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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024; 69:2241-2259. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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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.
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Sretavan K, Braun H, Liu Z, Bullock D, Palnitkar T, Patriat R, Chandrasekaran J, Brenny S, Johnson MD, Widge AS, Harel N, Heilbronner SR. A reproducible pipeline for parcellation of the anterior limb of the internal capsule. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00196-4. [PMID: 39053578 DOI: 10.1016/j.bpsc.2024.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/11/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND The anterior limb of the internal capsule (ALIC) is a white matter structure connecting the prefrontal cortex (PFC) to the brainstem, thalamus, and subthalamic nucleus. It is a target for deep brain stimulation (DBS) for obsessive-compulsive disorder. There is strong interest in improving DBS targeting by using diffusion tractography to reconstruct and target specific ALIC fiber pathways, but this methodology is susceptible to errors and lacks validation. To address these limitations, we developed a novel diffusion tractography pipeline that generates reliable and biologically validated ALIC white matter reconstructions. METHODS Following algorithm development and refinement, we analyzed 43 control subjects each with 2 sets of 3T MRI data and a subset of 5 controls with 7T data from the Human Connectome Project. We generated 22 segmented ALIC fiber bundles (11 per hemisphere) based on prefrontal PFC regions of interest, and we analyzed the relationships among bundles. RESULTS We successfully reproduced the topographies established by prior anatomical work using images acquired at both 3T and 7T. Quantitative assessment demonstrated significantly smaller intra-subject variability relative to inter-subject variability for both test and retest groups across all but one PFC region. We examined the overlap between fibers from different PFC regions and a response tract for obsessive-compulsive disorder deep brain stimulation, and we reconstructed the PFC hyperdirect pathway using a modified version of our pipeline. DISCUSSION Our dMRI algorithm reliably generates biologically validated ALIC white matter reconstructions, allowing for more precise modelling of fibers for neuromodulation therapies.
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Affiliation(s)
- Karianne Sretavan
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, Minnesota; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Henry Braun
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Zoe Liu
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Daniel Bullock
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Tara Palnitkar
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Remi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Jayashree Chandrasekaran
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Samuel Brenny
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Alik S Widge
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota; Department of Neurosurgery, University of Minnesota, Minneapolis, Minnesota
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Wang Y, Zhu D, Zhao L, Wang X, Zhang Z, Hu B, Wu D, Zheng W. Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation. Neuroimage 2024; 295:120660. [PMID: 38815676 DOI: 10.1016/j.neuroimage.2024.120660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
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Affiliation(s)
- Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, China; School of Physics, Hangzhou Normal University, Hangzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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Takemura H, Kaneko T, Sherwood CC, Johnson GA, Axer M, Hecht EE, Ye FQ, Leopold DA. A prominent vertical occipital white matter fasciculus unique to primate brains. Curr Biol 2024:S0960-9822(24)00818-2. [PMID: 38991613 DOI: 10.1016/j.cub.2024.06.034] [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: 02/18/2024] [Revised: 06/09/2024] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
Vision in humans and other primates enlists parallel processing streams in the dorsal and ventral visual cortex, known to support spatial and object processing, respectively. These streams are bridged, however, by a prominent white matter tract, the vertical occipital fasciculus (VOF), identified in both classical neuroanatomy and recent diffusion-weighted magnetic resonance imaging (dMRI) studies. Understanding the evolution of the VOF may shed light on its origin, function, and role in visually guided behaviors. To this end, we acquired high-resolution dMRI data from the brains of select mammalian species, including anthropoid and strepsirrhine primates, a tree shrew, rodents, and carnivores. In each species, we attempted to delineate the VOF after first locating the optic radiations in the occipital white matter. In all primate species examined, the optic radiation was flanked laterally by a prominent and coherent white matter fasciculus recognizable as the VOF. By contrast, the equivalent analysis applied to four non-primate species from the same superorder as primates (tree shrew, ground squirrel, paca, and rat) failed to reveal white matter tracts in the equivalent location. Clear evidence for a VOF was also absent in two larger carnivore species (ferret and fox). Although we cannot rule out the existence of minor or differently organized homologous fiber pathways in the non-primate species, the results suggest that the VOF has greatly expanded, or possibly emerged, in the primate lineage. This adaptation likely facilitated the evolution of unique visually guided behaviors in primates, with direct impacts on manual object manipulation, social interactions, and arboreal locomotion.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585, Japan; The Graduate Institute for Advanced Studies, SOKENDAI, Shonan Village, Hayama-cho, Kanagawa 240-0193, Japan; Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita-shi, Osaka 565-0871, Japan.
| | - Takaaki Kaneko
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, 41-2 Kanrin, Inuyama-shi, Aichi 484-8506, Japan; Division of Behavioral Development, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi, Japan
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, 800 22nd St. NW, Washington, DC 20052, USA
| | - G Allan Johnson
- Department of Radiology, Duke Center for In Vivo Microscopy, Duke Medical Center, 311 Research Drive, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, 101 Science Dive., Durham, NC 27705, USA
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany; Department of Physics, School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20 42119, Wuppertal, Germany
| | - Erin E Hecht
- Department of Human Evolutionary Biology, Harvard University, 11 Divinity Avenue, Cambridge, MA 02138, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD 20814, USA; Systems Neurodevelopment Laboratory, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA.
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Feng G, Wang Y, Huang W, Chen H, Cheng J, Shu N. Spatial and temporal pattern of structure-function coupling of human brain connectome with development. eLife 2024; 13:RP93325. [PMID: 38900563 PMCID: PMC11189631 DOI: 10.7554/elife.93325] [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] [Indexed: 06/21/2024] Open
Abstract
Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7-21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
- BABRI Centre, Beijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal UniversityBeijingChina
| | - Yiwen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
- BABRI Centre, Beijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal UniversityBeijingChina
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
- BABRI Centre, Beijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal UniversityBeijingChina
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
- BABRI Centre, Beijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal UniversityBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
- BABRI Centre, Beijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal UniversityBeijingChina
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Skandalakis GP, Linn W, Yeh F, Kazim SF, Komaitis S, Neromyliotis E, Dimopoulos D, Drosos E, Hadjipanayis CG, Kongkham PN, Zadeh G, Stranjalis G, Koutsarnakis C, Kogan M, Evans LT, Kalyvas A. Unveiling the axonal connectivity between the precuneus and temporal pole: Structural evidence from the cingulum pathways. Hum Brain Mapp 2024; 45:e26771. [PMID: 38925589 PMCID: PMC11199201 DOI: 10.1002/hbm.26771] [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: 10/30/2023] [Revised: 04/17/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
Neuroimaging studies have consistently demonstrated concurrent activation of the human precuneus and temporal pole (TP), both during resting-state conditions and various higher-order cognitive functions. However, the precise underlying structural connectivity between these brain regions remains uncertain despite significant advancements in neuroscience research. In this study, we investigated the connectivity of the precuneus and TP by employing parcellation-based fiber micro-dissections in human brains and fiber tractography techniques in a sample of 1065 human subjects and a sample of 41 rhesus macaques. Our results demonstrate the connectivity between the posterior precuneus area POS2 and the areas 35, 36, and TG of the TP via the fifth subcomponent of the cingulum (CB-V) also known as parahippocampal cingulum. This finding contributes to our understanding of the connections within the posteromedial cortices, facilitating a more comprehensive integration of anatomy and function in both normal and pathological brain processes. PRACTITIONER POINTS: Our investigation delves into the intricate architecture and connectivity patterns of subregions within the precuneus and temporal pole, filling a crucial gap in our knowledge. We revealed a direct axonal connection between the posterior precuneus (POS2) and specific areas (35, 35, and TG) of the temporal pole. The direct connections are part of the CB-V pathway and exhibit a significant association with the cingulum, SRF, forceps major, and ILF. Population-based human tractography and rhesus macaque fiber tractography showed consistent results that support micro-dissection outcomes.
