1
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Qian P, Manubens-Gil L, Jiang S, Peng H. Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks. Cell Rep 2024; 43:113871. [PMID: 38451816 DOI: 10.1016/j.celrep.2024.113871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.
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Affiliation(s)
- Penghao Qian
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Linus Manubens-Gil
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
| | - Shengdian Jiang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Hanchuan Peng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
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2
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Yang L, Wang Z, Wang G, Liang L, Liu M, Wang J. Brain-inspired modular echo state network for EEG-based emotion recognition. Front Neurosci 2024; 18:1305284. [PMID: 38495107 PMCID: PMC10940514 DOI: 10.3389/fnins.2024.1305284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 01/10/2024] [Indexed: 03/19/2024] Open
Abstract
Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.
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Affiliation(s)
- Liuyi Yang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Zhaoze Wang
- School of Engineering and Applied Science, University of Pennsylvania, Pennsylvania, PA, United States
| | - Guoyu Wang
- Department of Auromation, Tiangong University, Tianjin, China
| | - Lixin Liang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Meng Liu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Junsong Wang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
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3
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Kawai Y, Park J, Asada M. Reservoir computing using self-sustained oscillations in a locally connected neural network. Sci Rep 2023; 13:15532. [PMID: 37726352 PMCID: PMC10509144 DOI: 10.1038/s41598-023-42812-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 09/14/2023] [Indexed: 09/21/2023] Open
Abstract
Understanding how the structural organization of neural networks influences their computational capabilities is of great interest to both machine learning and neuroscience communities. In our previous work, we introduced a novel learning system, called the reservoir of basal dynamics (reBASICS), which features a modular neural architecture (small-sized random neural networks) capable of reducing chaoticity of neural activity and of producing stable self-sustained limit cycle activities. The integration of these limit cycles is achieved by linear summation of their weights, and arbitrary time series are learned by modulating these weights. Despite its excellent learning performance, interpreting a modular structure of isolated small networks as a brain network has posed a significant challenge. Here, we investigate how local connectivity, a well-known characteristic of brain networks, contributes to reducing neural system chaoticity and generates self-sustained limit cycles based on empirical experiments. Moreover, we present the learning performance of the locally connected reBASICS in two tasks: a motor timing task and a learning task of the Lorenz time series. Although its performance was inferior to that of modular reBASICS, locally connected reBASICS could learn a time series of tens of seconds while the time constant of neural units was ten milliseconds. This work indicates that the locality of connectivity in neural networks may contribute to generation of stable self-sustained oscillations to learn arbitrary long-term time series, as well as the economy of wiring cost.
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Affiliation(s)
- Yuji Kawai
- Symbiotic Intelligent Systems Research Center, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka, 565-0871, Japan.
| | - Jihoon Park
- Symbiotic Intelligent Systems Research Center, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka, 565-0871, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan
| | - Minoru Asada
- Symbiotic Intelligent Systems Research Center, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka, 565-0871, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan
- International Professional University of Technology in Osaka, Kita-ku, Osaka, 530-0001, Japan
- Chubu University Academy of Emerging Sciences, Chubu University, Kasugai, Aichi, 487-8501, Japan
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4
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Mansour L S, Seguin C, Smith RE, Zalesky A. Connectome spatial smoothing (CSS): Concepts, methods, and evaluation. Neuroimage 2022; 250:118930. [PMID: 35077853 DOI: 10.1016/j.neuroimage.2022.118930] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 10/19/2022] Open
Abstract
Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds-if not thousands-of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations for optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.
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Affiliation(s)
- Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
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5
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Chen X, Wang Y, Kopetzky SJ, Butz-Ostendorf M, Kaiser M. Connectivity within regions characterizes epilepsy duration and treatment outcome. Hum Brain Mapp 2021; 42:3777-3791. [PMID: 33973688 PMCID: PMC8288103 DOI: 10.1002/hbm.25464] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/13/2021] [Accepted: 04/26/2021] [Indexed: 11/11/2022] Open
Abstract
Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan-Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.
