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Falcão M, Monteiro P, Jacinto L. Tactile sensory processing deficits in genetic mouse models of autism spectrum disorder. J Neurochem 2024; 168:2105-2123. [PMID: 38837765 DOI: 10.1111/jnc.16135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 05/04/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024]
Abstract
Altered sensory processing is a common feature in autism spectrum disorder (ASD), as recognized in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Although altered responses to tactile stimuli are observed in over 60% of individuals with ASD, the neurobiological basis of this phenomenon is poorly understood. ASD has a strong genetic component and genetic mouse models can provide valuable insights into the mechanisms underlying tactile abnormalities in ASD. This review critically addresses recent findings regarding tactile processing deficits found in mouse models of ASD, with a focus on behavioral, anatomical, and functional alterations. Particular attention was given to cellular and circuit-level functional alterations, both in the peripheral and central nervous systems, with the objective of highlighting possible convergence mechanisms across models. By elucidating the impact of mutations in ASD candidate genes on somatosensory circuits and correlating them with behavioral phenotypes, this review significantly advances our understanding of tactile deficits in ASD. Such insights not only broaden our comprehension but also pave the way for future therapeutic interventions.
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Affiliation(s)
- Margarida Falcão
- Department of Biomedicine-Experimental Biology Unit, Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Patricia Monteiro
- Department of Biomedicine-Experimental Biology Unit, Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
| | - Luis Jacinto
- Department of Biomedicine-Experimental Biology Unit, Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal
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2
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Varga L, Moca VV, Molnár B, Perez-Cervera L, Selim MK, Díaz-Parra A, Moratal D, Péntek B, Sommer WH, Mureșan RC, Canals S, Ercsey-Ravasz M. Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture. Cell Syst 2024; 15:770-786.e5. [PMID: 39142285 DOI: 10.1016/j.cels.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 11/01/2023] [Accepted: 07/22/2024] [Indexed: 08/16/2024]
Abstract
Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.
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Affiliation(s)
- Levente Varga
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania; Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Vasile V Moca
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Botond Molnár
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania; Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Laura Perez-Cervera
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, San Juan de Alicante, Spain
| | - Mohamed Kotb Selim
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, San Juan de Alicante, Spain
| | - Antonio Díaz-Parra
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
| | - Balázs Péntek
- Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Wolfgang H Sommer
- Institute of Psychopharmacology and Clinic for Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Raul C Mureșan
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania; STAR-UBB Institute, Babeș-Bolyai University, Cluj-Napoca, Romania.
| | - Santiago Canals
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, San Juan de Alicante, Spain.
| | - Maria Ercsey-Ravasz
- Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania; Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania.
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Londei F, Arena G, Ferrucci L, Russo E, Ceccarelli F, Genovesio A. Connecting the dots in the zona incerta: A study of neural assemblies and motifs of inter-area coordination in mice. iScience 2024; 27:108761. [PMID: 38274403 PMCID: PMC10808920 DOI: 10.1016/j.isci.2023.108761] [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: 07/19/2023] [Revised: 10/23/2023] [Accepted: 11/11/2023] [Indexed: 01/27/2024] Open
Abstract
The zona incerta (ZI), a subthalamic area connected to numerous brain regions, has raised clinical interest because its stimulation alleviates the motor symptoms of Parkinson's disease. To explore its coordinative nature, we studied the assembly formation in a dataset of neural recordings in mice and quantified the degree of functional coordination of ZI with other 24 brain areas. We found that the ZI is a highly integrative area. The analysis in terms of "loop-like" motifs, directional assemblies composed of three neurons spanning two areas, has revealed reciprocal functional interactions with reentrant signals that, in most cases, start and end with the activation of ZI units. In support of its proposed integrative role, we found that almost one-third of the ZI's neurons formed assemblies with more than half of the other recorded areas and that loop-like assemblies may stand out as hyper-integrative motifs compared to other types of activation patterns.
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Affiliation(s)
- Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Giulia Arena
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Eleonora Russo
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
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4
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Dai W, Qiu E, Lin X, Zhang S, Zhang M, Han X, Jia Z, Su H, Bian X, Zang X, Li M, Zhang Q, Ran Y, Gong Z, Wang X, Wang R, Tian L, Dong Z, Yu S. Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study. Ann Neurol 2023; 94:1168-1181. [PMID: 37635687 DOI: 10.1002/ana.26783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine. METHODS We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results. RESULTS The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86. INTERPRETATION Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.
