiliation(s)
- M M Mulholland
- Department of Comparative Medicine, The University of Texas MD Anderson Cancer Center, 650 Cool Water Drive, Bastrop, TX 78602, USA.
| | - S J Schapiro
- Department of Comparative Medicine, The University of Texas MD Anderson Cancer Center, 650 Cool Water Drive, Bastrop, TX 78602, USA; Department of Experimental Medicine, University of Copenhagen, Copenhagen, Denmark
| | - C C Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC 20052, USA
| | - W D Hopkins
- Department of Comparative Medicine, The University of Texas MD Anderson Cancer Center, 650 Cool Water Drive, Bastrop, TX 78602, USA
| |
Collapse
|
14
|
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.
Collapse
|
15
|
Abstract
Brain asymmetry is a hallmark of the human brain. Recent studies report a certain degree of abnormal asymmetry of brain lateralization between left and right brain hemispheres can be associated with many neuropsychiatric conditions. In this regard, some questions need answers. First, the accelerated brain asymmetry is programmed during the pre-natal period that can be called “accelerated brain decline clock”. Second, can we find the right biomarkers to predict these changes? Moreover, can we establish the dynamics of these changes in order to identify the right time window for proper interventions that can reverse or limit the neurological decline? To find answers to these questions, we performed a systematic online search for the last 10 years in databases using keywords. Conclusion: we need to establish the right in vitro model that meets human conditions as much as possible. New biomarkers are necessary to establish the “good” or the “bad” borders of brain asymmetry at the epigenetic and functional level as early as possible.
Collapse
|
16
|
The human mediodorsal thalamus: Organization, connectivity, and function. Neuroimage 2022; 249:118876. [PMID: 34998970 DOI: 10.1016/j.neuroimage.2022.118876] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/06/2021] [Accepted: 01/04/2022] [Indexed: 01/10/2023] Open
Abstract
The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition.
Collapse
|
17
|
Hemispheric Specialization of the Primate Inferior Parietal Lobule. Neurosci Bull 2021; 38:334-336. [PMID: 34964954 PMCID: PMC8975971 DOI: 10.1007/s12264-021-00807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/08/2021] [Indexed: 10/28/2022] Open
|
18
|
Abstract
Asymmetries in the functional and structural organization of the nervous system are widespread in the animal kingdom and especially characterize the human brain. Although there is little doubt that asymmetries arise through genetic and nongenetic factors, an overarching model to explain the development of functional lateralization patterns is still lacking. Current genetic psychology collects data on genes relevant to brain lateralizations, while animal research provides information on the cellular mechanisms mediating the effects of not only genetic but also environmental factors. This review combines data from human and animal research (especially on birds) and outlines a multi-level model for asymmetry formation. The relative impact of genetic and nongenetic factors varies between different developmental phases and neuronal structures. The basic lateralized organization of a brain is already established through genetically controlled embryonic events. During ongoing development, hemispheric specialization increases for specific functions and subsystems interact to shape the final functional organization of a brain. In particular, these developmental steps are influenced by environmental experiences, which regulate the fine-tuning of neural networks via processes that are referred to as ontogenetic plasticity. The plastic potential of the nervous system could be decisive for the evolutionary success of lateralized brains.
Collapse
|