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Affiliation(s)
- Xue Chen
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China.,School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
| | - Sebastian J Kopetzky
- Biomax Informatics AG, Brain Science, Planegg, Germany.,TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | | | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne, UK.,NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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6
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Al Harrach M, Pretzel P, Groeschel S, Rousseau F, Dhollander T, Hertz-Pannier L, Lefevre J, Chabrier S, Dinomais M. A connectome-based approach to assess motor outcome after neonatal arterial ischemic stroke. Ann Clin Transl Neurol 2021; 8:1024-1037. [PMID: 33787079 PMCID: PMC8108427 DOI: 10.1002/acn3.51292] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/08/2020] [Accepted: 12/09/2020] [Indexed: 12/22/2022] Open
Abstract
Objective Studies of motor outcome after Neonatal Arterial Ischemic Stroke (NAIS) often rely on lesion mapping using MRI. However, clinical measurements indicate that motor deficit can be different than what would solely be anticipated by the lesion extent and location. Because this may be explained by the cortical disconnections between motor areas due to necrosis following the stroke, the investigation of the motor network can help in the understanding of visual inspection and outcome discrepancy. In this study, we propose to examine the structural connectivity between motor areas in NAIS patients compared to healthy controls in order to define the cortical and subcortical connections that can reflect the motor outcome. Methods Thirty healthy controls and 32 NAIS patients with and without Cerebral Palsy (CP) underwent MRI acquisition and manual assessment. The connectome of all participants was obtained from T1‐weighted and diffusion‐weighted imaging. Results Significant disconnections in the lesioned and contra‐lesioned hemispheres of patients were found. Furthermore, significant correlations were detected between the structural connectivity metric of specific motor areas and manuality assessed by the Box and Block Test (BBT) scores in patients. Interpretation Using the connectivity measures of these links, the BBT score can be estimated using a multiple linear regression model. In addition, the presence or not of CP can also be predicted using the KNN classification algorithm. According to our results, the structural connectome can be an asset in the estimation of gross manual dexterity and can help uncover structural changes between brain regions related to NAIS.
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Affiliation(s)
- Mariam Al Harrach
- Université d'Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS) EA7315, Angers, 49000, France.,Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image (LTSI), INSERM U1099, Rennes, F-35000, France
| | - Pablo Pretzel
- Experimental Paediatric Neuroimaging, Department of Child Neurology, University Hospital Tübingen, Tübingen, Germany
| | - Samuel Groeschel
- Experimental Paediatric Neuroimaging, Department of Child Neurology, University Hospital Tübingen, Tübingen, Germany
| | | | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Lucie Hertz-Pannier
- UNIACT, Neurospin, Institut Joliot, CEA-Paris Saclay, Inserm U114, Université de Paris, Gif sur Yvette, F-91191, France
| | - Julien Lefevre
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, 13385, France
| | - Stéphane Chabrier
- INSERM, UMR1059 Sainbiose, Univ Saint-Étienne, Univ Lyon, Saint-Étienne, F-42023, France.,Paediatric Physical and Rehabilitation Medicine Department, CHU Saint-Étienne, French Centre for Paediatric Stroke, INSERM, CIC 1408, Saint-Étienne, F-42055, France
| | - Mickael Dinomais
- Université d'Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS) EA7315, Angers, 49000, France.,Département de Médecine Physique et de Réadaptions and LUNAM, CHU Angers, Angers, France
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7
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Kreilkamp BAK, McKavanagh A, Alonazi B, Bryant L, Das K, Wieshmann UC, Marson AG, Taylor PN, Keller SS. Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance. Neuroimage Clin 2021; 29:102564. [PMID: 33508622 PMCID: PMC7841400 DOI: 10.1016/j.nicl.2021.102564] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 12/19/2022]
Abstract
Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.
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Affiliation(s)
- Barbara A K Kreilkamp
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK; Department of Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany.
| | - Andrea McKavanagh
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Batil Alonazi
- Department of Radiology and Medical Imaging, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia
| | - Lorna Bryant
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Kumar Das
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Udo C Wieshmann
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Anthony G Marson
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, UK; UCL Queen Square Institute of Neurology, Queen Square, London, UK
| | - Simon S Keller
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
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8
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Huo S, Tian C, Zheng M, Guan S, Zhou C, Liu Z. Spatial multi-scaled chimera states of cerebral cortex network and its inherent structure-dynamics relationship in human brain. Natl Sci Rev 2021; 8:nwaa125. [PMID: 34691552 PMCID: PMC8288421 DOI: 10.1093/nsr/nwaa125] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 04/01/2020] [Accepted: 05/21/2020] [Indexed: 11/20/2022] Open
Abstract
Human cerebral cortex displays various dynamics patterns under different states, however the mechanism how such diverse patterns can be supported by the underlying brain network is still not well understood. Human brain has a unique network structure with different regions of interesting to perform cognitive tasks. Using coupled neural mass oscillators on human cortical network and paying attention to both global and local regions, we observe a new feature of chimera states with multiple spatial scales and a positive correlation between the synchronization preference of local region and the degree of symmetry of the connectivity of the region in the network. Further, we use the concept of effective symmetry in the network to build structural and dynamical hierarchical trees and find close matching between them. These results help to explain the multiple brain rhythms observed in experiments and suggest a generic principle for complex brain network as a structure substrate to support diverse functional patterns.