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Affiliation(s)
- Georgios P. Skandalakis
- Section of NeurosurgeryDartmouth Hitchcock Medical CenterLebanonNew HampshireUSA
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | - Wen‐Jieh Linn
- Department of Neurological SurgeryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Fang‐Cheng Yeh
- Department of Neurological SurgeryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Syed Faraz Kazim
- Department of NeurosurgeryUniversity of New Mexico HospitalAlbuquerqueNew MexicoUSA
| | - Spyridon Komaitis
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | - Eleftherios Neromyliotis
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | - Dimitrios Dimopoulos
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | - Evangelos Drosos
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | | | - Paul N. Kongkham
- Department of NeurosurgeryToronto Western Hospital, University Health NetworkTorontoOntarioCanada
| | - Gelareh Zadeh
- Department of NeurosurgeryToronto Western Hospital, University Health NetworkTorontoOntarioCanada
| | - George Stranjalis
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | - Christos Koutsarnakis
- Department of NeurosurgeryNational and Kapodistrian University of Athens School of MedicineAthensGreece
| | - Michael Kogan
- Department of NeurosurgeryUniversity of New Mexico HospitalAlbuquerqueNew MexicoUSA
| | - Linton T. Evans
- Section of NeurosurgeryDartmouth Hitchcock Medical CenterLebanonNew HampshireUSA
| | - Aristotelis Kalyvas
- Department of NeurosurgeryToronto Western Hospital, University Health NetworkTorontoOntarioCanada
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Sanda P, Hlinka J, van den Berg M, Skoch A, Bazhenov M, Keliris GA, Krishnan GP. Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression. PLoS Comput Biol 2024; 20:e1012099. [PMID: 38843298 PMCID: PMC11185486 DOI: 10.1371/journal.pcbi.1012099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 06/18/2024] [Accepted: 04/23/2024] [Indexed: 06/19/2024] Open
Abstract
Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.
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Affiliation(s)
- Pavel Sanda
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Georgios A. Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | - Giri P. Krishnan
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Park BS, Lee DA, Lee H, Kim J, Ko J, Lee WH, Yi J, Park KM. Correlation of diffusion tensor tractography with obstructive sleep apnea severity. Brain Behav 2024; 14:e3541. [PMID: 38773829 PMCID: PMC11109523 DOI: 10.1002/brb3.3541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/28/2024] [Accepted: 04/28/2024] [Indexed: 05/24/2024] Open
Abstract
INTRODUCTION Using correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity. METHODS We enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)-values were used in the connectometry analysis. The apnea-hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false-discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections. RESULTS Connectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto-occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI. CONCLUSION Several WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.
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Affiliation(s)
- Bong Soo Park
- Departments of Internal Medicine, Haeundae Paik HospitalInje University College of MedicineBusanSouth Korea
| | - Dong Ah Lee
- Departments of Neurology, Haeundae Paik HospitalInje University College of MedicineBusanSouth Korea
| | - Ho‐Joon Lee
- Departments of Radiology, Haeundae Paik HospitalInje University College of MedicineBusanSouth Korea
| | - Jinseung Kim
- Department of Family Medicine, Busan Paik HospitalInje University College of MedicineBusanRepublic of Korea
| | - Junghae Ko
- Departments of Internal Medicine, Haeundae Paik HospitalInje University College of MedicineBusanSouth Korea
| | - Won Hee Lee
- Department of Neurosurgey, Busan Paik HospitalInje University College of MedicineBusanRepublic of Korea
| | - Jiyae Yi
- Departments of Internal Medicine, Haeundae Paik HospitalInje University College of MedicineBusanSouth Korea
| | - Kang Min Park
- Departments of Neurology, Haeundae Paik HospitalInje University College of MedicineBusanSouth Korea
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9
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Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness. Sci Transl Med 2024; 16:eadj4303. [PMID: 38691619 DOI: 10.1126/scitranslmed.adj4303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.
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Affiliation(s)
- Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Holly J Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Samuel B Snider
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - J Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Robin L Haynes
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Bram R Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Hannah C Kinney
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
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10
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Schmid W, Danstrom IA, Crespo Echevarria M, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. J Neurosci Methods 2024; 405:110106. [PMID: 38453060 PMCID: PMC11233030 DOI: 10.1016/j.jneumeth.2024.110106] [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: 11/03/2023] [Revised: 01/24/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Isabel A Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Garrett P Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Andrew J Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sarah R Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
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11
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Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K. Structural-functional brain network coupling predicts human cognitive ability. Neuroimage 2024; 290:120563. [PMID: 38492685 DOI: 10.1016/j.neuroimage.2024.120563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/14/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.
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Affiliation(s)
- Johanna L Popp
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
| | - Jonas A Thiele
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
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12
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Henssen DJHA, Pritsch C, Nazari P, Mulleners W, Vissers K. The non-decussating and decussating trigeminothalamic tracts in humans: A combination of connectome-based tractography and histological validation. Cephalalgia 2024; 44:3331024241235168. [PMID: 38613234 DOI: 10.1177/03331024241235168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
BACKGROUND Functional anatomical research proposed the existence of a bilateral trigeminal ascending system although the anatomy trajectories of the trigeminothalamic connections cranial to the pons remain largely elusive. This study therefore aimed to clarify the anatomical distributions of the trigeminothalamic connections in humans. METHODS Advanced deterministic tractography to an averaged template of diffusion tensor imaging data from 1065 subjects from the Human Connectome Project was used. Seedings masks were placed in Montreal Neurological Institute standard space by use of the BigBrain histological dataset. Waypoint masks of the sensory thalamus was obtained from the Brainnetome Atlas. RESULTS Tractography results were validated by use of the BigBrain histological dataset and Polarized Light Imaging microscopy. The trigeminothalamic tract bifurcated into a decussating ventral and a non-decussating dorsal tract. The ventral and dorsal tracts ascended to the contralateral thalamus and ipsilateral thalamus and reflected the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract, respectively. The projection of the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract to both thalami confirm the existence of a bilateral trigeminothalamic system in humans. CONCLUSIONS Because our study is strictly anatomical, no further conclusions can be drawn with regard to physiological functionality. Future research should explore if the dorsal trigeminothalamic tract and the ventral trigeminothalamic tract actually transmit signals from noxious stimuli, this offers potential in understanding and possibly treating neuropathology in the orofacial region.
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Affiliation(s)
- Dylan J H A Henssen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition & Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Cynthia Pritsch
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Pouyan Nazari
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Wim Mulleners
- Department of Anesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Kris Vissers
- Department of Anesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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13
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Thng G, Shen X, Stolicyn A, Adams MJ, Yeung HW, Batziou V, Conole ELS, Buchanan CR, Lawrie SM, Bastin ME, McIntosh AM, Deary IJ, Tucker-Drob EM, Cox SR, Smith KM, Romaniuk L, Whalley HC. A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples. Psychol Med 2024:1-12. [PMID: 38497116 DOI: 10.1017/s0033291724000643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.
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Affiliation(s)
- Gladi Thng
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Hon Wah Yeung
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Venia Batziou
- Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Eleanor L S Conole
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Colin R Buchanan
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas, Austin, TX, USA
- Population Research Center and Center on Aging and Population Sciences, University of Texas, Austin, TX, USA
| | - Simon R Cox
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Keith M Smith
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - Liana Romaniuk
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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14
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Xia J, Liu C, Li J, Meng Y, Yang S, Chen H, Liao W. Decomposing cortical activity through neuronal tracing connectome-eigenmodes in marmosets. Nat Commun 2024; 15:2289. [PMID: 38480767 PMCID: PMC10937940 DOI: 10.1038/s41467-024-46651-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Deciphering the complex relationship between neuroanatomical connections and functional activity in primate brains remains a daunting task, especially regarding the influence of monosynaptic connectivity on cortical activity. Here, we investigate the anatomical-functional relationship and decompose the neuronal-tracing connectome of marmoset brains into a series of eigenmodes using graph signal processing. These cellular connectome eigenmodes effectively constrain the cortical activity derived from resting-state functional MRI, and uncover a patterned cellular-functional decoupling. This pattern reveals a spatial gradient from coupled dorsal-posterior to decoupled ventral-anterior cortices, and recapitulates micro-structural profiles and macro-scale hierarchical cortical organization. Notably, these marmoset-derived eigenmodes may facilitate the inference of spontaneous cortical activity and functional connectivity of homologous areas in humans, highlighting the potential generalizing of the connectomic constraints across species. Collectively, our findings illuminate how neuronal-tracing connectome eigenmodes constrain cortical activity and improve our understanding of the brain's anatomical-functional relationship.