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Affiliation(s)
- Wei Dai
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Enchao Qiu
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Xiaoxue Lin
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shuhua Zhang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Mingjie Zhang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xun Han
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zhihua Jia
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hui Su
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiangbing Bian
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiao Zang
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Meng Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Qingkui Zhang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ye Ran
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zihua Gong
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaolin Wang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Rongfei Wang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Zhao Dong
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shengyuan Yu
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
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Mahani FSN, Kalantari A, Fink GR, Hoehn M, Aswendt M. A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain. Front Neurosci 2023; 17:1194630. [PMID: 37554291 PMCID: PMC10405456 DOI: 10.3389/fnins.2023.1194630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/05/2023] [Indexed: 08/10/2023] Open
Abstract
Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.
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Affiliation(s)
- Fatemeh S. N. Mahani
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Aref Kalantari
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Markus Aswendt
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
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Schmitt O, Eipert P, Wang Y, Kanoke A, Rabiller G, Liu J. Connectome-based prediction of functional impairment in experimental stroke models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.05.539601. [PMID: 37205373 PMCID: PMC10187266 DOI: 10.1101/2023.05.05.539601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.
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Affiliation(s)
- Oliver Schmitt
- Medical School Hamburg - University of Applied Sciences, Department of Anatomy; University of Rostock, Institute of Anatomy
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
| | - Peter Eipert
- Medical School Hamburg - University of Applied Sciences, Department of Anatomy; University of Rostock, Institute of Anatomy
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
| | - Yonggang Wang
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
- Department of Neurological Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China, 100050
| | - Atsushi Kanoke
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
| | - Gratianne Rabiller
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
| | - Jialing Liu
- Department of Neurological Surgery, UCSF
- SFVAMC, 1700 Owens Street, San Francisco, CA 94158
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7
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Yang Y, Zhu Q, Wang L, Gao D, Wang Z, Geng Z. Effects of hypertension and aging on brain function in spontaneously hypertensive rats: a longitudinal resting-state functional magnetic resonance imaging study. Cereb Cortex 2022; 33:5493-5500. [PMID: 36408643 PMCID: PMC10152091 DOI: 10.1093/cercor/bhac436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 11/22/2022] Open
Abstract
Abstract
To investigate the dynamic evolution of brain function under the comorbidities of hypertension and aging. Resting-state functional magnetic resonance imaging scans were longitudinally acquired at 10, 24, and 52 weeks in spontaneously hypertensive rats (SHRs) and Wistar-Kyoto rats. We computed the mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), and functional connectivity (FC). There was no interaction between hypertension and aging on brain function. The main effect of aging reflects primarily the cumulative increase of brain activity, especially the increase of mALFF in amygdala and mReHo in cingulate cortex, accompanied by the decrease of brain activity. The main effect of hypertension reflects primarily decreased brain activity in default modal network, accompanied by increased brain activity. The main effect of aging shows reduced brain FC as early as 24 weeks, and the main effect of hypertension shows higher brain FC in SHRs. The novel discovery is that 1 brain FC network increased linearly with age in SHRs, in addition to the linearly decreasing FC. Hypertension and aging independently contribute to spatiotemporal alterations in brain function in SHRs following ongoing progression and compensation. This study provides new insight into the dynamic characteristics of brain function.
