101
|
Robinson PA, Henderson JA, Gabay NC, Aquino KM, Babaie-Janvier T, Gao X. Determination of Dynamic Brain Connectivity via Spectral Analysis. Front Hum Neurosci 2021; 15:655576. [PMID: 34335207 PMCID: PMC8323754 DOI: 10.3389/fnhum.2021.655576] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/03/2021] [Indexed: 11/30/2022] Open
Abstract
Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.
Collapse
Affiliation(s)
- Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - James A Henderson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Kevin M Aquino
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Xiao Gao
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia.,Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, Australia
| |
Collapse
|
102
|
Sorrentino P, Seguin C, Rucco R, Liparoti M, Troisi Lopez E, Bonavita S, Quarantelli M, Sorrentino G, Jirsa V, Zalesky A. The structural connectome constrains fast brain dynamics. eLife 2021; 10:67400. [PMID: 34240702 PMCID: PMC8294846 DOI: 10.7554/elife.67400] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 07/07/2021] [Indexed: 02/07/2023] Open
Abstract
Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
Collapse
Affiliation(s)
- Pierpaolo Sorrentino
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy.,Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy.,Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
| | - Caio Seguin
- University of Melbourne, Melbourne, Australia
| | - Rosaria Rucco
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy.,Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy.,Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | - Emahnuel Troisi Lopez
- Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy.,Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
| | | | - Mario Quarantelli
- Biostructure and Bioimaging Institute, National Research Council, Naples, Italy
| | - Giuseppe Sorrentino
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
| | - Viktor Jirsa
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | | |
Collapse
|
103
|
Chiêm B, Crevecoeur F, Delvenne JC. Structure-informed functional connectivity driven by identifiable and state-specific control regions. Netw Neurosci 2021; 5:591-613. [PMID: 34189379 PMCID: PMC8233121 DOI: 10.1162/netn_a_00192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/17/2021] [Indexed: 11/19/2022] Open
Abstract
Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states. Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.
Collapse
Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Jean-Charles Delvenne
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| |
Collapse
|
104
|
Barakovic M, Girard G, Schiavi S, Romascano D, Descoteaux M, Granziera C, Jones DK, Innocenti GM, Thiran JP, Daducci A. Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts. Front Neurosci 2021; 15:646034. [PMID: 34211362 PMCID: PMC8239216 DOI: 10.3389/fnins.2021.646034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/17/2021] [Indexed: 12/30/2022] Open
Abstract
In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.
Collapse
Affiliation(s)
- Muhamed Barakovic
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Gabriel Girard
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Simona Schiavi
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Computer Science, University of Verona, Verona, Italy
| | - David Romascano
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Giorgio M. Innocenti
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | | |
Collapse
|
105
|
Liu X, He C, Fan D, Zhu Y, Zang F, Wang Q, Zhang H, Zhang Z, Zhang H, Xie C. Disrupted rich-club network organization and individualized identification of patients with major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110074. [PMID: 32818534 DOI: 10.1016/j.pnpbp.2020.110074] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 07/14/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural brain network analyses, and the associated connections of these regions are particularly susceptible to depression remain unclear. METHODS We acquired resting-state functional magnetic resonance imaging (R-fMRI) and diffusion tensor imaging (DTI) from 31 unmedicated MDD patients and 32 cognitively normal (CN) subjects and completed a series of neuropsychological tests. Rich-club organization, network properties, and coupling between structural and functional connectivity (SC-FC) were explored. Furthermore, whether these indices could potentially deliver effective clinical predictive value for MDD patients were examined. RESULTS The MDD patients showed disrupted structural rich-club organization and modularity, as well as a distinct correlation pattern between global efficiency and rich-club organization. Importantly, reduced SC-FC coupling, reflecting a decreased agreement in the integrity of the networks, was significantly associated with the strength of structural rich-club connections in the MDD patients. Furthermore, the disrupted structural rich-club organization, which was primarily located in the default mode network (DMN) and executive control network (ECN), emerged as a valuable indicator to distinguish between MDD and CN. CONCLUSIONS Findings of this study identified that the disrupted rich-club structural organization significantly influenced brain structural network modularity and integrity and could serve as a promising biological marker for the identification of MDD patients.
Collapse
Affiliation(s)
- Xinyi Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Yao Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Feifei Zang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Haisan Zhang
- Psychology School of Xinxiang Medical University, Xinxiang, Henan 453003, China; Xinxiang Key Laboratory of Multimodal Brain Imaging, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, Henan 45300, China; Department of Psychiatry, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, Henan 45300, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Neuropsychiatric Institute, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China
| | - Hongxing Zhang
- Psychology School of Xinxiang Medical University, Xinxiang, Henan 453003, China; Xinxiang Key Laboratory of Multimodal Brain Imaging, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, Henan 45300, China; Department of Psychiatry, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, Henan 45300, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Neuropsychiatric Institute, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China.
| |
Collapse
|
106
|
Song Y, Zhou D, Li S. Maximum Entropy Principle Underlies Wiring Length Distribution in Brain Networks. Cereb Cortex 2021; 31:4628-4641. [PMID: 33999124 DOI: 10.1093/cercor/bhab110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 11/14/2022] Open
Abstract
A brain network comprises a substantial amount of short-range connections with an admixture of long-range connections. The portion of long-range connections in brain networks is observed to be quantitatively dissimilar across species. It is hypothesized that the length of connections is constrained by the spatial embedding of brain networks, yet fundamental principles that underlie the wiring length distribution remain unclear. By quantifying the structural diversity of a brain network using Shannon's entropy, here we show that the wiring length distribution across multiple species-including Drosophila, mouse, macaque, human, and C. elegans-follows the maximum entropy principle (MAP) under the constraints of limited wiring material and the spatial locations of brain areas or neurons. In addition, by considering stochastic axonal growth, we propose a network formation process capable of reproducing wiring length distributions of the 5 species, thereby implementing MAP in a biologically plausible manner. We further develop a generative model incorporating MAP, and show that, for the 5 species, the generated network exhibits high similarity to the real network. Our work indicates that the brain connectivity evolves to be structurally diversified by maximizing entropy to support efficient interareal communication, providing a potential organizational principle of brain networks.
Collapse
Affiliation(s)
- Yuru Song
- Neuroscience Graduate Program, University of California, San Diego, CA, USA
| | - Douglas Zhou
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Songting Li
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
107
|
Sun J, Zhang Q, Li Y, Meng J, Chen Q, Yang W, Wei D, Qiu J. Plasticity of the resting-state brain: static and dynamic functional connectivity change induced by divergent thinking training. Brain Imaging Behav 2021; 14:1498-1506. [PMID: 30868403 DOI: 10.1007/s11682-019-00077-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Creativity is very important and is linked to almost all areas of our everyday life. Improving creativity brings great benefits. Various strategies and training paradigms have been used to stimulate creative thinking. These training approaches have been confirmed to be effective. However, whether or not training can reshape the resting-state brain is still unclear. The present study examined whether or not the divergent thinking training intervention can reshape the resting-state brain functional connectivity (FC). Static seed-based and dynamic approaches were used to explore this problem. Results demonstrate significant changes in static and dynamic FCs. FCs, such as dorsal anterior cingulate cortex-inferior parietal lobule, dorsal anterior cingulate cortex-precuneus and left and right dorsolateral prefrontal cortex, was significantly improved through the training. Furthermore, the temporal variability of the supplementary motor area and middle temporal gyrus was improved. These results indicate that divergent thinking training may lead to resting-state brain plasticity. Considering the role of these regions in brain networks, the present study further confirms the close relationship between the brain networks' dynamic interactions and divergent thinking processes.