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Affiliation(s)
- Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Cirong Liu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, P.R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Siqi Yang
- School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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15
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Feng G, Wang Y, Huang W, Chen H, Cheng J, Shu N. Spatial and temporal pattern of structure-function coupling of human brain connectome with development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.11.557107. [PMID: 38559278 PMCID: PMC10979860 DOI: 10.1101/2023.09.11.557107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7 to 21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yiwen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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16
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Rajeswari J, Jagannath M. Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals. Sci Rep 2024; 14:5561. [PMID: 38448538 PMCID: PMC10917737 DOI: 10.1038/s41598-024-56384-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/05/2024] [Indexed: 03/08/2024] Open
Abstract
Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics-University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics-University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.
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Affiliation(s)
- J Rajeswari
- Department of Electronics and Communication Engineering, Agni College of Technology, Chennai, Tamil Nadu, India
| | - M Jagannath
- School of Electronics Engineering, Vellore Institute of Technology (VIT) Chennai, Chennai, Tamil Nadu, India.
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17
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Xu M, Li X, Teng T, Huang Y, Liu M, Long Y, Lv F, Zhi D, Li X, Feng A, Yu S, Calhoun V, Zhou X, Sui J. Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Netw Open 2024; 7:e241933. [PMID: 38470418 PMCID: PMC10933730 DOI: 10.1001/jamanetworkopen.2024.1933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/13/2024] Open
Abstract
Importance Adolescent major depressive disorder (MDD) is associated with serious adverse implications for brain development and higher rates of self-injury and suicide, raising concerns about its neurobiological mechanisms in clinical neuroscience. However, most previous studies regarding the brain alterations in adolescent MDD focused on single-modal images or analyzed images of different modalities separately, ignoring the potential role of aberrant interactions between brain structure and function in the psychopathology. Objective To examine alterations of structural and functional connectivity (SC-FC) coupling in adolescent MDD by integrating both diffusion magnetic resonance imaging (MRI) and resting-state functional MRI data. Design, Setting, and Participants This cross-sectional study recruited participants aged 10 to 18 years from January 2, 2020, to December 28, 2021. Patients with first-episode MDD were recruited from the outpatient psychiatry clinics at The First Affiliated Hospital of Chongqing Medical University. Healthy controls were recruited by local media advertisement from the general population in Chongqing, China. The sample was divided into 5 subgroup pairs according to different environmental stressors and clinical characteristics. Data were analyzed from January 10, 2022, to February 20, 2023. Main Outcomes and Measures The SC-FC coupling was calculated for each brain region of each participant using whole-brain SC and FC. Primary analyses included the group differences in SC-FC coupling and clinical symptom associations between SC-FC coupling and participants with adolescent MDD and healthy controls. Secondary analyses included differences among 5 types of MDD subgroups: with or without suicide attempt, with or without nonsuicidal self-injury behavior, with or without major life events, with or without childhood trauma, and with or without school bullying. Results Final analyses examined SC-FC coupling of 168 participants with adolescent MDD (mean [mean absolute deviation (MAD)] age, 16.0 [1.7] years; 124 females [73.8%]) and 101 healthy controls (mean [MAD] age, 15.1 [2.4] years; 61 females [60.4%]). Adolescent MDD showed increased SC-FC coupling in the visual network, default mode network, and insula (Cohen d ranged from 0.365 to 0.581; false discovery rate [FDR]-corrected P < .05). Some subgroup-specific alterations were identified via subgroup analyses, particularly involving parahippocampal coupling decrease in participants with suicide attempt (partial η2 = 0.069; 90% CI, 0.025-0.121; FDR-corrected P = .007) and frontal-limbic coupling increase in participants with major life events (partial η2 ranged from 0.046 to 0.068; FDR-corrected P < .05). Conclusions and Relevance Results of this cross-sectional study suggest increased SC-FC coupling in adolescent MDD, especially involving hub regions of the default mode network, visual network, and insula. The findings enrich knowledge of the aberrant brain SC-FC coupling in the psychopathology of adolescent MDD, underscoring the vulnerability of frontal-limbic SC-FC coupling to external stressors and the parahippocampal coupling in shaping future-minded behavior.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yicheng Long
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Hunan, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dongmei Zhi
- International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Sui
- International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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18
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Deuter D, Mederer T, Kohl Z, Forras P, Rosengarth K, Schlabeck M, Röhrl D, Wendl C, Fellner C, Schmidt NO, Schlaier J. Amelioration of Parkinsonian tremor evoked by DBS: which role play cerebello-(sub)thalamic fiber tracts? J Neurol 2024; 271:1451-1461. [PMID: 38032372 PMCID: PMC10896868 DOI: 10.1007/s00415-023-12095-1] [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] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Current pathophysiological models of Parkinson's disease (PD) assume a malfunctioning network being adjusted by the DBS signal. As various authors showed a main involvement of the cerebellum within this network, cerebello-cerebral fiber tracts are gaining special interest regarding the mediation of DBS effects. OBJECTIVES The crossing and non-decussating fibers of the dentato-rubro-thalamic tract (c-DRTT/nd-DRTT) and the subthalamo-ponto-cerebellar tract (SPCT) are thought to build up an integrated network enabling a bidimensional communication between the cerebellum and the basal ganglia. The aim of this study was to investigate the influence of these tracts on clinical control of Parkinsonian tremor evoked by DBS. METHODS We analyzed 120 electrode contacts from a cohort of 14 patients with tremor-dominant or equivalence-type PD having received bilateral STN-DBS. Probabilistic tractography was performed to depict the c-DRTT, nd-DRTT, and SPCT. Distance maps were calculated for the tracts and correlated to clinical tremor control for each electrode pole. RESULTS A significant difference between "effective" and "less-effective" contacts was only found for the c-DRTT (p = 0.039), but not for the SPCT, nor the nd-DRTT. In logistic and linear regressions, significant results were also found for the c-DRTT only (pmodel logistic = 0.035, ptract logistic = 0,044; plinear = 0.027). CONCLUSIONS We found a significant correlation between the distance of the DBS electrode pole to the c-DRTT and the clinical efficacy regarding tremor reduction. The c-DRTT might therefore play a major role in the mechanisms of alleviation of Parkinsonian tremor and could eventually serve as a possible DBS target for tremor-dominant PD in future.
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Affiliation(s)
- Daniel Deuter
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
| | - Tobias Mederer
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Zacharias Kohl
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, Regensburg Medbo District Hospital, Universitätsstraße 84, 93053, Regensburg, Germany
| | - Patricia Forras
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Neurology, Regensburg Medbo District Hospital, Universitätsstraße 84, 93053, Regensburg, Germany
| | - Katharina Rosengarth
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Mona Schlabeck
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Anesthesiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Daniela Röhrl
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Anesthesiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Christina Wendl
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Radiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Department of Radiology, Regensburg Medbo District Hospital, Universitätsstraße 84, 93053, Regensburg, Germany
| | - Claudia Fellner
- Department of Radiology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Nils-Ole Schmidt
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
| | - Jürgen Schlaier
- Department of Neurosurgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
- Center for Deep Brain Stimulation, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany
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Betzel R, Puxeddu MG, Seguin C, Bazinet V, Luppi A, Podschun A, Singleton SP, Faskowitz J, Parakkattu V, Misic B, Markett S, Kuceyeski A, Parkes L. Controlling the human connectome with spatially diffuse input signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.581006. [PMID: 38463980 PMCID: PMC10925126 DOI: 10.1101/2024.02.27.581006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.