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Affiliation(s)
- Siyu Huo
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
| | - Changhai Tian
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
- School of Data Science, Tongren University, Tongren 554300, China
| | - Muhua Zheng
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
| | - Shuguang Guan
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, China
- Department of Physics, Zhejiang University, Hangzhou 310058, China
| | - Zonghua Liu
- State Key Laboratory of Precision Spectroscopy and Department of Physics, East China Normal University, Shanghai 200062, China
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9
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Giannakakis E, Han CE, Weber B, Hutchings F, Kaiser M. Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions. Neurocomputing 2020; 416:38-44. [PMID: 33250573 PMCID: PMC7598092 DOI: 10.1016/j.neucom.2020.01.050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Simulations of neural networks can be used to study the direct effect of internal or external changes on brain dynamics. However, some changes are not immediate but occur on the timescale of weeks, months, or years. Examples include effects of strokes, surgical tissue removal, or traumatic brain injury but also gradual changes during brain development. Simulating network activity over a long time, even for a small number of nodes, is a computational challenge. Here, we model a coupled network of human brain regions with a modified Wilson-Cowan model representing dynamics for each region and with synaptic plasticity adjusting connection weights within and between regions. Using strategies ranging from different models for plasticity, vectorization and a different differential equation solver setup, we achieved one second runtime for one second biological time.
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Affiliation(s)
- Emmanouil Giannakakis
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
| | - Cheol E Han
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Germany
| | - Frances Hutchings
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom.,Institute of Neuroscience, Newcastle University, the Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom.,Department of Functional Neurosurgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
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10
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Ramaraju S, Wang Y, Sinha N, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Taylor PN. Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom. Front Neurol 2020; 11:563847. [PMID: 33071948 PMCID: PMC7543719 DOI: 10.3389/fneur.2020.563847] [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: 05/19/2020] [Accepted: 08/20/2020] [Indexed: 01/21/2023] Open
Abstract
Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.
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Affiliation(s)
- Sriharsha Ramaraju
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fergus Rugg-Gunn
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
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11
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Saggio ML, Crisp D, Scott JM, Karoly P, Kuhlmann L, Nakatani M, Murai T, Dümpelmann M, Schulze-Bonhage A, Ikeda A, Cook M, Gliske SV, Lin J, Bernard C, Jirsa V, Stacey WC. A taxonomy of seizure dynamotypes. eLife 2020; 9:55632. [PMID: 32691734 PMCID: PMC7375810 DOI: 10.7554/elife.55632] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.
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Affiliation(s)
- Maria Luisa Saggio
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Dakota Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Jared M Scott
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Philippa Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Mitsuyoshi Nakatani
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Tomohiko Murai
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Mark Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Jack Lin
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Christophe Bernard
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States.,Department of Neurology, University of Michigan, Ann Arbor, United States
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12
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Kurmukov A, Mussabaeva A, Denisova Y, Moyer D, Jahanshad N, Thompson PM, Gutman BA. Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering. Brain Connect 2020; 10:183-194. [PMID: 32264696 PMCID: PMC7247040 DOI: 10.1089/brain.2019.0722] [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] [Indexed: 11/12/2022] Open
Abstract
This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.
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Affiliation(s)
- Anvar Kurmukov
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Higher School of Economics, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Ayagoz Mussabaeva
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Yulia Denisova
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Daniel Moyer
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Boris A Gutman
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
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13
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Betzel RF. Organizing principles of whole-brain functional connectivity in zebrafish larvae. Netw Neurosci 2020; 4:234-256. [PMID: 32166210 PMCID: PMC7055648 DOI: 10.1162/netn_a_00121] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 12/04/2019] [Indexed: 12/13/2022] Open
Abstract
Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.
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Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
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14
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Badcock PB, Friston KJ, Ramstead MJD, Ploeger A, Hohwy J. The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2019; 19:1319-1351. [PMID: 31115833 PMCID: PMC6861365 DOI: 10.3758/s13415-019-00721-3] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.