Collapse
Affiliation(s)
- Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Qinglin Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Yu Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Jie Meng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China. .,Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing, 400715, China.
| |
Collapse
|
108
|
Boshkovski T, Kocarev L, Cohen-Adad J, Mišić B, Lehéricy S, Stikov N, Mancini M. The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure. Netw Neurosci 2021; 5:358-372. [PMID: 34189369 PMCID: PMC8233108 DOI: 10.1162/netn_a_00179] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/11/2020] [Indexed: 11/05/2022] Open
Abstract
Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture. In the present work, we show that by using a myelin-sensitive measure we can complement the diffusion MRI-based connectivity and provide a different picture of the brain organization. We show that the R1-weighted average distribution does not follow the same trend as the number of streamlines strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions have higher R1-weighted average but fewer streamlines. This could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions rely on connections with higher myelination.
Collapse
Affiliation(s)
| | - Ljupco Kocarev
- Macedonian Academy of Sciences and Arts, Skopje, Macedonia
| | | | | | - Stéphane Lehéricy
- Paris Brain Institute (ICM), Centre for NeuroImaging Research (CENIR), Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013, Paris, France
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada
| | - Matteo Mancini
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada
| |
Collapse
|
109
|
Yang Y, Chattun MR, Yan R, Zhao K, Chen Y, Zhu R, Shi J, Wang X, Lu Q, Yao Z. Atrophy of right inferior frontal orbital gyrus and frontoparietal functional connectivity abnormality in depressed suicide attempters. Brain Imaging Behav 2021; 14:2542-2552. [PMID: 32157476 DOI: 10.1007/s11682-019-00206-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Although structural and functional brain abnormalities have been observed in depressed suicide attempters (DS), structural deficits and functional impairments together with their relationship in DS remain unclear. To clarify this issue, we aimed to examine the differences in gray matter (GM) alteration, corresponding functional connectivity (FC) change, and their relationship between DS and depressed non-suicide attempters (NDS). Sixty-eight DS, 119 NDS and 103 healthy controls were enrolled and subjected to magnetic resonance imaging scans. The patients were evaluated using the 17-item Hamilton Rating Scale for Depression (HRSD) and Nurses' Global Assessment of Suicide Risk (NGASR) scale. Both voxel-based morphometry and resting-state FC analyses were performed based on functional and structural imaging data. Compared with NDS, the DS group showed reduced GM volume in the right inferior frontal orbital gyrus (IFOG) and left caudate (CAU) but increased GM volume in the left calcarine fissure, weaker negative right IFOG-left rectus gyrus (REG) FC, and weaker positive right IFOG-left inferior parietal lobule (IPL) FC. In DS, the GM volume of the right IFOG and left CAU was negatively correlated with NGASR and HRSD scores, respectively; the right IFOG-left IPL FC was negatively correlated with cognitive factor scores; and the GM volume of the right IFOG was positively correlated with IFOG-REG and IFOG-IPL FC. Our findings indicate that structural deficit with its related functional alterations in brain circuits converged in right IFOG centralized pathways and may play a central role in suicidal behaviors in depression.
Collapse
Affiliation(s)
- Yuyin Yang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Ke Zhao
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325000, China
| | - Yu Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rongxin Zhu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiabo Shi
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xinyi Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, 210096, China. .,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China.
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China. .,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China.
| |
Collapse
|
110
|
Constructing the rodent stereotaxic brain atlas: a survey. SCIENCE CHINA-LIFE SCIENCES 2021; 65:93-106. [PMID: 33860452 DOI: 10.1007/s11427-020-1911-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/03/2021] [Indexed: 12/22/2022]
Abstract
The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience. Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing: brain image, atlas, and stereotaxis. We also refine four technical indices for evaluating the construction of atlases: the quality of staining and labeling, the granularity of delineation, spatial resolution, and the precision of spatial location and orientation. Additionally, we discuss state-of-the-art technologies and their trends in the fields of image acquisition, stereotaxic coordinate construction, image processing, anatomical structure recognition, and publishing: the procedures of brain atlas illustration. We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas, which will greatly benefit the development of neuroscience.
Collapse
|
111
|
Chen H, Geng W, Shang S, Shi M, Zhou L, Jiang L, Wang P, Yin X, Chen YC. Alterations of brain network topology and structural connectivity-functional connectivity coupling in capsular versus pontine stroke. Eur J Neurol 2021; 28:1967-1976. [PMID: 33657258 DOI: 10.1111/ene.14794] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/25/2021] [Accepted: 02/27/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND AND PURPOSE This study was conducted to investigate whether capsular stroke (CS) and pontine stroke (PS) have different topological alterations of structural connectivity (SC) and functional connectivity (FC), as well as correlations of SC-FC coupling with movement assessment scores. METHODS Resting-state functional magnetic resonance imaging and diffusion tensor imaging were prospectively acquired in 46 patients with CS, 36 with PS, and 29 healthy controls (HCs). Graph theoretical network analyses of SC and FC were performed. Patients with left and right lesions were analyzed separately. RESULTS With regard to FC, the PS and CS groups both showed higher local efficiency than the HCs, and the CS group also had a higher clustering coefficient (Cp) than the HCs in the right lesion analysis. With regard to SC, the PS and CS groups both showed different normalized clustering coefficient (γ), small-worldness (σ), and characteristic path length (Lp) compared with the HC group. Additionally, the CS group showed higher normalized characteristic path length (λ) and a lower Cp than the HCs and the PS group showed higher λ and lower global efficiency than the HCs in the right-lesion analysis. However, γ, σ, Cp and Lp were only significantly different in the PS and CS groups compared with the HC group in the right-lesion analysis. Importantly, the CS group was found to have a weaker SC-FC coupling than the PS group and the HC group in the right-lesion analysis. In addition, both patient groups had weaker structural-functional connectome correlation than the HCs. CONCLUSIONS The CS and PS groups both showed FC and SC disruption and the CS group had a weaker SC-FC coupling than the PS group in the right lesion analysis. This may provide useful information for individualized rehabilitative strategies.
Collapse
Affiliation(s)
- Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wen Geng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song'an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Mengye Shi
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Leilei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Liang Jiang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| |
Collapse
|
112
|
Pircher T, Pircher B, Schlücker E, Feigenspan A. The structure dilemma in biological and artificial neural networks. Sci Rep 2021; 11:5621. [PMID: 33692408 PMCID: PMC7970964 DOI: 10.1038/s41598-021-84813-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 02/19/2021] [Indexed: 01/22/2023] Open
Abstract
Brain research up to date has revealed that structure and function are highly related. Thus, for example, studies have repeatedly shown that the brains of patients suffering from schizophrenia or other diseases have a different connectome compared to healthy people. Apart from stochastic processes, however, an inherent logic describing how neurons connect to each other has not yet been identified. We revisited this structural dilemma by comparing and analyzing artificial and biological-based neural networks. Namely, we used feed-forward and recurrent artificial neural networks as well as networks based on the structure of the micro-connectome of C. elegans and of the human macro-connectome. We trained these diverse networks, which markedly differ in their architecture, initialization and pruning technique, and we found remarkable parallels between biological-based and artificial neural networks, as we were additionally able to show that the dilemma is also present in artificial neural networks. Our findings show that structure contains all the information, but that this structure is not exclusive. Indeed, the same structure was able to solve completely different problems with only minimal adjustments. We particularly put interest on the influence of weights and the neuron offset value, as they show a different adaption behaviour. Our findings open up new questions in the fields of artificial and biological information processing research.