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Affiliation(s)
- Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
- Program in Neuroscience, Indiana University, Bloomington IN 47401
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Andrea Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | | | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vibin Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY
- Department of Computational Biology, Cornell University, Ithaca, NY
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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20
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Tsolaki E, Kashanian A, Chiu K, Bari A, Pouratian N. Connectivity-based segmentation of the thalamic motor region for deep brain stimulation in essential tremor: A comparison of deterministic and probabilistic tractography. Neuroimage Clin 2024; 41:103587. [PMID: 38422832 PMCID: PMC10944185 DOI: 10.1016/j.nicl.2024.103587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Deep brain stimulation (DBS) studies have shown that stimulation of the motor segment of the thalamus based on probabilistic tractography is predictive of improvement in essential tremor (ET). However, probabilistic methods are computationally demanding, requiring the need for alternative tractography methods for use in the clinical setting. The purpose of this study was to compare probabilistic vs deterministic tractography methods for connectivity-based targeting in patients with ET. METHODS Probabilistic and deterministic tractography methods were retrospectively applied to diffusion-weighted data sets in 36 patients with refractory ET. The thalamus and precentral gyrus were selected as regions of interest and fiber tracking was performed between these regions to produce connectivity-based thalamic segmentations, per prior methods. The resultant deterministic target maps were compared with those of thresholded probabilistic maps. The center of gravity (CG) of each connectivity map was determined and the differences in spatial distribution between the tractography methods were characterized. Furthermore, the intersection between the connectivity maps and CGs with the therapeutic volume of tissue activated (VTA) was calculated. A mixed linear model was then used to assess clinical improvement in tremor with volume of overlap. RESULTS Both tractography methods delineated the region of the thalamus with connectivity to the precentral gyrus to be within the posterolateral aspect of the thalamus. The average CG of deterministic maps was more medial-posterior in both the left (3.7 ± 1.3 mm3) and the right (3.5 ± 2.2 mm3) hemispheres when compared to 30 %-thresholded probabilistic maps. Mixed linear model showed that the volume of overlap between CGs of deterministic and probabilistic targeting maps and therapeutic VTAs were significant predictors of clinical improvement. CONCLUSIONS Deterministic tractography can reconstruct DBS thalamic target maps in approximately 5 min comparable to those produced by probabilistic methods that require > 12 h to generate. Despite differences in CG between the methods, both deterministic-based and probabilistic targeting were predictive of clinical improvement in ET.
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Affiliation(s)
- Evangelia Tsolaki
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| | - Alon Kashanian
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Kevin Chiu
- Brainlab, Inc., 5 Westbrook Corporate Center, Suite 1000, Westchester, IL 60154, USA
| | - Ausaf Bari
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, USA
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21
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Thompson E, Schroder A, He T, Shand C, Soskic S, Oxtoby NP, Barkhof F, Alexander DC. Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-19. [PMID: 38947941 PMCID: PMC11211996 DOI: 10.1162/imag_a_00089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/07/2023] [Accepted: 01/02/2024] [Indexed: 07/02/2024]
Abstract
Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.
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Affiliation(s)
- Elinor Thompson
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Anna Schroder
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Tiantian He
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Cameron Shand
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Sonja Soskic
- UCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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22
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Liang X, Sun L, Liao X, Lei T, Xia M, Duan D, Zeng Z, Li Q, Xu Z, Men W, Wang Y, Tan S, Gao JH, Qin S, Tao S, Dong Q, Zhao T, He Y. Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence. Nat Commun 2024; 15:784. [PMID: 38278807 PMCID: PMC10817914 DOI: 10.1038/s41467-024-44863-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/08/2024] [Indexed: 01/28/2024] Open
Abstract
Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.
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Affiliation(s)
- Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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Ragone E, Tanner J, Jo Y, Zamani Esfahlani F, Faskowitz J, Pope M, Coletta L, Gozzi A, Betzel R. Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains. Commun Biol 2024; 7:126. [PMID: 38267534 PMCID: PMC10810083 DOI: 10.1038/s42003-024-05766-w] [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: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.
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Affiliation(s)
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
| | - Youngheun Jo
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Farnaz Zamani Esfahlani
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Maria Pope
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Richard Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA.
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24
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Koga SF, Hodges WB, Adamyan H, Hayes T, Fecci PE, Tsvankin V, Pradilla G, Hoang KB, Lee IY, Sankey EW, Codd PJ, Huie D, Zacharia BE, Verma R, Baboyan VG. Preoperative validation of edema-corrected tractography in neurosurgical practice: translating surgeon insights into novel software implementation. Front Neurol 2024; 14:1322815. [PMID: 38259649 PMCID: PMC10801029 DOI: 10.3389/fneur.2023.1322815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Background Peritumoral edema alters diffusion anisotropy, resulting in false negatives in tractography reconstructions negatively impacting surgical decision-making. With supratotal resections tied to survival benefit in glioma patients, advanced diffusion modeling is critical to visualize fibers within the peritumoral zone to prevent eloquent fiber transection thereafter. A preoperative assessment paradigm is therefore warranted to systematically evaluate multi-subject tractograms along clinically meaningful parameters. We propose a novel noninvasive surgically-focused survey to evaluate the benefits of a tractography algorithm for preoperative planning, subsequently applied to Synaptive Medical's free-water correction algorithm developed for clinically feasible single-shell DTI data. Methods Ten neurosurgeons participated in the study and were presented with patient datasets containing histological lesions of varying degrees of edema. They were asked to compare standard (uncorrected) tractography reconstructions overlaid onto anatomical images with enhanced (corrected) reconstructions. The raters assessed the datasets in terms of overall data quality, tract alteration patterns, and the impact of the correction on lesion definition, brain-tumor interface, and optimal surgical pathway. Inter-rater reliability coefficients were calculated, and statistical comparisons were made. Results Standard tractography was perceived as problematic in areas proximal to the lesion, presenting with significant tract reduction that challenged assessment of the brain-tumor interface and of tract infiltration. With correction applied, significant reduction in false negatives were reported along with additional insight into tract infiltration. Significant positive correlations were shown between favorable responses to the correction algorithm and the lesion-to-edema ratio, such that the correction offered further clarification in increasingly edematous and malignant lesions. Lastly, the correction was perceived to introduce false tracts in CSF spaces and - to a lesser degree - the grey-white matter interface, highlighting the need for noise mitigation. As a result, the algorithm was modified by free-water-parameterizing the tractography dataset and introducing a novel adaptive thresholding tool for customizable correction guided by the surgeon's discretion. Conclusion Here we translate surgeon insights into a clinically deployable software implementation capable of recovering peritumoral tracts in edematous zones while mitigating artifacts through the introduction of a novel and adaptive case-specific correction tool. Together, these advances maximize tractography's clinical potential to personalize surgical decisions when faced with complex pathologies.