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Affiliation(s)
- Paul B Badcock
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Maxwell J D Ramstead
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
- Department of Philosophy, McGill University, Montreal, QC, Canada
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Annemie Ploeger
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Jakob Hohwy
- Cognition & Philosophy Lab, Monash University, Clayton, VIC, Australia
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15
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Badcock PB, Friston KJ, Ramstead MJD. The hierarchically mechanistic mind: A free-energy formulation of the human psyche. Phys Life Rev 2019; 31:104-121. [PMID: 30704846 PMCID: PMC6941235 DOI: 10.1016/j.plrev.2018.10.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Revised: 09/04/2018] [Accepted: 10/10/2018] [Indexed: 11/29/2022]
Abstract
This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.
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Affiliation(s)
- Paul B Badcock
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, 3052, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, 3010, Australia; Orygen, the National Centre of Excellence in Youth Mental Health, Melbourne, 3052, Australia.
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N3BG, UK
| | - Maxwell J D Ramstead
- Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N3BG, UK; Department of Philosophy, McGill University, Montreal, Quebec, H3A 2T7, Canada; Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Quebec, H3A 1A1, Canada
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16
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Reimann MW, Gevaert M, Shi Y, Lu H, Markram H, Muller E. A null model of the mouse whole-neocortex micro-connectome. Nat Commun 2019; 10:3903. [PMID: 31467291 PMCID: PMC6715727 DOI: 10.1038/s41467-019-11630-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 07/25/2019] [Indexed: 11/27/2022] Open
Abstract
In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.
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Affiliation(s)
- Michael W Reimann
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Michael Gevaert
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Ying Shi
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Huanxiang Lu
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Eilif Muller
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
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17
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Pospelov N, Nechaev S, Anokhin K, Valba O, Avetisov V, Gorsky A. Spectral peculiarity and criticality of a human connectome. Phys Life Rev 2019; 31:240-256. [PMID: 31353222 DOI: 10.1016/j.plrev.2019.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Accepted: 07/06/2019] [Indexed: 12/12/2022]
Abstract
We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. Our results indicate new peculiar features of connectomes of higher organisms. We found that the spectral density of adjacency matrices of human connectome has maximal deviation from the one of randomized network, compared to other organisms. Considering the network evolution induced by the preference of 3-cycles formation, we discovered that for macaque and human connectomes the evolution with the conservation of local clusterization is crucial, while for primitive organisms the conservation of averaged clusterization is sufficient. Investigating for the first time the level spacing distribution of the spectrum of human connectome Laplacian matrix, we explicitly demonstrate that the spectral statistics corresponds to the critical regime, which is hybrid of Wigner-Dyson and Poisson distributions. This observation provides strong support for debated statement of the brain criticality.
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Affiliation(s)
- N Pospelov
- Lomonosov Moscow State University, 119991, Moscow, Russia
| | - S Nechaev
- Interdisciplinary Scientific Center Poncelet (CNRS UMI 2615), 119002 Moscow, Russia; P.N. Lebedev Physical Institute RAS, Moscow, Russia.
| | - K Anokhin
- Lomonosov Moscow State University, 119991, Moscow, Russia; National Research Center "Kurchatov Institute", 123098, Moscow, Russia
| | - O Valba
- N.N. Semenov Institute of Chemical Physics RAS, 119991 Moscow, Russia; Department of Applied Mathematics, National Research University Higher School of Economics, 101000 Moscow, Russia
| | - V Avetisov
- N.N. Semenov Institute of Chemical Physics RAS, 119991 Moscow, Russia
| | - A Gorsky
- Institute for Information Transmission Problems RAS, 127051 Moscow, Russia; Moscow Institute of Physics and Technology, Dolgoprudny, 141700 Russia
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18
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Yuan JP, Henje Blom E, Flynn T, Chen Y, Ho TC, Connolly CG, Dumont Walter RA, Yang TT, Xu D, Tymofiyeva O. Test-Retest Reliability of Graph Theoretic Metrics in Adolescent Brains. Brain Connect 2018; 9:144-154. [PMID: 30398373 DOI: 10.1089/brain.2018.0580] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Graph theory analysis of structural brain networks derived from diffusion tensor imaging (DTI) has become a popular analytical method in neuroscience, enabling advanced investigations of neurological and psychiatric disorders. The purpose of this study was to investigate (1) the effects of edge weighting schemes and (2) the effects of varying interscan periods on graph metrics within the adolescent brain. We compared a binary (B) network definition with three weighting schemes: fractional anisotropy (FA), streamline count, and streamline count with density and length correction (SDL). Two commonly used global and two local graph metrics were examined. The analysis was conducted with two groups of adolescent volunteers who received DTI scans either 12 weeks apart (16.62 ± 1.10 years) or within the same scanning session (30 min apart) (16.65 ± 1.14 years). The intraclass correlation coefficient was used to assess test-retest reliability and the coefficient of variation (CV) was used to assess precision. On average, each edge scheme produced reliable results at both time intervals. Weighted measures outperformed binary measures, with SDL weights producing the most reliable metrics. All edge schemes except FA displayed high CV values, leaving FA as the only edge scheme that consistently showed high precision while also producing reliable results. Overall findings suggest that FA weights are more suited for DTI connectome studies in adolescents.