Collapse
Affiliation(s)
- Thomas Pircher
- Institute of Process Machinery and Systems Engineering, Friedrich-Alexander University Erlangen-Nuremberg, Cauerstraße 4, 91058, Erlangen, Germany.
| | - Bianca Pircher
- Department Biology, Animal Physiology, Friedrich-Alexander University Erlangen-Nuremberg, Staudtstraße 5, 91058, Erlangen, Germany
| | - Eberhard Schlücker
- Institute of Process Machinery and Systems Engineering, Friedrich-Alexander University Erlangen-Nuremberg, Cauerstraße 4, 91058, Erlangen, Germany
| | - Andreas Feigenspan
- Department Biology, Animal Physiology, Friedrich-Alexander University Erlangen-Nuremberg, Staudtstraße 5, 91058, Erlangen, Germany
| |
Collapse
|
113
|
Crane NA, Chang F, Kinney KL, Klumpp H. Individual differences in striatal and amygdala response to emotional faces are related to symptom severity in social anxiety disorder. NEUROIMAGE-CLINICAL 2021; 30:102615. [PMID: 33735785 PMCID: PMC7985697 DOI: 10.1016/j.nicl.2021.102615] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 08/21/2020] [Accepted: 02/25/2021] [Indexed: 11/05/2022]
Abstract
Amygdala & striatal neural activity may underlie Social Anxiety Disorder (SAD). 80 individuals with SAD completed an emotion processing task during fMRI. Dorsal striatal & amygdala response to angry > happy related to illness severity. Activity in these regions may contribute to individual differences in SAD.
Social anxiety disorder (SAD) is a common heterogeneous disorder characterized by excessive fear and deficient positive experiences. Case-control emotion processing studies indicate that altered amygdala and striatum function may underlie SAD; however, links between these regions and symptomatology have yet to be established. Therefore, in the current study, 80 individuals diagnosed with SAD completed a validated emotion processing task during functional magnetic resonance imaging. Anatomy-based regions of interest were amygdala, caudate, putamen, and nucleus accumbens. Neural activity in response to angry > happy faces and fearful > happy faces in these regions were submitted to multiple linear regression analysis with bootstrapping. Additionally, multiple linear regression analysis was performed to explore clinical features of SAD. Results showed greater putamen activity and less amygdala activity in response to angry > happy faces were related to greater social anxiety severity. In the model consisting of caudate and amygdala activity in response to angry > happy faces, results were marginally related to social anxiety severity and the pattern of activity was similar to the regression model comprising putamen and amygdala. Nucleus accumbens activity was not related to social anxiety severity. There was no correspondence between brain activity in response to fearful > happy faces and social anxiety severity. Clinical variables revealed greater levels of anhedonia and general anxiety were related to social anxiety severity, however, neural activity was not related to these features of SAD. Neuroimaging findings suggest that variance in dorsal striatal and amygdala activity in response to certain social signals of threat contrasted with an approach/rewarding social signal may contribute to individual differences in SAD. Clinical findings indicate variance in anhedonia and general anxiety symptoms may contribute to individual differences in social anxiety severity.
Collapse
Affiliation(s)
- Natania A Crane
- Department of Psychiatry (NAC, FC, KLK, HK), University of Illinois at Chicago, 1601 W. Taylor St (M/C 912), Chicago, IL 60612, United States.
| | - Fini Chang
- Department of Psychiatry (NAC, FC, KLK, HK), University of Illinois at Chicago, 1601 W. Taylor St (M/C 912), Chicago, IL 60612, United States; Department of Psychology (FC, KLK, HK), University of Illinois at Chicago, 1007 W. Harrison St (M/C 285), Chicago, IL 60607, United States
| | - Kerry L Kinney
- Department of Psychiatry (NAC, FC, KLK, HK), University of Illinois at Chicago, 1601 W. Taylor St (M/C 912), Chicago, IL 60612, United States; Department of Psychology (FC, KLK, HK), University of Illinois at Chicago, 1007 W. Harrison St (M/C 285), Chicago, IL 60607, United States
| | - Heide Klumpp
- Department of Psychiatry (NAC, FC, KLK, HK), University of Illinois at Chicago, 1601 W. Taylor St (M/C 912), Chicago, IL 60612, United States; Department of Psychology (FC, KLK, HK), University of Illinois at Chicago, 1007 W. Harrison St (M/C 285), Chicago, IL 60607, United States
| |
Collapse
|
114
|
Structural brain network topology underpinning ADHD and response to methylphenidate treatment. Transl Psychiatry 2021; 11:150. [PMID: 33654073 PMCID: PMC7925571 DOI: 10.1038/s41398-021-01278-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 01/21/2021] [Accepted: 02/12/2021] [Indexed: 02/03/2023] Open
Abstract
Behavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.
Collapse
|
115
|
Saha S, Mamun KA, Ahmed K, Mostafa R, Naik GR, Darvishi S, Khandoker AH, Baumert M. Progress in Brain Computer Interface: Challenges and Opportunities. Front Syst Neurosci 2021; 15:578875. [PMID: 33716680 PMCID: PMC7947348 DOI: 10.3389/fnsys.2021.578875] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
Collapse
Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Khondaker A. Mamun
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Khawza Ahmed
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Raqibul Mostafa
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Sam Darvishi
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Ahsan H. Khandoker
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| |
Collapse
|
116
|
Tao L, Wang G, Zhu M, Cai Q. Bilingualism and domain-general cognitive functions from a neural perspective: A systematic review. Neurosci Biobehav Rev 2021; 125:264-295. [PMID: 33631315 DOI: 10.1016/j.neubiorev.2021.02.029] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 02/11/2021] [Accepted: 02/18/2021] [Indexed: 12/23/2022]
Abstract
A large body of research has indicated that bilingualism - through continual practice in language control - may impact cognitive functions, as well as relevant aspects of brain function and structure. The present review aimed to bring together findings on the relationship between bilingualism and domain-general cognitive functions from a neural perspective. The final sample included 210 studies, covering findings regarding neural responses to bilingual language control and/or domain-general cognitive tasks, as well as findings regarding effects of bilingualism on non-task-related brain function and brain structure. The evidence indicates that a) bilingual language control likely entails neural mechanisms responsible for domain-general cognitive functions; b) bilingual experiences impact neural responses to domain-general cognitive functions; and c) bilingual experiences impact non-task-related brain function (both resting-state and metabolic function) as well as aspects of brain structure (both macrostructure and microstructure), each of which may in turn impact mental processes, including domain-general cognitive functions. Such functional and structural neuroplasticity associated with bilingualism may contribute to both cognitive and neural reserves, producing benefits across the lifespan.
Collapse
Affiliation(s)
- Lily Tao
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China
| | - Gongting Wang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China
| | - Miaomiao Zhu
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China
| | - Qing Cai
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, China; Institute of Brain and Education Innovation, East China Normal University, China; NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, China.
| |
Collapse
|
117
|
Ashourvan A, Shah P, Pines A, Gu S, Lynn CW, Bassett DS, Davis KA, Litt B. Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states. Commun Biol 2021; 4:210. [PMID: 33594239 PMCID: PMC7887247 DOI: 10.1038/s42003-021-01700-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/06/2021] [Indexed: 01/30/2023] Open
Abstract
A major challenge in neuroscience is determining a quantitative relationship between the brain's white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes' activation patterns' probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM's interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions' distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain's structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.
Collapse
Affiliation(s)
- Arian Ashourvan
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Preya Shah
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Adam Pines
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Shi Gu
- grid.54549.390000 0004 0369 4060Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Christopher W. Lynn
- grid.25879.310000 0004 1936 8972Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA ,grid.411115.10000 0004 0435 0884Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| | - Kathryn A. Davis
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Brian Litt
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA ,grid.411115.10000 0004 0435 0884Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| |
Collapse
|
118
|
Probing the structure-function relationship with neural networks constructed by solving a system of linear equations. Sci Rep 2021; 11:3808. [PMID: 33589672 PMCID: PMC7884791 DOI: 10.1038/s41598-021-82964-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/27/2021] [Indexed: 11/17/2022] Open
Abstract
Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.
Collapse
|
119
|
High-resolution connectomic fingerprints: Mapping neural identity and behavior. Neuroimage 2021; 229:117695. [PMID: 33422711 DOI: 10.1016/j.neuroimage.2020.117695] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/16/2020] [Accepted: 12/23/2020] [Indexed: 01/30/2023] Open
Abstract
Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.