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Affiliation(s)
- Sebastian F. Koga
- Franciscan Missionaries of Our Lady Health System, Baton Rouge, LA, United States
| | | | | | - Tim Hayes
- Synaptive Medical Inc., Toronto, ON, Canada
| | - Peter E. Fecci
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States
| | - Vadim Tsvankin
- Colorado Brain and Spine Institute, Englewood, CO, United States
| | - Gustavo Pradilla
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Kimberly B. Hoang
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Ian Y. Lee
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, United States
| | - Eric W. Sankey
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States
| | - Patrick J. Codd
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States
| | - David Huie
- Department of Neurosurgery, Duke University Medical Center, Durham, NC, United States
| | - Brad E. Zacharia
- Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, PA, United States
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Cohen Veterans Bioscience, New York, NY, United States
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25
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Stacey P, Mensinkai A, Bansal P, Hosseini SH, Lavigne A, Gwardjan B, Leylachian S, Deng Z(J, Chari V, Giles S, Nesathurai S. Using Nutraceuticals to Help Manage Traumatic Spinal Cord Injury. Pharmaceuticals (Basel) 2024; 17:71. [PMID: 38256904 PMCID: PMC10820888 DOI: 10.3390/ph17010071] [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: 10/21/2023] [Revised: 11/26/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
Traumatic spinal cord injury (TSCI) is a significant public health challenge that has an adverse impact on functional independence, quality of life, and life expectancy. Management of people's chronic conditions is a key aspect of contemporary medical practice. Our study was an open label, single arm, prospective pilot study to evaluate the feasibility of treating people with TSCI. The study intervention was treatment with oral selenium and vitamin E. Participants were 18 years or older and experienced a TSCI at least one year prior to enrollment. Daily doses of 50 mcg of selenium and 400 IU of vitamin E were administered. Participants had radiologic (MRI tractography) and clinical (ASIA) assessments prior to initiating treatment, and these assessments were repeated after one year of treatment. Four subjects completed the full twelve-month study. Adherence, based on pill counts, was approximately 75% in all subjects. There were no adverse events related to study medications. During the treatment period, subjects reported improvement in certain symptoms. There was no significant difference in ASIA scores before and after the intervention. Combination treatment with vitamin E and selenium has been demonstrated as safe for TSCI patients. It is possible to use DTI values to locate the epicenter of a lesion as well as gauge the extent of injury. MRI tractography may serve as a meaningful surrogate endpoint. The results of this study suggest that it is feasible to conduct a larger long-term clinical trial to evaluate the efficacy of combination treatment of TSCI.
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Affiliation(s)
- Paul Stacey
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Arun Mensinkai
- Department of Diagnostic Imaging, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada
- Department of Medical Imaging, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada;
| | - Pankaj Bansal
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Seyed-Hossein Hosseini
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Andrew Lavigne
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Basia Gwardjan
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sayna Leylachian
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Zhihui (Joy) Deng
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Vinjamuri Chari
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sandra Giles
- Department of Medical Imaging, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada;
| | - Shanker Nesathurai
- Department of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON L8L 2X2, Canada (V.C.)
- Division of Physical Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
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26
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Gonzalez Alam TRJ, Cruz Arias J, Jefferies E, Smallwood J, Leemans A, Marino Davolos J. Ventral and dorsal aspects of the inferior frontal-occipital fasciculus support verbal semantic access and visually-guided behavioural control. Brain Struct Funct 2024; 229:207-221. [PMID: 38070006 PMCID: PMC10827863 DOI: 10.1007/s00429-023-02729-5] [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: 08/17/2022] [Accepted: 11/03/2023] [Indexed: 01/31/2024]
Abstract
The Inferior Frontal Occipital Fasciculus (IFOF) is a major anterior-to-posterior white matter pathway in the ventral human brain that connects parietal, temporal and occipital regions to frontal cortex. It has been implicated in a range of functions, including language, semantics, inhibition and the control of action. The recent research shows that the IFOF can be sub-divided into a ventral and dorsal branch, but the functional relevance of this distinction, as well as any potential hemispheric differences, are poorly understood. Using DTI tractography, we investigated the involvement of dorsal and ventral subdivisions of the IFOF in the left and right hemisphere in a response inhibition task (Go/No-Go), where the decision to respond or to withhold a prepotent response was made on the basis of semantic or non-semantic aspects of visual inputs. The task also varied the presentation modality (whether concepts were presented as written words or images). The results showed that the integrity of both dorsal and ventral IFOF in the left hemisphere were associated with participants' inhibition performance when the signal to stop was meaningful and presented in the verbal modality. This effect was absent in the right hemisphere. The integrity of dorsal IFOF was also associated with participants' inhibition efficiency in difficult perceptually guided decisions. This pattern of results indicates that left dorsal IFOF is implicated in the domain-general control of visually-guided behaviour, while the left ventral branch might interface with the semantic system to support the control of action when the inhibitory signal is based on meaning.
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Affiliation(s)
- Tirso R J Gonzalez Alam
- Department of Psychology and York Neuroimaging Centre, University of York, York, UK.
- School of Psychology, Bangor University, Bangor, UK.
| | | | - Elizabeth Jefferies
- Department of Psychology and York Neuroimaging Centre, University of York, York, UK
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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27
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Gajwani M, Oldham S, Pang JC, Arnatkevičiūtė A, Tiego J, Bellgrove MA, Fornito A. Can hubs of the human connectome be identified consistently with diffusion MRI? Netw Neurosci 2023; 7:1326-1350. [PMID: 38144690 PMCID: PMC10631793 DOI: 10.1162/netn_a_00324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/17/2023] [Indexed: 12/26/2023] Open
Abstract
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
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Affiliation(s)
- Mehul Gajwani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - James C. Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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28
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Astolfi P, Verhagen R, Petit L, Olivetti E, Sarubbo S, Masci J, Boscaini D, Avesani P. Supervised tractogram filtering using Geometric Deep Learning. Med Image Anal 2023; 90:102893. [PMID: 37741032 DOI: 10.1016/j.media.2023.102893] [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: 12/01/2022] [Revised: 04/18/2023] [Accepted: 07/07/2023] [Indexed: 09/25/2023]
Abstract
A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.
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Affiliation(s)
- Pietro Astolfi
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; PAVIS, Istituto Italiano di Tecnologia, Geonva, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | | | - Laurent Petit
- GIN, IMN, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Emanuele Olivetti
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | - Silvio Sarubbo
- Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy; Department of Neurosurgery, Azienda Provinciale per i Servizi Sanitari, "Santa Chiara" Hospital, Trento, Italy
| | | | | | - Paolo Avesani
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy.
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29
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Rostampour M, Gharaylou Z, Rostampour A, Shahbodaghy F, Zarei M, Fadaei R, Khazaie H. Study of structural network connectivity using DTI tractography in insomnia disorder. Psychiatry Res Neuroimaging 2023; 336:111730. [PMID: 37944426 DOI: 10.1016/j.pscychresns.2023.111730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/01/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
Most of tractography studies on insomnia disorder (ID) have reported decreased structural connectivity between cortical and subcortical structures. Tractography based on standard diffusion tensor imaging (DTI) can generate high number of false-positive streamlines connections between gray matter regions. In the present study, we employed the convex optimization modeling for microstructure informed tractography-2 (COMMIT2) to improve the accuracy of the reconstructed whole-brain connectome and filter implausible brain connections in 28 patients with ID and compared with 27 healthy controls. Then, we used NBS-predict (a prediction-based extension to the network-based statistic method) in the COMMIT2-weighted connectome. Our results revealed decreased structural connectivity between subregions of the left somatomotor, ventral attention, frontoparietal, dorsal attention and default mode networks in the insomnia group. Moreover, there is a negative correlation between sleep efficiency and structural connectivity within the left frontoparietal, visual, default mode network, limbic, dorsal attention, right dorsal attention as well as right default mode networks. By comparing with standard connectivity analysis, we showed that by removing of false-positive streamlines connections after COMMIT2 filtering, abnormal structural connectivity was reduced in patients with ID compared to controls. Our results demonstrate the importance of improving the accuracy of tractography for understanding structural connectivity networks in ID.