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Affiliation(s)
- Yingying Yang
- Hebei Medical University Medical Imaging Specialty, Graduate School, , Shijiazhuang 050000 , China
- The First Hospital of Qinhuangdao Department of Imaging, , Qinhuangdao 066000 , China
| | - Qingfeng Zhu
- The Second Hospital of Hebei Medical University Department of Medical Imaging, , Shijiazhuang 050000 , China
| | - Lixin Wang
- The Second Hospital of Hebei Medical University Department of Medical Imaging, , Shijiazhuang 050000 , China
| | - Duo Gao
- The Second Hospital of Hebei Medical University Department of Medical Imaging, , Shijiazhuang 050000 , China
| | - Zhanqiu Wang
- The First Hospital of Qinhuangdao Department of Imaging, , Qinhuangdao 066000 , China
| | - Zuojun Geng
- The Second Hospital of Hebei Medical University Department of Medical Imaging, , Shijiazhuang 050000 , China
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8
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Zhang YS, Takahashi DY, El Hady A, Liao DA, Ghazanfar AA. Active neural coordination of motor behaviors with internal states. Proc Natl Acad Sci U S A 2022; 119:e2201194119. [PMID: 36122243 PMCID: PMC9522379 DOI: 10.1073/pnas.2201194119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/16/2022] [Indexed: 11/18/2022] Open
Abstract
The brain continuously coordinates skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that the brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time series, we found that information flowing from the brain to movements and heart-rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct, but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brain's dynamics actively and efficiently coordinate motor behavior with internal physiology.
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Affiliation(s)
- Yisi S. Zhang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
| | - Daniel Y. Takahashi
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Brain Institute, Federal University of Rio Grande do Norte, Natal 59076-550, Brazil
| | - Ahmed El Hady
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Center for Advanced Study of Collective Behavior, University of Konstanz, Konstanz 78464, Germany
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Konstanz 78464, Germany
| | - Diana A. Liao
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
| | - Asif A. Ghazanfar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Department of Psychology, Princeton University, Princeton, NJ 08544
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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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10
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Pérez-Ramírez Ú, López-Madrona VJ, Pérez-Segura A, Pallarés V, Moreno A, Ciccocioppo R, Hyytiä P, Sommer WH, Moratal D, Canals S. Brain Network Allostasis after Chronic Alcohol Drinking Is Characterized by Functional Dedifferentiation and Narrowing. J Neurosci 2022; 42:4401-4413. [PMID: 35437279 PMCID: PMC9145238 DOI: 10.1523/jneurosci.0389-21.2022] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 03/25/2022] [Accepted: 03/30/2022] [Indexed: 11/21/2022] Open
Abstract
Alcohol use disorder (AUD) causes complex alterations in the brain that are poorly understood. The heterogeneity of drinking patterns and the high incidence of comorbid factors compromise mechanistic investigations in AUD patients. Here we used male Marchigian Sardinian alcohol-preferring (msP) rats, a well established animal model of chronic alcohol drinking, and a combination of longitudinal resting-state fMRI and manganese-enhanced MRI to provide objective measurements of brain connectivity and activity, respectively. We found that 1 month of chronic alcohol drinking changed the correlation between resting-state networks. The change was not homogeneous, resulting in the reorganization of pairwise interactions and a shift in the equilibrium of functional connections. We identified two fundamentally different forms of network reorganization. First is functional dedifferentiation, which is defined as a regional increase in neuronal activity and overall correlation, with a concomitant decrease in preferential connectivity between specific networks. Through this mechanism, occipital cortical areas lost their specific interaction with sensory-insular cortex, striatal, and sensorimotor networks. Second is functional narrowing, which is defined as an increase in neuronal activity and preferential connectivity between specific brain networks. Functional narrowing strengthened the interaction between striatal and prefrontocortical networks, involving the anterior insular, cingulate, orbitofrontal, prelimbic, and infralimbic cortices. Importantly, these two types of alterations persisted after alcohol discontinuation, suggesting that dedifferentiation and functional narrowing rendered persistent network states. Our results support the idea that chronic alcohol drinking, albeit at moderate intoxicating levels, induces an allostatic change in the brain functional connectivity that propagates into early abstinence.SIGNIFICANCE STATEMENT Excessive consumption of alcohol is positioned among the top five risk factors for disease and disability. Despite this priority, the transformations that the nervous system undergoes from an alcohol-naive state to a pathologic alcohol drinking are not well understood. In our study, we use an animal model with proven translational validity to study this transformation longitudinally. The results show that shortly after chronic alcohol consumption there is an increase in redundant activity shared by brain structures, and the specific communication shrinks to a set of pathways. This functional dedifferentiation and narrowing are not reversed immediately after alcohol withdrawal but persist during early abstinence. We causally link chronic alcohol drinking with an early and abstinence-persistent retuning of the functional equilibrium of the brain.