Collapse
|
120
|
Naze S, Proix T, Atasoy S, Kozloski JR. Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes. Neuroimage 2021; 224:117364. [PMID: 32947015 PMCID: PMC7779370 DOI: 10.1016/j.neuroimage.2020.117364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/14/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022] Open
Abstract
Recently, it has been proposed that the harmonic patterns emerging from the brain's structural connectivity underlie the resting state networks of the human brain. These harmonic patterns, termed connectome harmonics, are estimated as the Laplace eigenfunctions of the combined gray and white matters connectivity matrices and yield a connectome-specific extension of the well-known Fourier basis. However, it remains unclear how topological properties of the combined connectomes constrain the precise shape of the connectome harmonics and their relationships to the resting state networks. Here, we systematically study how alterations of the local and long-range connectivity matrices affect the spatial patterns of connectome harmonics. Specifically, the proportion of local gray matter homogeneous connectivity versus long-range white-matter heterogeneous connectivity is varied by means of weight-based matrix thresholding, distance-based matrix trimming, and several types of matrix randomizations. We demonstrate that the proportion of local gray matter connections plays a crucial role for the emergence of wide-spread, functionally meaningful, and originally published connectome harmonic patterns. This finding is robust for several different cortical surface templates, mesh resolutions, or widths of the local diffusion kernel. Finally, using the connectome harmonic framework, we also provide a proof-of-concept for how targeted structural changes such as the atrophy of inter-hemispheric callosal fibers and gray matter alterations may predict functional deficits associated with neurodegenerative conditions.
Collapse
Affiliation(s)
- Sébastien Naze
- IBM T.J. Watson Research Center, Yorktown Heights, New York, USA; IBM Research Australia, Melbourne, Victoria, Australia.
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, UK
| | - James R Kozloski
- IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
| |
Collapse
|
121
|
Abdelnour F, Dayan M, Devinsky O, Thesen T, Raj A. Algebraic relationship between the structural network's Laplacian and functional network's adjacency matrix is preserved in temporal lobe epilepsy subjects. Neuroimage 2020; 228:117705. [PMID: 33385550 DOI: 10.1016/j.neuroimage.2020.117705] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 11/19/2022] Open
Abstract
The relationship between anatomic and resting state functional connectivity of large-scale brain networks is a major focus of current research. In previous work, we introduced a model based on eigen decomposition of the Laplacian which predicts the functional network from the structural network in healthy brains. In this work, we apply the eigen decomposition model to two types of epilepsy; temporal lobe epilepsy associated with mesial temporal sclerosis, and MRI-normal temporal lobe epilepsy. Our findings show that the eigen relationship between function and structure holds for patients with temporal lobe epilepsy as well as normal individuals. These results suggest that the brain under TLE conditions reconfigures and rewires the fine-scale connectivity (a process which the model parameters are putatively sensitive to), in order to achieve the necessary structure-function relationship.
Collapse
Affiliation(s)
- Farras Abdelnour
- Radiology and Biomedical Imaging Graduate Program in BioEngineering UCSF, San Francisco, CA, USA.
| | - Michael Dayan
- Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland
| | | | - Thomas Thesen
- Department of Physiology, Neuroscience & Behavioral Sciences, St. George's University, Grenada, West Indies
| | - Ashish Raj
- Radiology and Biomedical Imaging Graduate Program in BioEngineering UCSF, San Francisco, CA, USA
| |
Collapse
|
122
|
Signorelli CM, Uhrig L, Kringelbach M, Jarraya B, Deco G. Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss. Neuroimage 2020; 227:117618. [PMID: 33307225 DOI: 10.1016/j.neuroimage.2020.117618] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/14/2020] [Accepted: 12/01/2020] [Indexed: 11/30/2022] Open
Abstract
Anesthesia induces a reconfiguration of the repertoire of functional brain states leading to a high function-structure similarity. However, it is unclear how these functional changes lead to loss of consciousness. Here we suggest that the mechanism of conscious access is related to a general dynamical rearrangement of the intrinsic hierarchical organization of the cortex. To measure cortical hierarchy, we applied the Intrinsic Ignition analysis to resting-state fMRI data acquired in awake and anesthetized macaques. Our results reveal the existence of spatial and temporal hierarchical differences of neural activity within the macaque cortex, with a strong modulation by the depth of anesthesia and the employed anesthetic agent. Higher values of Intrinsic Ignition correspond to rich and flexible brain dynamics whereas lower values correspond to poor and rigid, structurally driven brain dynamics. Moreover, spatial and temporal hierarchical dimensions are disrupted in a different manner, involving different hierarchical brain networks. All together suggest that disruption of brain hierarchy is a new signature of consciousness loss.
Collapse
Affiliation(s)
- Camilo Miguel Signorelli
- Department of Computer Science, University of Oxford, UK; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, France; Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Spain.
| | - Lynn Uhrig
- Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, France; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Direction de la Recherche Fondamentale, NeuroSpin Center, France; Department of Anesthesiology and Critical Care, Necker Hospital, University Paris Descartes, France; Department of Anesthesiology and Critical Care, Sainte-Anne Hospital, University Paris Descartes, France
| | - Morten Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, UK; Department of Psychiatry, University of Oxford, UK
| | - Bechir Jarraya
- Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, France; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Direction de la Recherche Fondamentale, NeuroSpin Center, France; Neurosurgery Department, Foch Hospital, Suresnes, France; University of Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, France.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Spain; Institució Catalana de la Recerca i Estudis Avançats, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
| |
Collapse
|
123
|
Zhang T, Li X, Jiang X, Ge F, Zhang S, Zhao L, Liu H, Huang Y, Wang X, Yang J, Guo L, Hu X, Liu T. Cortical 3-hinges could serve as hubs in cortico-cortical connective network. Brain Imaging Behav 2020; 14:2512-2529. [PMID: 31950404 PMCID: PMC7647986 DOI: 10.1007/s11682-019-00204-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Mapping the relation between cortical convolution and structural/functional brain architectures could provide deep insights into the mechanisms of brain development, evolution and diseases. In our previous studies, we found a unique gyral folding pattern, termed a 3-hinge, which was defined as the conjunction of three gyral crests. The uniqueness of the 3-hinge was evidenced by its thicker cortex and stronger fiber connections than other gyral regions. However, the role that 3-hinges play in cortico-cortical connective architecture remains unclear. To this end, we conducted MRI studies by constructing structural cortico-cortical connective networks based on a fine-granular cortical parcellation, the parcels of which were automatically labeled as 3-hinge, 2-hinge (ordinary gyrus) or sulcus. On human brains, 3-hinges possess significantly higher degrees, strengths and betweennesses than 2-hinges, suggesting that 3-hinges could serve more like hubs in the cortico-cortical connective network. This hypothesis gains supports from human functional network analyses, in which 3-hinges are involved in more global functional networks than ordinary gyri. In addition, 3-hinges could serve as 'connector' hubs rather than 'provincial' hubs and they account for a dominant proportion of nodes in the high-level 'backbone' of the network. These structural results are reproduced on chimpanzee and macaque brains, while the roles of 3-hinges as hubs become more pronounced in higher order primates. Our new findings could provide a new window to the relation between cortical convolution, anatomical connection and brain function.