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Affiliation(s)
- Masoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | - Ali Rostampour
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
| | - Fatemeh Shahbodaghy
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Mojtaba Zarei
- Department of Neurology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Reza Fadaei
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
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30
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Deuter D, Torka E, Kohl Z, Schmidt NO, Schlaier J. Mediation of Tremor Control by the Decussating and Nondecussating Part of the Dentato-Rubro-Thalamic Tract in Deep Brain Stimulation in Essential Tremor: Which Part Should Be Stimulated? Neuromodulation 2023; 26:1668-1679. [PMID: 35715283 DOI: 10.1016/j.neurom.2022.04.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/09/2022] [Accepted: 04/11/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVES The dentato-rubro-thalamic tract (DRTT) has been found to play a major role in the mechanisms of tremor alleviation by deep brain stimulation (DBS) in essential tremor (ET). Still, the influence of the two different parts of the DRTT, consisting of crossing and nondecussating fibers, is not yet clear with respect to tremor reduction. The aim of this study was to assess the influence of the crossing and the nondecussating part of the DRTT on tremor control in ET. MATERIALS AND METHODS We investigated 80 electrode contacts in ten patients with ET who received bilateral DBS of the Nucleus ventralis intermedius of the thalamus (VIM). Preoperatively and with patients under general anesthesia, 3T magnetic resonance imaging scans were performed, including Diffusion Tensor Imaging scans with 64 gradient directions. We calculated the course of the two parts of the DRTT based on a workflow for probabilistic fiber tracking including protocols for correction of susceptibility- and eddy current-induced distortions. Distances of electrode contacts were correlated with clinical data from neurologic single pole testing. RESULTS Voltage- and current-steered systems were analyzed separately. Regarding postural tremor, effective contacts showed significantly lower distances to both parts of the DRTT (crossing p < 0.001, nondecussating p < 0.05) in voltage-steered systems. Regarding intentional tremor, significant results were only found for the crossing part (p < 0.01). Regarding both tremor types, effective contacts were closer to the crossing part, unlike less effective contacts. Nonlinear regression analyses using a logistic model showed higher coefficients for the crossing part of the DRTT. Multivariate regression models including distances to both parts of the DRTT showed a significant influence of only the crossing part. Analysis of current-steered systems showed unstable data, probably because of the small number of analyzed patients. CONCLUSIONS Our data suggest an involvement of both parts of the DRTT in tremor reduction, indicating mediation of DBS effects by both fiber bundles, although the crossing part showed stronger correlations with good clinical responses. Nevertheless, special attention should be paid to methodologic aspects when using probabilistic tractography for patient-specific targeting to avoid uncertain and inaccurate results.
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Affiliation(s)
- Daniel Deuter
- Department of Neurosurgery, University of Regensburg Medical Center, Regensburg, Germany; Center for Deep Brain Stimulation, University of Regensburg Medical Center, Regensburg, Germany.
| | - Elisabeth Torka
- Center for Deep Brain Stimulation, University of Regensburg Medical Center, Regensburg, Germany; Department of Neurology, University of Regensburg Medical Center, Regensburg, Germany
| | - Zacharias Kohl
- Center for Deep Brain Stimulation, University of Regensburg Medical Center, Regensburg, Germany; Department of Neurology, University of Regensburg Medical Center, Regensburg, Germany
| | - Nils-Ole Schmidt
- Department of Neurosurgery, University of Regensburg Medical Center, Regensburg, Germany
| | - Juergen Schlaier
- Department of Neurosurgery, University of Regensburg Medical Center, Regensburg, Germany; Center for Deep Brain Stimulation, University of Regensburg Medical Center, Regensburg, Germany
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31
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Park KM, Kim KT, Lee DA, Cho YW. Correlation of Diffusion Tensor Tractography with Restless Legs Syndrome Severity. Brain Sci 2023; 13:1560. [PMID: 38002520 PMCID: PMC10670044 DOI: 10.3390/brainsci13111560] [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: 10/17/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
This prospective study investigated white matter tracts associated with restless legs syndrome (RLS) severity in 69 patients with primary RLS using correlational tractography based on diffusion tensor imaging. Fractional anisotropy (FA) and quantitative anisotropy (QA) were analyzed separately to understand white matter abnormalities in RLS patients. Connectometry analysis revealed positive correlations between RLS severity and FA values in various white matter tracts, including the left and right cerebellum, corpus callosum forceps minor and major, corpus callosum body, right cingulum, and frontoparietal tract. In addition, connectometry analysis revealed that the FA of the middle cerebellar peduncle, left inferior longitudinal fasciculus, left corticospinal tract, corpus callosum forceps minor, right cerebellum, left frontal aslant tract, left dentatorubrothalamic tract, right inferior longitudinal fasciculus, left corticostriatal tract superior, and left cingulum parahippocampoparietal tract was negatively correlated with RLS severity in patients with RLS. However, there were no significant correlations between QA values and RLS severity. It is implied that RLS symptoms may be potentially reversible with appropriate treatment. This study highlights the importance of considering white matter alterations in understanding the pathophysiology of RLS and in developing effective treatment strategies.
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Affiliation(s)
- Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea; (K.M.P.); (D.A.L.)
| | - Keun Tae Kim
- Department of Neurology, Keimyung University School of Medicine, Daegu 42601, Republic of Korea;
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea; (K.M.P.); (D.A.L.)
| | - Yong Won Cho
- Department of Neurology, Keimyung University School of Medicine, Daegu 42601, Republic of Korea;
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32
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Schmid W, Danstrom IA, Echevarria MC, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565525. [PMID: 37986830 PMCID: PMC10659345 DOI: 10.1101/2023.11.03.565525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Isabel A. Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Garrett P. Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sarah R. Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
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33
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Sarwar T, Ramamohanarao K, Daducci A, Schiavi S, Smith RE, Zalesky A. Evaluation of tractogram filtering methods using human-like connectome phantoms. Neuroimage 2023; 281:120376. [PMID: 37714389 DOI: 10.1016/j.neuroimage.2023.120376] [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: 07/19/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023] Open
Abstract
Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic- unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic- unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic- unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic- unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Victoria, 3000, Australia.
| | | | | | - Simona Schiavi
- Department of Computer Science, University of Verona, 37129, Italy
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, 3084, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 2010, Australia
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Howell AM, Warrington S, Fonteneau C, Cho YT, Sotiropoulos SN, Murray JD, Anticevic A. The spatial extent of anatomical connections within the thalamus varies across the cortical hierarchy in humans and macaques. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.22.550168. [PMID: 37546767 PMCID: PMC10401924 DOI: 10.1101/2023.07.22.550168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows them to modulate distributed information flow. However, there is a lack of quantitative investigations into anatomical connectivity patterns within the thalamus. Consequently, it remains unknown if cortical areas exhibit systematic differences in the extent of their anatomical connections within the thalamus. To address this knowledge gap, we used diffusion magnetic resonance imaging (dMRI) to perform brain-wide probabilistic tractography for 828 healthy adults from the Human Connectome Project. We then developed a framework to quantify the spatial extent of each cortical area's anatomical connections within the thalamus. Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy. Our results revealed two distinct corticothalamic tractography motifs: 1) a sensorimotor cortical motif characterized by focal thalamic connections targeting posterolateral thalamus, associated with fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting anteromedial thalamus, associated with slow, feed-back information flow. These findings were consistent across human subjects and were also observed in macaques, indicating cross-species generalizability. Overall, our study demonstrates that sensorimotor and association cortical areas exhibit differences in the spatial extent of their anatomical connections within the thalamus, which may support functionally-distinct cortico-thalamic information flow.
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Affiliation(s)
- Amber M Howell
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, 06511, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, 06511, USA
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, 06511, USA
| | - Youngsun T Cho
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, 06511, USA
| | - 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
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, 06511, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, 06511, USA
- Physics, Yale University, New Haven, Connecticut, 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, 06511, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, 06511, USA
- Department of Psychology, Yale University, New Haven, Connecticut, 06511, USA
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35
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Milisav F, Bazinet V, Iturria-Medina Y, Misic B. Resolving inter-regional communication capacity in the human connectome. Netw Neurosci 2023; 7:1051-1079. [PMID: 37781139 PMCID: PMC10473316 DOI: 10.1162/netn_a_00318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/03/2023] [Indexed: 10/03/2023] Open
Abstract
Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.