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Affiliation(s)
- Úrsula Pérez-Ramírez
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, E-46022 Valencia, Spain
| | - Víctor J López-Madrona
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, 03550 Sant Joan d'Alacant, Spain
| | - Andrés Pérez-Segura
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, 03550 Sant Joan d'Alacant, Spain
| | - Vicente Pallarés
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, 03550 Sant Joan d'Alacant, Spain
| | - Andrea Moreno
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, 03550 Sant Joan d'Alacant, Spain
| | | | - Petri Hyytiä
- Department of Pharmacology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Wolfgang H Sommer
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68159 Mannheim, Germany
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, E-46022 Valencia, Spain
| | - Santiago Canals
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, 03550 Sant Joan d'Alacant, Spain
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11
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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12
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Kotlarz P, Nino JC, Febo M. Connectomic analysis of Alzheimer's disease using percolation theory. Netw Neurosci 2022; 6:213-233. [PMID: 36605889 PMCID: PMC9810282 DOI: 10.1162/netn_a_00221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/08/2021] [Indexed: 01/09/2023] Open
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory-based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates (n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network.
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Affiliation(s)
- Parker Kotlarz
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA,* Corresponding Author:
| | - Juan C. Nino
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA
| | - Marcelo Febo
- Department of Psychiatry, University of Florida, Gainesville, FL, USA
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13
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Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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14
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Ra D, Sa B, Sl B, Js M, Sj M, DA D, Ew S, O K, Eb B, Ad C, Vx T, Gg G, Pa C, Dc M, Wg B. Is Exposure to BMAA a Risk Factor for Neurodegenerative Diseases? A Response to a Critical Review of the BMAA Hypothesis. Neurotox Res 2021; 39:81-106. [PMID: 33547590 PMCID: PMC7904546 DOI: 10.1007/s12640-020-00302-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 12/15/2022]
Abstract
In a literature survey, Chernoff et al. (2017) dismissed the hypothesis that chronic exposure to β-N-methylamino-L-alanine (BMAA) may be a risk factor for progressive neurodegenerative disease. They question the growing scientific literature that suggests the following: (1) BMAA exposure causes ALS/PDC among the indigenous Chamorro people of Guam; (2) Guamanian ALS/PDC shares clinical and neuropathological features with Alzheimer's disease, Parkinson's disease, and ALS; (3) one possible mechanism for protein misfolds is misincorporation of BMAA into proteins as a substitute for L-serine; and (4) chronic exposure to BMAA through diet or environmental exposures to cyanobacterial blooms can cause neurodegenerative disease. We here identify multiple errors in their critique including the following: (1) their review selectively cites the published literature; (2) the authors reported favorably on HILIC methods of BMAA detection while the literature shows significant matrix effects and peak coelution in HILIC that may prevent detection and quantification of BMAA in cyanobacteria; (3) the authors build alternative arguments to the BMAA hypothesis, rather than explain the published literature which, to date, has been unable to refute the BMAA hypothesis; and (4) the authors erroneously attribute methods to incorrect studies, indicative of a failure to carefully consider all relevant publications. The lack of attention to BMAA research begins with the review's title which incorrectly refers to BMAA as a "non-essential" amino acid. Research regarding chronic exposure to BMAA as a cause of human neurodegenerative diseases is emerging and requires additional resources, validation, and research. Here, we propose strategies for improvement in the execution and reporting of analytical methods and the need for additional and well-executed inter-lab comparisons for BMAA quantitation. We emphasize the need for optimization and validation of analytical methods to ensure that they are fit-for-purpose. Although there remain gaps in the literature, an increasingly large body of data from multiple independent labs using orthogonal methods provides increasing evidence that chronic exposure to BMAA may be a risk factor for neurological illness.