Collapse
Affiliation(s)
- Tuo Zhang
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China.
| | - Xiao Li
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fangfei Ge
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Shu Zhang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Lin Zhao
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Huan Liu
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Ying Huang
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Jian Yang
- Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| |
Collapse
|
124
|
Liégeois R, Santos A, Matta V, Van De Ville D, Sayed AH. Revisiting correlation-based functional connectivity and its relationship with structural connectivity. Netw Neurosci 2020; 4:1235-1251. [PMID: 33409438 PMCID: PMC7781609 DOI: 10.1162/netn_a_00166] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 08/16/2020] [Indexed: 12/25/2022] Open
Abstract
Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations or their unnormalized version encoded in the precision matrix. The latter FC metrics yield more meaningful comparisons to SC because they capture ‘direct’ statistical dependencies, that is, discarding the effects of mediators, but their use has been limited because of estimation issues. With the rise of high-quality and large neuroimaging datasets, we revisit the relevance of different FC metrics in the context of SC-FC comparisons. Using data from 100 unrelated Human Connectome Project subjects, we first explore the amount of functional data required to reliably estimate various FC metrics. We find that precision-based FC yields a better match to SC than correlation-based FC when using 5 minutes of functional data or more. Finally, using a linear model linking SC and FC, we show that the SC-FC match can be used to further interrogate various aspects of brain structure and function such as the timescales of functional dynamics in different resting-state networks or the intensity of anatomical self-connections.
Collapse
Affiliation(s)
- Raphael Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Centre for Informatics and Systems, University of Coimbra, Portugal
| | - Augusto Santos
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Centre for Informatics and Systems, University of Coimbra, Portugal
| | - Vincenzo Matta
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Italy
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Centre for Informatics and Systems, University of Coimbra, Portugal
| | - Ali H Sayed
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Centre for Informatics and Systems, University of Coimbra, Portugal
| |
Collapse
|
125
|
Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A. Structure-function coupling in the human connectome: A machine learning approach. Neuroimage 2020; 226:117609. [PMID: 33271268 DOI: 10.1016/j.neuroimage.2020.117609] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 02/03/2023] Open
Abstract
While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an individual's structural connectome explained significant inter-individual variation in cognitive performance. Our results suggest that structure-function coupling in human brain networks is substantially tighter than previously suggested. We establish the margin by which current brain network models can be improved and demonstrate how deep learning can facilitate investigation of relations between brain function and behavior.
Collapse
Affiliation(s)
- T Sarwar
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Y Tian
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - B T T Yeo
- Department of Electrical and Computer Engineering, Center for Sleep & Cognition & N.1 Institute for Health, National University of Singapore, 15 119077, Singapore
| | - K Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia; Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia.
| |
Collapse
|
126
|
Zhao S, Wang G, Yan T, Xiang J, Yu X, Li H, Wang B. Sex Differences in Anatomical Rich-Club and Structural-Functional Coupling in the Human Brain Network. Cereb Cortex 2020; 31:1987-1997. [PMID: 33230551 DOI: 10.1093/cercor/bhaa335] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/28/2020] [Accepted: 10/14/2020] [Indexed: 01/01/2023] Open
Abstract
Structural and functional differences between the brains of female and male adults have been well documented. However, potential sex differences in the patterns of rich-club organization and the coupling between their structural connectivity (SC) and functional connectivity (FC) remain to be determined. In this study, functional magnetic resonance imaging and diffusion tensor imaging techniques were combined to examine sex differences in rich-club organization. Females had a stronger SC-FC coupling than males. Moreover, stronger SC-FC coupling in the females was primarily located in feeder connections and non-rich-club nodes of the left inferior frontal gyrus and inferior parietal lobe and the right superior frontal gyrus and superior parietal gyrus, whereas higher coupling strength in males was primarily located in rich-club connections and rich-club node of the right insula, and non-rich-club nodes of the left hippocampus and the right parahippocampal gyrus. Sex-specific patterns in correlations were also shown between SC-FC coupling and cognitive function, including working memory and reasoning ability. The topological changes in rich-club organization provide novel insight into sex-specific effects on white matter connections that underlie a potential network mechanism of sex-based differences in cognitive function.
Collapse
Affiliation(s)
- Shuo Zhao
- School of Psychology, Shenzhen University, Shenzhen 518061, China.,Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Gongshu Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Xuexue Yu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Hong Li
- School of Psychology, Shenzhen University, Shenzhen 518061, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| |
Collapse
|
127
|
Li X, Liu T, Li Y, Li Q, Wang X, Hu X, Guo L, Zhang T, Liu T. Marmoset Brain ISH Data Revealed Molecular Difference Between Cortical Folding Patterns. Cereb Cortex 2020; 31:1660-1674. [PMID: 33152757 DOI: 10.1093/cercor/bhaa317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 08/23/2020] [Accepted: 09/29/2020] [Indexed: 01/14/2023] Open
Abstract
Literature studies have demonstrated the structural, connectional, and functional differences between cortical folding patterns in mammalian brains, such as convex and concave patterns. However, the molecular underpinning of such convex/concave differences remains largely unknown. Thanks to public access to a recently released set of marmoset whole-brain in situ hybridization data by RIKEN, Japan; this data's accessibility empowers us to improve our understanding of the organization, regulation, and function of genes and their relation to macroscale metrics of brains. In this work, magnetic resonance imaging and diffusion tensor imaging macroscale neuroimaging data in this dataset were used to delineate convex/concave patterns in marmoset and to examine their structural features. Machine learning and visualization tools were employed to investigate the possible transcriptome difference between cortical convex and concave patterns. Experimental results demonstrated that a collection of genes is differentially expressed in convex and concave patterns, and their expression profiles can robustly characterize and differentiate the two folding patterns. More importantly, neuroscientific interpretations of these differentially expressed genes, as well as axonal guidance pathway analysis and gene enrichment analysis, offer novel understanding of structural and functional differences between cortical folding patterns in different regions from a molecular perspective.
Collapse
Affiliation(s)
- Xiao Li
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tao Liu
- Center for Genomics and Computational Biology, College of Science, North China University of Science and Technology, 063210, China.,Center of Computational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yujie Li
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Qing Li
- The Information Processing Laboratory, School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
| | - Xianqiao Wang
- Computational Nano/Bio-Mechanics Lab, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Xintao Hu
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lei Guo
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tuo Zhang
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| |
Collapse
|
128
|
Ogawa A, Osada T, Tanaka M, Kamagata K, Aoki S, Konishi S. Connectivity-based localization of human hypothalamic nuclei in functional images of standard voxel size. Neuroimage 2020; 221:117205. [PMID: 32735999 DOI: 10.1016/j.neuroimage.2020.117205] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 07/17/2020] [Accepted: 07/23/2020] [Indexed: 12/19/2022] Open
Abstract
Despite their critical roles in autonomic functions, individual hypothalamic nuclei have not been extensively investigated in humans using functional magnetic resonance imaging, partly due to the difficulty in resolving individual nuclei contained in the small structure of the hypothalamus. Areal parcellation analyses enable discrimination of individual hypothalamic nuclei but require a higher spatial resolution, which necessitates long scanning time or large amounts of data to compensate for the low signal-to-noise ratio in 3T or 1.5T scanners. In this study, we present analytic procedures to estimate likely locations of individual nuclei in the standard 2-mm resolution based on our higher resolution dataset. The spatial profiles of functional connectivity with the cerebral cortex for each nucleus in the medial hypothalamus were calculated using our higher resolution dataset. Voxels in the hypothalamus in standard resolution images from the Human Connectome Project (HCP) database that predominantly shared connectivity profiles with the same nucleus were subsequently identified. Voxels representing individual nuclei, as identified with the analytic procedures, were reproducible across 20 HCP datasets of 20 subjects each. Furthermore, the identified voxels were spatially separate. These results suggest that these analytic procedures are capable of refining voxels that represent individual hypothalamic nuclei in standard resolution. Our results highlight the potential utility of these procedures in various settings such as patient studies, where lengthy scans are infeasible.