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Affiliation(s)
- Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yasser Iturria-Medina
- 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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36
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Nie X, Shi Y. Flow-based Geometric Interpolation of Fiber Orientation Distribution Functions. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14227:46-55. [PMID: 38549783 PMCID: PMC10978007 DOI: 10.1007/978-3-031-43993-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
The fiber orientation distribution function (FOD) is an advanced model for high angular resolution diffusion MRI representing complex fiber geometry. However, the complicated mathematical structures of the FOD function pose challenges for FOD image processing tasks such as interpolation, which plays a critical role in the propagation of fiber tracts in tractography. In FOD-based tractography, linear interpolation is commonly used for numerical efficiency, but it is prone to generate false artificial information, leading to anatomically incorrect fiber tracts. To overcome this difficulty, we propose a flowbased and geometrically consistent interpolation framework that considers peak-wise rotations of FODs within the neighborhood of each location. Our method decomposes a FOD function into multiple components and uses a smooth vector field to model the flows of each peak in its neighborhood. To generate the interpolated result along the flow of each vector field, we develop a closed-form and efficient method to rotate FOD peaks in neighboring voxels and realize geometrically consistent interpolation of FOD components. By combining the interpolation results from each peak, we obtain the final interpolation of FODs. Experimental results on Human Connectome Project (HCP) data demonstrate that our method produces anatomically more meaningful FOD interpolations and significantly enhances tractography performance.
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Affiliation(s)
- Xinyu Nie
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Yonggang Shi
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
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37
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Alushaj E, Hemachandra D, Kuurstra A, Menon RS, Ganjavi H, Sharma M, Kashgari A, Barr J, Reisman W, Khan AR, MacDonald PA. Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder. Neuroimage Clin 2023; 40:103519. [PMID: 37797434 PMCID: PMC10568416 DOI: 10.1016/j.nicl.2023.103519] [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: 07/12/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Dimuthu Hemachandra
- Robarts Research Institute, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alia Kashgari
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Jennifer Barr
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - William Reisman
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
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38
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Hain A, Jörgens D, Moreno R. Randomized iterative spherical-deconvolution informed tractogram filtering. Neuroimage 2023; 278:120248. [PMID: 37423271 DOI: 10.1016/j.neuroimage.2023.120248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/11/2023] Open
Abstract
Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.
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Affiliation(s)
- Antonia Hain
- Saarland University, Faculty of Mathematics and Computer Science, Campus E1.7, Saarbruecken, 66041, Saarland, Germany.
| | - Daniel Jörgens
- Division of Brain, Imaging, and Behaviour, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Huddinge, 14157, Stockholm, Sweden.
| | - Rodrigo Moreno
- KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Huddinge, 14157, Stockholm, Sweden.
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39
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- 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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40
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Girard G, Rafael-Patiño J, Truffet R, Aydogan DB, Adluru N, Nair VA, Prabhakaran V, Bendlin BB, Alexander AL, Bosticardo S, Gabusi I, Ocampo-Pineda M, Battocchio M, Piskorova Z, Bontempi P, Schiavi S, Daducci A, Stafiej A, Ciupek D, Bogusz F, Pieciak T, Frigo M, Sedlar S, Deslauriers-Gauthier S, Kojčić I, Zucchelli M, Laghrissi H, Ji Y, Deriche R, Schilling KG, Landman BA, Cacciola A, Basile GA, Bertino S, Newlin N, Kanakaraj P, Rheault F, Filipiak P, Shepherd TM, Lin YC, Placantonakis DG, Boada FE, Baete SH, Hernández-Gutiérrez E, Ramírez-Manzanares A, Coronado-Leija R, Stack-Sánchez P, Concha L, Descoteaux M, Mansour L S, Seguin C, Zalesky A, Marshall K, Canales-Rodríguez EJ, Wu Y, Ahmad S, Yap PT, Théberge A, Gagnon F, Massi F, Fischi-Gomez E, Gardier R, Haro JLV, Pizzolato M, Caruyer E, Thiran JP. Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge. Neuroimage 2023; 277:120231. [PMID: 37330025 PMCID: PMC10771037 DOI: 10.1016/j.neuroimage.2023.120231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/12/2023] [Accepted: 06/14/2023] [Indexed: 06/19/2023] Open
Abstract
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
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Affiliation(s)
- Gabriel Girard
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Jonathan Rafael-Patiño
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Raphaël Truffet
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
| | - Dogu Baran Aydogan
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Barbara B Bendlin
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ilaria Gabusi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Mario Ocampo-Pineda
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Matteo Battocchio
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Zuzana Piskorova
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Brno Faculty of Electrical Engineering and Communication, Department of mathematics, University of Technology, Brno, Czech Republic
| | - Pietro Bontempi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | | | - Dominika Ciupek
- Sano Centre for Computational Personalised Medicine, Kraków, Poland
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Matteo Frigo
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Sara Sedlar
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | | | - Ivana Kojčić
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Mauro Zucchelli
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Hiba Laghrissi
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France; Institut de Biologie de Valrose, Université Côte d'Azur, Nice, France
| | - Yang Ji
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Rachid Deriche
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy; Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI), Tsinghua University, Beijing, China; Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Nancy Newlin
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Praitayini Kanakaraj
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Timothy M Shepherd
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Dimitris G Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, United States
| | - Fernando E Boada
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Erick Hernández-Gutiérrez
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | | | - Ricardo Coronado-Leija
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Pablo Stack-Sánchez
- Computer Science Department, Centro de Investigación en Matemáticas A.C, Guanajuato, México
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia; School of Biomedical Engineering, The University of Sydney, Sydney, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Kenji Marshall
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; McGill University, Montréal, QC, Canada
| | - Erick J Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Antoine Théberge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Florence Gagnon
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Frédéric Massi
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Elda Fischi-Gomez
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Rémy Gardier
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Juan Luis Villarreal Haro
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Emmanuel Caruyer
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
| | - Jean-Philippe Thiran
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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41
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Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Sustaining wakefulness: Brainstem connectivity in human consciousness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548265. [PMID: 37502983 PMCID: PMC10369992 DOI: 10.1101/2023.07.13.548265] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 μm resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness.
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Affiliation(s)
- Brian L. Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Holly J. Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Samuel B. Snider
- Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - J. Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Yelena G. Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Robin L. Haynes
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Bram R. Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Joseph T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Emery N. Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hannah C. Kinney
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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42
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children with Developmental Language Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548858. [PMID: 37503009 PMCID: PMC10370025 DOI: 10.1101/2023.07.13.548858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of cortico-striatal systems, little is known about the role of the cerebellum in DLD. Cortico-cerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination in this tract, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not cortico-cerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech acquisition and development.
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Affiliation(s)
- Salomi S. Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J. Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J. Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E. Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P. Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E. Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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43
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Taghvaei M, Cook P, Sadaghiani S, Shakibajahromi B, Tackett W, Dolui S, De D, Brown C, Khandelwal P, Yushkevich P, Das S, Wolk DA, Detre JA. Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography. Hum Brain Mapp 2023; 44:3943-3953. [PMID: 37148501 PMCID: PMC10258527 DOI: 10.1002/hbm.26326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/08/2023] Open
Abstract
White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.