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Affiliation(s)
- Justin P Yuan
- 1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Eva Henje Blom
- 2 Department of Clinical Science, Child- and Adolescent Psychiatry, Umeå University, Umeå, Sweden.,3 Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, University of California, San Francisco, San Francisco, California
| | - Trevor Flynn
- 1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Yiran Chen
- 1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Tiffany C Ho
- 3 Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, University of California, San Francisco, San Francisco, California.,4 Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Colm G Connolly
- 3 Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, University of California, San Francisco, San Francisco, California.,5 Department of Biomedical Sciences, Florida State University, Tallahassee, Florida
| | - Rebecca A Dumont Walter
- 1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Tony T Yang
- 3 Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, University of California, San Francisco, San Francisco, California
| | - Duan Xu
- 1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Olga Tymofiyeva
- 1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California
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19
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Abstract
We explore the main characteristics of big brain network data that offer unique statistical challenges. The brain networks are biologically expected to be both sparse and hierarchical. Such unique characterizations put specific topological constraints onto statistical approaches and models we can use effectively. We explore the limitations of the current models used in the field and offer alternative approaches and explain new challenges.
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20
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Baum GL, Roalf DR, Cook PA, Ciric R, Rosen AFG, Xia C, Elliott MA, Ruparel K, Verma R, Tunç B, Gur RC, Gur RE, Bassett DS, Satterthwaite TD. The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 2018; 173:275-286. [PMID: 29486323 PMCID: PMC5911236 DOI: 10.1016/j.neuroimage.2018.02.041] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/19/2018] [Accepted: 02/21/2018] [Indexed: 12/27/2022] Open
Abstract
Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.
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Affiliation(s)
- Graham L Baum
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Rastko Ciric
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Adon F G Rosen
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Cedric Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Mark A Elliott
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Birkan Tunç
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
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21
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What We Know About the Brain Structure-Function Relationship. Behav Sci (Basel) 2018; 8:bs8040039. [PMID: 29670045 PMCID: PMC5946098 DOI: 10.3390/bs8040039] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 04/11/2018] [Accepted: 04/16/2018] [Indexed: 11/21/2022] Open
Abstract
How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.
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22
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Taylor PN, Sinha N, Wang Y, Vos SB, de Tisi J, Miserocchi A, McEvoy AW, Winston GP, Duncan JS. The impact of epilepsy surgery on the structural connectome and its relation to outcome. Neuroimage Clin 2018; 18:202-214. [PMID: 29876245 PMCID: PMC5987798 DOI: 10.1016/j.nicl.2018.01.028] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 12/05/2017] [Accepted: 01/21/2018] [Indexed: 01/26/2023]
Abstract
Background Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. Methods We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks. Results Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole. Conclusion Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.
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Affiliation(s)
- Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
| | - Jane de Tisi
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Gavin P Winston
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
| | - John S Duncan
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
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Kaiser M. Mechanisms of Connectome Development. Trends Cogn Sci 2017; 21:703-717. [PMID: 28610804 DOI: 10.1016/j.tics.2017.05.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/12/2017] [Accepted: 05/16/2017] [Indexed: 12/17/2022]
Abstract
At the centenary of D'Arcy Thompson's seminal work 'On Growth and Form', pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain networks. Here, we outline potential principles for connectome development. We will describe recent results on how spatial and temporal factors shape connectome development in health and disease. Understanding the developmental origins of brain diseases in individuals will be crucial for deciding on personalized treatment options. We argue that longitudinal studies, experimentally derived parameters for connection formation, and biologically realistic computational models are needed to better understand the link between brain network development, network structure, and network function.
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Affiliation(s)
- Marcus Kaiser
- ICOS Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.
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