Collapse
Affiliation(s)
- Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan; Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo, Japan; Sportology Center, Juntendo University School of Medicine, Tokyo, Japan; Advanced Research Institute for Health Science, Juntendo University School of Medicine, Tokyo, Japan.
| |
Collapse
|
129
|
Yang F, Zhang J, Fan L, Liao M, Wang Y, Chen C, Zhai T, Zhang Y, Li L, Su L, Dai Z. White matter structural network disturbances in first-episode, drug-naïve adolescents with generalized anxiety disorder. J Psychiatr Res 2020; 130:394-404. [PMID: 32889357 DOI: 10.1016/j.jpsychires.2020.08.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/12/2020] [Accepted: 08/09/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Previous studies have suggested that individuals with generalized anxiety disorder (GAD) would show inefficient whole-brain communication and dysconnectivity in the fronto-parietal-subcortical sub-networks in the white matter (WM) structural network. However, these hypotheses have yet to be tested. METHODS Individual WM structural networks were constructed based on diffusion MRI data and deterministic tractography in 34 first-episode, medication-naïve adolescents with GAD and 27 healthy controls (HCs). Graph theory was applied to investigate the topological organization alterations of the structural network. RESULTS GAD patients showed disrupted small-world configurations (i.e., increased path length and decreased clustering coefficient) and hub organization (i.e., less connection strength in the feeder and local connections). A decreased connection strength was found in a GAD-related sub-network (mainly involving the frontal-subcortical circuits), which was able to distinguish GAD patients from HCs with higher accuracy (area under the curve of 0.96, sensitivity of 94%, specificity of 89%) than clinical scores and other topological alternations. LIMITATIONS The current study just compared GAD patients with HCs based on a small sample, leaving whether the alternations found here are specific to GAD still an open question. Future studies are recommended to recruit patients with other anxiety disorders (e.g., social anxiety disorder) and/or comorbid mood disorders to identify the GAD-specific WM alterations using a larger sample. CONCLUSIONS Our findings highlight the disruption of the topological organization of the whole-brain WM structural network (especially the frontal-subcortical circuits) in GAD, and suggest the potential of using structural connectivity of the GAD-related sub-network as a biomarker for GAD patients.
Collapse
Affiliation(s)
- Fan Yang
- Guangdong Mental Health Center, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jinbo Zhang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Linlin Fan
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Mei Liao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuyin Wang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Chang Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Tianyi Zhai
- Department of Psychiatry, Guangzhou Huiai Hospital, Guangzhou, China
| | - Yan Zhang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Linyan Su
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
130
|
Yoo HB, Mohan A, De Ridder D, Vanneste S. Paradoxical relationship between distress and functional network topology in phantom sound perception. PROGRESS IN BRAIN RESEARCH 2020; 260:367-395. [PMID: 33637228 DOI: 10.1016/bs.pbr.2020.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Distress is a domain-general symptom that accompanies several disorders, including tinnitus. Based on previous studies, we know that distress is encoded by changes in functional connectivity between cortical and subcortical regions. However, how distress relates to large-scale brain networks is not yet clear. In the current study, we investigate the relationship between distress and the efficiency of a network by examining its topological properties using resting state fMRI collected from 90 chronic tinnitus patients. The present results indicate that distress negatively correlates with path length and positively correlates with clustering coefficient, small-worldness, and efficiency of information transfer. Specifically, path analysis showed that the relationship between distress and efficiency is significantly mediated by the resilience of the feeder connections and the centrality of the rich-club connections. In other words, the higher the network efficiency, the lower the resilience of the feeder connections and the centrality of the rich-club connections, which in turn reflects in higher distress in tinnitus patients. This indicates a reorganization of the network towards a paradoxically more efficient topology in patients with high distress, potentially explaining their increased rumination on the tinnitus percept itself.
Collapse
Affiliation(s)
- Hye Bin Yoo
- Department of Neurological Surgery, University of Texas Southwestern, United States
| | - Anusha Mohan
- Lab for Clinical and Integrative Neuroscience, Global Brain Health Institute, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
| | - Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Sven Vanneste
- Lab for Clinical and Integrative Neuroscience, Global Brain Health Institute, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland; Lab for Clinical and Integrative Neuroscience, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, United States.
| |
Collapse
|
131
|
Beyond traditional approaches: a partial directed coherence with graph theory-based mental load assessment using EEG modality. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05408-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
|
132
|
Sanchez-Rodriguez LM, Iturria-Medina Y, Mouches P, Sotero RC. Detecting brain network communities: Considering the role of information flow and its different temporal scales. Neuroimage 2020; 225:117431. [PMID: 33045336 DOI: 10.1016/j.neuroimage.2020.117431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 09/22/2020] [Accepted: 09/29/2020] [Indexed: 12/16/2022] Open
Abstract
The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a parameter-dependent modularity function that often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. Next, it was applied to real and simulated anatomical and/or functional connectomes in the macaque and human brains. We found a clear hierarchical repertoire of community structures in both the anatomical and the functional networks. The observed partitions range from the evident division in two hemispheres -in which all processes are managed globally- to specialized communities seemingly shaped by physical proximity and shared function. Additionally, the spatial scales of a network's community structure (characterized by a measure we term within-communities path length) appear inversely proportional to the oscillatory modes' average frequencies. The proportionality constant may constitute a network-specific propagation velocity for the information flow. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow in the brain network.
Collapse
Affiliation(s)
- Lazaro M Sanchez-Rodriguez
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill Univ., Montreal, Canada; McConnel Brain Imaging Center, Montreal Neurological Institute, McGill Univ., Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill Univ., Montreal, Canada.
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill Univ., Montreal, Canada; McConnel Brain Imaging Center, Montreal Neurological Institute, McGill Univ., Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill Univ., Montreal, Canada
| | - Pauline Mouches
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, Canada
| | - Roberto C Sotero
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, Canada.
| |
Collapse
|
133
|
Feng C, Zhu Z, Cui Z, Ushakov V, Dreher JC, Luo W, Gu R, Wu X, Krueger F. Prediction of trust propensity from intrinsic brain morphology and functional connectome. Hum Brain Mapp 2020; 42:175-191. [PMID: 33001541 PMCID: PMC7721234 DOI: 10.1002/hbm.25215] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/31/2020] [Accepted: 09/09/2020] [Indexed: 01/08/2023] Open
Abstract
Trust forms the basis of virtually all interpersonal relationships. Although significant individual differences characterize trust, the driving neuropsychological signatures behind its heterogeneity remain obscure. Here, we applied a prediction framework in two independent samples of healthy participants to examine the relationship between trust propensity and multimodal brain measures. Our multivariate prediction analyses revealed that trust propensity was predicted by gray matter volume and node strength across multiple regions. The gray matter volume of identified regions further enabled the classification of individuals from an independent sample with the propensity to trust or distrust. Our modular and functional decoding analyses showed that the contributing regions were part of three large‐scale networks implicated in calculus‐based trust strategy, cost–benefit calculation, and trustworthiness inference. These findings do not only deepen our neuropsychological understanding of individual differences in trust propensity, but also provide potential biomarkers in predicting trust impairment in neuropsychiatric disorders.