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Affiliation(s)
- Mohammad Taghvaei
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip Cook
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shokufeh Sadaghiani
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - William Tackett
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sudipto Dolui
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Debarun De
- Department of Computer EngineeringUniversity of IllinoisUrbanaIllinoisUSA
| | - Christopher Brown
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Pulkit Khandelwal
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul Yushkevich
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu Das
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John A. Detre
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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44
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Skibbe H, Rachmadi MF, Nakae K, Gutierrez CE, Hata J, Tsukada H, Poon C, Schlachter M, Doya K, Majka P, Rosa MGP, Okano H, Yamamori T, Ishii S, Reisert M, Watakabe A. The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain. PLoS Biol 2023; 21:e3002158. [PMID: 37384809 DOI: 10.1371/journal.pbio.3002158] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/11/2023] [Indexed: 07/01/2023] Open
Abstract
The primate brain has unique anatomical characteristics, which translate into advanced cognitive, sensory, and motor abilities. Thus, it is important that we gain insight on its structure to provide a solid basis for models that will clarify function. Here, we report on the implementation and features of the Brain/MINDS Marmoset Connectivity Resource (BMCR), a new open-access platform that provides access to high-resolution anterograde neuronal tracer data in the marmoset brain, integrated to retrograde tracer and tractography data. Unlike other existing image explorers, the BMCR allows visualization of data from different individuals and modalities in a common reference space. This feature, allied to an unprecedented high resolution, enables analyses of features such as reciprocity, directionality, and spatial segregation of connections. The present release of the BMCR focuses on the prefrontal cortex (PFC), a uniquely developed region of the primate brain that is linked to advanced cognition, including the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset. Moreover, the inclusion of tractography data from diffusion MRI allows systematic analyses of this noninvasive modality against gold-standard cellular connectivity data, enabling detection of false positives and negatives, which provide a basis for future development of tractography. This paper introduces the BMCR image preprocessing pipeline and resources, which include new tools for exploring and reviewing the data.
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Affiliation(s)
- Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | | | - Ken Nakae
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Aichi, Japan
| | - Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Hiromichi Tsukada
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
- Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai, Aichi, Japan
| | - Charissa Poon
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Matthias Schlachter
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
| | - Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Australia
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Australia
| | - Marcello G P Rosa
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Australia
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Australia
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Tetsuo Yamamori
- Laboratory of Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Animals, Kawasaki, Japan
| | - Shin Ishii
- Department of Systems Science, Kyoto University, Kyoto, Japan
| | - Marco Reisert
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Stereotactic and Functional Neurosurgery, Medical Center of the University of Freiburg, Freiburg Im Breisgau, Germany
- Medical Faculty of the University of Freiburg, Freiburg Im Breisgau, Germany
- Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center-University of Freiburg, Freiburg Im Breisgau, Germany
| | - Akiya Watakabe
- Laboratory of Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama, Japan
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45
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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46
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Arefin TM, Lee CH, Liang Z, Rallapalli H, Wadghiri YZ, Turnbull DH, Zhang J. Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI. Neuroimage 2023; 273:120111. [PMID: 37060936 PMCID: PMC10149621 DOI: 10.1016/j.neuroimage.2023.120111] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/29/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.
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Affiliation(s)
- Tanzil Mahmud Arefin
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States; Center for Neurotechnology in Mental Health Research, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Choong Heon Lee
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Zifei Liang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Harikrishna Rallapalli
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Youssef Z Wadghiri
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Daniel H Turnbull
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Jiangyang Zhang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States.
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47
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Bazinet V, Hansen JY, Vos de Wael R, Bernhardt BC, van den Heuvel MP, Misic B. Assortative mixing in micro-architecturally annotated brain connectomes. Nat Commun 2023; 14:2850. [PMID: 37202416 DOI: 10.1038/s41467-023-38585-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 05/08/2023] [Indexed: 05/20/2023] Open
Abstract
The wiring of the brain connects micro-architecturally diverse neuronal populations, but the conventional graph model, which encodes macroscale brain connectivity as a network of nodes and edges, abstracts away the rich biological detail of each regional node. Here, we annotate connectomes with multiple biological attributes and formally study assortative mixing in annotated connectomes. Namely, we quantify the tendency for regions to be connected based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species, and consider a range of molecular, cellular, and laminar annotations. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections and find that the arrangement of connections with respect to biological annotations is associated to patterns of regional functional specialization. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.
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Affiliation(s)
- Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Justine Y Hansen
- 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
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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48
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Zhu X, Yan H, Zhan Y, Feng F, Wei C, Yao YG, Liu C. An anatomical and connectivity atlas of the marmoset cerebellum. Cell Rep 2023; 42:112480. [PMID: 37163375 DOI: 10.1016/j.celrep.2023.112480] [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: 10/18/2022] [Revised: 02/01/2023] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution ex vivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.
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Affiliation(s)
- Xiaojia Zhu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haotian Yan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yafeng Zhan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Furui Feng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chuanyao Wei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Cirong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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49
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Fountain C, Ghuman H, Paldino M, Tamber M, Panigrahy A, Modo M. Acquisition and Analysis of Excised Neocortex from Pediatric Patients with Focal Cortical Dysplasia Using Mesoscale Diffusion MRI. Diagnostics (Basel) 2023; 13:1529. [PMID: 37174921 PMCID: PMC10177920 DOI: 10.3390/diagnostics13091529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/01/2023] [Accepted: 04/15/2023] [Indexed: 05/15/2023] Open
Abstract
Non-invasive classification of focal cortical dysplasia (FCD) subtypes remains challenging from a radiology perspective. Quantitative imaging biomarkers (QIBs) have the potential to distinguish subtypes that lack pathognomonic features and might help in defining the extent of abnormal connectivity associated with each FCD subtype. A key motivation of diagnostic imaging is to improve the localization of a "lesion" that can guide the surgical resection of affected tissue, which is thought to cause seizures. Conversely, surgical resections to eliminate or reduce seizures provided unique opportunities to develop magnetic resonance imaging (MRI)-based QIBs by affording long scan times to evaluate multiple contrast mechanisms at the mesoscale (0.5 mm isotropic voxel dimensions). Using ex vivo hybrid diffusion tensor imaging on a 9.4 T MRI scanner, the grey to white matter ratio of scalar indices was lower in the resected middle temporal gyrus (MTG) of two neuropathologically confirmed cases of FCD compared to non-diseased control postmortem fixed temporal lobes. In contrast, fractional anisotropy was increased within FCD and also adjacent white matter tracts. Connectivity (streamlines/mm3) in the MTG was higher in FCD, suggesting that an altered connectivity at the lesion locus can potentially provide a tangible QIB to distinguish and characterize FCD abnormalities. However, as illustrated here, a major challenge for a robust tractographical comparison lies in the considerable differences in the ex vivo processing of bioptic and postmortem samples. Mesoscale diffusion MRI has the potential to better define and characterize epileptic tissues obtained from surgical resection to advance our understanding of disease etiology and treatment.
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Affiliation(s)
- Chandler Fountain
- Department of Radiology and Medical Imaging, University of Virginia Health System, 1215 Lee St, Chartlottesville, VA 22903, USA
| | - Harmanvir Ghuman
- Department of Bioengineering, University of Pittsburgh, 302 Benedum Hall, 3700 O’Hara Street, Pititsburgh, PA 15260, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, 450 Technology Drive, Suite 300, Pittsburgh, PA 15219, USA
| | - Michael Paldino
- Department of Radiology, University of Pittsburgh, PUH Suite E204, 200 Lothrop Street, Pittsburgh, PA 15213, USA
| | - Mandeep Tamber
- Department of Neurological Surgery, University of Pittsburgh, 200 Lothrop Street, Suite B 400, Pittsburgh, PA 15213, USA
| | - Ashok Panigrahy
- Department of Radiology, University of Pittsburgh, PUH Suite E204, 200 Lothrop Street, Pittsburgh, PA 15213, USA
| | - Michel Modo
- Department of Bioengineering, University of Pittsburgh, 302 Benedum Hall, 3700 O’Hara Street, Pititsburgh, PA 15260, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, 450 Technology Drive, Suite 300, Pittsburgh, PA 15219, USA
- Department of Radiology, University of Pittsburgh, PUH Suite E204, 200 Lothrop Street, Pittsburgh, PA 15213, USA
- Centre for the Neural Basis of Behavior, University of Pittsburgh and Carnegie Mellon University, 4074 Biomedical Science Tower 3, Pittsburgh, PA 15261, USA
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50
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Liang Z, Arefin TM, Lee CH, Zhang J. Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI. Neuroimage 2023; 270:119999. [PMID: 36871795 PMCID: PMC10052941 DOI: 10.1016/j.neuroimage.2023.119999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.
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Affiliation(s)
- Zifei Liang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Tanzil Mahmud Arefin
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Choong H Lee
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Jiangyang Zhang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA.
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