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Affiliation(s)
- Dunlop Ra
- Brain Chemistry Labs, Institute for Ethnomedicine, Jackson, WY, USA.
| | - Banack Sa
- Brain Chemistry Labs, Institute for Ethnomedicine, Jackson, WY, USA
| | - Bishop Sl
- Lewis Research Group, Faculty of Science, University of Calgary, Alberta, Canada
| | - Metcalf Js
- Brain Chemistry Labs, Institute for Ethnomedicine, Jackson, WY, USA
| | - Murch Sj
- Department of Chemistry, University of British Columbia, Kelowna, BC, Canada
| | - Davis DA
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Stommel Ew
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Karlsson O
- Department of Environmental Science, Stockholm University, Stockholm, Sweden
| | - Brittebo Eb
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | - Tan Vx
- Department of Biological Sciences, Macquarie University Centre for Motor Neuron Disease Research, Macquarie University, Ryde, Australia
| | - Guillemin Gg
- Department of Biological Sciences, Macquarie University Centre for Motor Neuron Disease Research, Macquarie University, Ryde, Australia
| | - Cox Pa
- Brain Chemistry Labs, Institute for Ethnomedicine, Jackson, WY, USA
| | - Mash Dc
- Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Bradley Wg
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
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15
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Whitesell JD, Liska A, Coletta L, Hirokawa KE, Bohn P, Williford A, Groblewski PA, Graddis N, Kuan L, Knox JE, Ho A, Wakeman W, Nicovich PR, Nguyen TN, van Velthoven CTJ, Garren E, Fong O, Naeemi M, Henry AM, Dee N, Smith KA, Levi B, Feng D, Ng L, Tasic B, Zeng H, Mihalas S, Gozzi A, Harris JA. Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network. Neuron 2020; 109:545-559.e8. [PMID: 33290731 PMCID: PMC8150331 DOI: 10.1016/j.neuron.2020.11.011] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/08/2020] [Accepted: 11/13/2020] [Indexed: 12/28/2022]
Abstract
The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN. Mouse resting-state default mode network anatomy described at high resolution in 3D Systematic axon tracing shows cortical DMN regions are preferentially interconnected Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons
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Affiliation(s)
| | - Adam Liska
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy; DeepMind, London EC4A 3TW, UK
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy; Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto, Italy
| | | | - Phillip Bohn
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ali Williford
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Joseph E Knox
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Boaz Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, Italy
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
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16
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Contextual experience modifies functional connectome indices of topological strength and efficiency. Sci Rep 2020; 10:19843. [PMID: 33199790 PMCID: PMC7670469 DOI: 10.1038/s41598-020-76935-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/27/2020] [Indexed: 11/08/2022] Open
Abstract
Stimuli presented at short temporal delays before functional magnetic resonance imaging (fMRI) can have a robust impact on the organization of synchronous activity in resting state networks. This presents an opportunity to investigate how sensory, affective and cognitive stimuli alter functional connectivity in rodent models. In the present study we assessed the effect on functional connectivity of a familiar contextual stimulus presented 10 min prior to sedation for imaging. A subset of animals were co-presented with an unfamiliar social stimulus in the same environment to further investigate the effect of familiarity on network topology. Rats were imaged at 11.1 T and graph theory analysis was applied to matrices generated from seed-based functional connectivity data sets with 144 brain regions (nodes) and 10,152 pairwise correlations (after excluding 144 diagonal edges). Our results show substantial changes in network topology in response to the familiar (context). Presentation of the familiar context, both in the absence and presence of the social stimulus, strongly reduced network strength, global efficiency, and altered the location of the highest eigenvector centrality nodes from cortex to the hypothalamus. We did not observe changes in modular organization, nodal cartographic assignments, assortative mixing, rich club organization, and network resilience. We propose that experiential factors, perhaps involving associative or episodic memory, can exert a dramatic effect on functional network strength and efficiency when presented at a short temporal delay before imaging.
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17
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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.
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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
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18
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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.