Collapse
Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Zhiyuan Zhu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of Education, Beijing Normal University, Beijing, China
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vadim Ushakov
- National Research Center, Kurchatov Institute, Moscow, Russia.,National Research Nuclear University MEPhI, Moscow Engineering Physics Institute, Moscow, Russia
| | - Jean-Claude Dreher
- Neuroeconomics, Reward and Decision Making Laboratory, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Bron, France
| | - Wenbo Luo
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xia Wu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of Education, Beijing Normal University, Beijing, China
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, Virginia, USA.,Department of Psychology, George Mason University, Fairfax, Virginia, USA
| |
Collapse
|
134
|
Ludl AA, Soriano J. Impact of Physical Obstacles on the Structural and Effective Connectivity of in silico Neuronal Circuits. Front Comput Neurosci 2020; 14:77. [PMID: 32982710 PMCID: PMC7488194 DOI: 10.3389/fncom.2020.00077] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/21/2020] [Indexed: 11/13/2022] Open
Abstract
Scaffolds and patterned substrates are among the most successful strategies to dictate the connectivity between neurons in culture. Here, we used numerical simulations to investigate the capacity of physical obstacles placed on a flat substrate to shape structural connectivity, and in turn collective dynamics and effective connectivity, in biologically-realistic neuronal networks. We considered μ-sized obstacles placed in mm-sized networks. Three main obstacle shapes were explored, namely crosses, circles and triangles of isosceles profile. They occupied either a small area fraction of the substrate or populated it entirely in a periodic manner. From the point of view of structure, all obstacles promoted short length-scale connections, shifted the in- and out-degree distributions toward lower values, and increased the modularity of the networks. The capacity of obstacles to shape distinct structural traits depended on their density and the ratio between axonal length and substrate diameter. For high densities, different features were triggered depending on obstacle shape, with crosses trapping axons in their vicinity and triangles funneling axons along the reverse direction of their tip. From the point of view of dynamics, obstacles reduced the capacity of networks to spontaneously activate, with triangles in turn strongly dictating the direction of activity propagation. Effective connectivity networks, inferred using transfer entropy, exhibited distinct modular traits, indicating that the presence of obstacles facilitated the formation of local effective microcircuits. Our study illustrates the potential of physical constraints to shape structural blueprints and remodel collective activity, and may guide investigations aimed at mimicking organizational traits of biological neuronal circuits.
Collapse
Affiliation(s)
- Adriaan-Alexander Ludl
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.,Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain.,Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain.,Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona, Spain
| |
Collapse
|
135
|
Yuan R, Biswal BB, Zaborszky L. Functional Subdivisions of Magnocellular Cell Groups in Human Basal Forebrain: Test-Retest Resting-State Study at Ultra-high Field, and Meta-analysis. Cereb Cortex 2020; 29:2844-2858. [PMID: 30137295 DOI: 10.1093/cercor/bhy150] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 05/11/2018] [Indexed: 12/23/2022] Open
Abstract
The heterogeneous neuronal subgroups of the basal forebrain corticopetal system (BFcs) have been shown to modulate cortical functions through their cholinergic, gamma-aminobutyric acid-ergic, and glutamatergic projections to the entire cortex. Although previous studies suggested that the basalo-cortical projection system influences various cognitive functions, particularly via its cholinergic component, these studies only focused on certain parts of the BFcs or nearby structures, leaving aside a more systematic picture of the functional connectivity of BFcs subcompartments. Moreover, these studies lacked the high-spatial resolution and the probability maps needed to identify specific subcompartments. Recent advances in the ultra-high field 7T functional magnetic resonance imaging (fMRI) provided potentially unprecedented spatial resolution of functional MRI images to study the subdivision of the BFcs. In this study, the BF space containing corticopetal cells was divided into 3 functionally distinct subdivisions based on functional connection to cortical regions derived from fMRI. The overall functional connection of each BFcs subdivision was examined with a test-retest study. Finally, a meta-analysis was used to study the related functional topics of each BF subdivision. Our results demonstrate distinct functional connectivity patterns of these subdivisions along the rostrocaudal axis of the BF. All three compartments have shown consistent segregation and overlap at specific target regions including the hippocampus, insula, thalamus, and the cingulate gyrus, suggesting functional integration and separation in BFcs.
Collapse
Affiliation(s)
- Rui Yuan
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Laszlo Zaborszky
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ, USA
| |
Collapse
|
136
|
Ma J, Liu F, Yang B, Xue K, Wang P, Zhou J, Wang Y, Niu Y, Zhang J. Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment. Neurosci Bull 2020; 37:287-297. [PMID: 32975745 DOI: 10.1007/s12264-020-00580-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 05/30/2020] [Indexed: 01/04/2023] Open
Abstract
Subcortical vascular mild cognitive impairment (svMCI) is a common prodromal stage of vascular dementia. Although mounting evidence has suggested abnormalities in several single brain network metrics, few studies have explored the consistency between functional and structural connectivity networks in svMCI. Here, we constructed such networks using resting-state fMRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls. The functional networks were then parcellated into topological modules, corresponding to several well-defined functional domains. The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level (whole brain and modular level). We found no significant intergroup differences in the functional-structural coupling within the whole brain; however, there was significantly increased functional-structural coupling within the dorsal attention module and decreased functional-structural coupling within the ventral attention module in the svMCI group. In addition, the svMCI patients demonstrated decreased intramodular connectivity strength in the visual, somatomotor, and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network, mainly linking the visual, somatomotor, dorsal attention, ventral attention, and frontoparietal control modules. There was no significant correlation between the altered module-level functional-structural coupling and cognitive performance in patients with svMCI. These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.
Collapse
Affiliation(s)
- Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Bingbing Yang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Pinxiao Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jian Zhou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yali Niu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| |
Collapse
|
137
|
Gabrieli D, Schumm SN, Vigilante NF, Parvesse B, Meaney DF. Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks. PLoS One 2020; 15:e0234749. [PMID: 32966291 PMCID: PMC7510994 DOI: 10.1371/journal.pone.0234749] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/01/2020] [Indexed: 12/26/2022] Open
Abstract
Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.
Collapse
Affiliation(s)
- David Gabrieli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Samantha N. Schumm
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Nicholas F. Vigilante
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Brandon Parvesse
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| |
Collapse
|
138
|
Yin C, Xiao X, Balaban V, Kandel ME, Lee YJ, Popescu G, Bogdan P. Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data. Sci Rep 2020; 10:15078. [PMID: 32934305 PMCID: PMC7492189 DOI: 10.1038/s41598-020-72013-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/19/2020] [Indexed: 11/30/2022] Open
Abstract
Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.
Collapse
Affiliation(s)
- Chenzhong Yin
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA
| | - Xiongye Xiao
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA
| | - Valeriu Balaban
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA
| | - Mikhail E Kandel
- Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA
| | - Young Jae Lee
- Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.,Neuroscience Program, University of Illinois at Urbana Champaign, 208 N Wright St., Urbana, IL, 61801, USA
| | - Gabriel Popescu
- Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA.
| |
Collapse
|
139
|
Yang S, Meng Y, Li J, Li B, Fan YS, Chen H, Liao W. The thalamic functional gradient and its relationship to structural basis and cognitive relevance. Neuroimage 2020; 218:116960. [DOI: 10.1016/j.neuroimage.2020.116960] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/04/2020] [Accepted: 05/14/2020] [Indexed: 12/30/2022] Open
|
140
|
Li Y, Liu J, Tang Z, Lei B. Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2818-2830. [PMID: 32112678 DOI: 10.1109/tmi.2020.2976825] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.
Collapse
|
141
|
Sokolov AA, Zeidman P, Razi A, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Asymmetric high-order anatomical brain connectivity sculpts effective connectivity. Netw Neurosci 2020; 4:871-890. [PMID: 33615094 PMCID: PMC7888488 DOI: 10.1162/netn_a_00150] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.
Collapse
Affiliation(s)
- Arseny A. Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Neurology, University Neurorehabilitation, University Hospital Inselspital, University of Bern, Bern, Switzerland
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Neuroscape Center, Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Monash Institute of Cognitive and Clinical Neurosciences & Monash Biomedical Imaging, Monash University, Clayton, Australia
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tübingen Medical School, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Marina A. Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, Tübingen, Germany
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| |
Collapse
|
142
|
Kitazono J, Kanai R, Oizumi M. Efficient search for informational cores in complex systems: Application to brain networks. Neural Netw 2020; 132:232-244. [PMID: 32919313 DOI: 10.1016/j.neunet.2020.08.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 08/04/2020] [Accepted: 08/23/2020] [Indexed: 11/16/2022]
Abstract
An important step in understanding the nature of the brain is to identify "cores" in the brain network, where brain areas strongly interact with each other. Cores can be considered as essential sub-networks for brain functions. In the last few decades, an information-theoretic approach to identifying cores has been developed. In this approach, interactions between parts are measured by an information loss function, which quantifies how much information would be lost if interactions between parts were removed. Then, a core called a "complex" is defined as a subsystem wherein the amount of information loss is locally maximal. Although identifying complexes can be a novel and useful approach, its application is practically impossible because computation time grows exponentially with system size. Here we propose a fast and exact algorithm for finding complexes, called Hierarchical Partitioning for Complex search (HPC). HPC hierarchically partitions systems to narrow down candidates for complexes. The computation time of HPC is polynomial, enabling us to find complexes in large systems (up to several hundred) in a practical amount of time. We prove that HPC is exact when an information loss function satisfies a mathematical property, monotonicity. We show that mutual information is one such information loss function. We also show that a broad class of submodular functions can be considered as such information loss functions, indicating the expandability of our framework to the class. We applied HPC to electrocorticogram recordings from a monkey and demonstrated that HPC revealed temporally stable and characteristic complexes.
Collapse
Affiliation(s)
- Jun Kitazono
- Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
| | | | - Masafumi Oizumi
- Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
| |
Collapse
|
143
|
Deslauriers-Gauthier S, Zucchelli M, Frigo M, Deriche R. A unified framework for multimodal structure-function mapping based on eigenmodes. Med Image Anal 2020; 66:101799. [PMID: 32889301 DOI: 10.1016/j.media.2020.101799] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/10/2020] [Accepted: 08/06/2020] [Indexed: 11/28/2022]
Abstract
Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure-function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure-function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure-function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit.
Collapse
Affiliation(s)
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, France
| | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, France
| |
Collapse
|
144
|
The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics. Neuroimage 2020; 222:117252. [PMID: 32800991 DOI: 10.1016/j.neuroimage.2020.117252] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/24/2020] [Accepted: 08/04/2020] [Indexed: 12/27/2022] Open
Abstract
Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 294), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.
Collapse
|
145
|
Moretti P, Hütt MT. Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks. Proc Natl Acad Sci U S A 2020; 117:18332-18340. [PMID: 32690716 PMCID: PMC7414146 DOI: 10.1073/pnas.1919785117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In models of excitable dynamics on graphs, excitations can travel in both directions of an undirected link. However, as a striking interplay of dynamics and network topology, excitations often establish a directional preference. Some of these cases of "link-usage asymmetry" are local in nature and can be mechanistically understood, for instance, from the degree gradient of a link (i.e., the difference in node degrees at both ends of the link). Other contributions to the link-usage asymmetry are instead, as we show, self-organized in nature, and strictly nonlocal. This is the case for excitation waves, where the preferential propagation of excitations along a link depends on its orientation with respect to a hub acting as a source, even if the link in question is several steps away from the hub itself. Here, we identify and quantify the contribution of such self-organized patterns to link-usage asymmetry and show that they extend to ranges significantly longer than those ascribed to local patterns. We introduce a topological characterization, the hub-set-orientation prevalence of a link, which indicates its average orientation with respect to the hubs of a graph. Our numerical results show that the hub-set-orientation prevalence of a link strongly correlates with the preferential usage of the link in the direction of propagation away from the hub core of the graph. Our methodology is embedding-agnostic and allows for the measurement of wave signals and the sizes of the cores from which they originate.
Collapse
Affiliation(s)
- Paolo Moretti
- Institute of Materials Simulation, Department of Materials Science, Friedrich-Alexander-University Erlangen-Nürnberg, D-90762 Fürth, Germany;
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, D-28759 Bremen, Germany
| |
Collapse
|
146
|
Dudley J, Yuan W, Diekfuss J, Barber Foss KD, DiCesare CA, Altaye M, Logan K, Leach JL, Myer GD. Altered Functional and Structural Connectomes in Female High School Soccer Athletes After a Season of Head Impact Exposure and the Effect of a Novel Collar. Brain Connect 2020; 10:292-301. [DOI: 10.1089/brain.2019.0729] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Affiliation(s)
- Jonathan Dudley
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Weihong Yuan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Jed Diekfuss
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Kim D. Barber Foss
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Christopher A. DiCesare
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Mekibib Altaye
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Kelsey Logan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - James L. Leach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati Ohio, USA
| | - Gregory D. Myer
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- The SPORT Center, Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- The Micheli Center for Sports Injury Prevention, Waltham, Massachusetts, USA
| |
Collapse
|
147
|
Zhang F, Iwaki S. Correspondence Between Effective Connections in the Stop-Signal Task and Microstructural Correlations. Front Hum Neurosci 2020; 14:279. [PMID: 32848664 PMCID: PMC7396500 DOI: 10.3389/fnhum.2020.00279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 06/19/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Fan Zhang
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
- Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Sunao Iwaki
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
- Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
- *Correspondence: Sunao Iwaki
| |
Collapse
|
148
|
Deslauriers-Gauthier S, Costantini I, Deriche R. Non-invasive inference of information flow using diffusion MRI, functional MRI, and MEG. J Neural Eng 2020; 17:045003. [PMID: 32443001 DOI: 10.1088/1741-2552/ab95ec] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To infer information flow in the white matter of the brain and recover cortical activity using functional MRI, diffusion MRI, and MEG without a manual selection of the white matter connections of interest. APPROACH A Bayesian network which encodes the priors knowledge of possible brain states is built from imaging data. Diffusion MRI is used to enumerate all possible connections between cortical regions. Functional MRI is used to prune connections without manual intervention and increase the likelihood of specific regions being active. MEG data is used as evidence into this network to obtain a posterior distribution on cortical regions and connections. MAIN RESULTS We show that our proposed method is able to identify connections associated with the a sensory-motor task. This allows us to build the Bayesian network with no manual selection of connections of interest. Using sensory-motor MEG evoked response as evidence into this network, our method identified areas known to be involved in a visuomotor task. In addition, information flow along white matter fiber bundles connecting those regions was also recovered. SIGNIFICANCE Current methods to estimate white matter information flow are extremely invasive, therefore limiting our understanding of the interaction between cortical regions. The proposed method makes use of functional MRI, diffusion MRI, and M/EEG to infer communication between cortical regions, therefore opening the door to the non-invasive exploration of information flow in the white matter.
Collapse
|
149
|
Brain Structural Connectivity Predicts Brain Functional Complexity: Diffusion Tensor Imaging Derived Centrality Accounts for Variance in Fractal Properties of Functional Magnetic Resonance Imaging Signal. Neuroscience 2020; 438:1-8. [DOI: 10.1016/j.neuroscience.2020.04.048] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 11/18/2022]
|
150
|
Individual differences in local functional brain connectivity affect TMS effects on behavior. Sci Rep 2020; 10:10422. [PMID: 32591568 PMCID: PMC7320140 DOI: 10.1038/s41598-020-67162-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 05/18/2020] [Indexed: 11/25/2022] Open
Abstract
Behavioral effects of transcranial magnetic stimulation (TMS) often show substantial differences between subjects. One factor that might contribute to these inter-individual differences is the interaction of current brain states with the effects of local brain network perturbation. The aim of the current study was to identify brain regions whose connectivity before and following right parietal perturbation affects individual behavioral effects during a visuospatial target detection task. 20 subjects participated in an fMRI experiment where their brain hemodynamic response was measured during resting state, and then during a visuospatial target detection task following 1 Hz rTMS and sham stimulation. To select a parsimonious set of associated brain regions, an elastic net analysis was used in combination with a whole-brain voxel-wise functional connectivity analysis. TMS-induced changes in accuracy were significantly correlated with the pattern of functional connectivity during the task state following TMS. The functional connectivity of the left superior temporal, angular, and precentral gyri was identified as key explanatory variable for the individual behavioral TMS effects. Our results suggest that the brain must reach an appropriate state in which right parietal TMS can induce improvements in visual target detection. The ability to reach this state appears to vary between individuals.
Collapse
|