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19
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Febo M, Rani A, Yegla B, Barter J, Kumar A, Wolff CA, Esser K, Foster TC. Longitudinal Characterization and Biomarkers of Age and Sex Differences in the Decline of Spatial Memory. Front Aging Neurosci 2020; 12:34. [PMID: 32153384 PMCID: PMC7044155 DOI: 10.3389/fnagi.2020.00034] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 02/04/2020] [Indexed: 01/10/2023] Open
Abstract
The current longitudinal study examined factors (sex, physical function, response to novelty, ability to adapt to a shift in light/dark cycle, brain connectivity), which might predict the emergence of impaired memory during aging. Male and female Fisher 344 rats were tested at 6, 12, and 18 months of age. Impaired spatial memory developed in middle-age (12 months), particularly in males, and the propensity for impairment increased with advanced age. A reduced response to novelty was observed over the course of aging, which is inconsistent with cross-sectional studies. This divergence likely resulted from differences in the history of environmental enrichment/impoverishment for cross-sectional and longitudinal studies. Animals that exhibited lower level exploration of the inner region on the open field test exhibited better memory at 12 months. Furthermore, males that exhibited a longer latency to enter a novel environment at 6 months, exhibited better memory at 12 months. For females, memory at 12 months was correlated with the ability to behaviorally adapt to a shift in light/dark cycle. Functional magnetic resonance imaging of the brain, conducted at 12 months, indicated that the decline in memory was associated with altered functional connectivity within different memory systems, most notably between the hippocampus and multiple regions such as the retrosplenial cortex, thalamus, striatum, and amygdala. Overall, some factors, specifically response to novelty at an early age and the capacity to adapt to shifts in light cycle, predicted spatial memory in middle-age, and spatial memory is associated with corresponding changes in brain connectivity. We discuss similarities and differences related to previous longitudinal and cross-sectional studies, as well as the role of sex differences in providing a theoretical framework to guide future longitudinal research on the trajectory of cognitive decline. In addition to demonstrating the power of longitudinal studies, these data highlight the importance of middle-age for identifying potential predictive indicators of sexual dimorphism in the trajectory in brain and cognitive aging.
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Affiliation(s)
- Marcelo Febo
- Department of Psychiatry, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Asha Rani
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Brittney Yegla
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Jolie Barter
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Ashok Kumar
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Christopher A Wolff
- Department of Physiology and Functional Genomics, Myology Institute, University of Florida, Gainesville, FL, United States
| | - Karyn Esser
- Department of Physiology and Functional Genomics, Myology Institute, University of Florida, Gainesville, FL, United States
| | - Thomas C Foster
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Genetics and Genomics Program, University of Florida, Gainesville, FL, United States
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20
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Straathof M, Sinke MRT, Roelofs TJM, Blezer ELA, Sarabdjitsingh RA, van der Toorn A, Schmitt O, Otte WM, Dijkhuizen RM. Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain. Sci Rep 2020; 10:56. [PMID: 31919379 PMCID: PMC6952407 DOI: 10.1038/s41598-019-56834-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 12/16/2019] [Indexed: 01/08/2023] Open
Abstract
An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis.
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Affiliation(s)
- Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Michel R T Sinke
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Theresia J M Roelofs
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.,Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - R Angela Sarabdjitsingh
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Annette van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Oliver Schmitt
- Department of Anatomy, University of Rostock, Rostock, Germany
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.,Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
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21
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Abstract
Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure-function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.
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22
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Deslauriers-Gauthier S, Lina JM, Butler R, Whittingstall K, Gilbert G, Bernier PM, Deriche R, Descoteaux M. White matter information flow mapping from diffusion MRI and EEG. Neuroimage 2019; 201:116017. [PMID: 31319180 DOI: 10.1016/j.neuroimage.2019.116017] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 02/07/2019] [Accepted: 07/12/2019] [Indexed: 10/26/2022] Open
Abstract
The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.
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Affiliation(s)
| | | | | | | | | | | | - Rachid Deriche
- Inria Sophia Antipolis-Meditérranée, Université Côte d'Azur, France
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23
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Franzmeier N, Rubinski A, Neitzel J, Kim Y, Damm A, Na DL, Kim HJ, Lyoo CH, Cho H, Finsterwalder S, Duering M, Seo SW, Ewers M. Functional connectivity associated with tau levels in ageing, Alzheimer's, and small vessel disease. Brain 2019; 142:1093-1107. [PMID: 30770704 PMCID: PMC6439332 DOI: 10.1093/brain/awz026] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/27/2018] [Accepted: 12/21/2018] [Indexed: 12/14/2022] Open
Abstract
In Alzheimer's disease, tau pathology spreads hierarchically from the inferior temporal lobe throughout the cortex, ensuing cognitive decline and dementia. Similarly, circumscribed patterns of pathological tau have been observed in normal ageing and small vessel disease, suggesting a spatially ordered distribution of tau pathology across normal ageing and different diseases. In vitro findings suggest that pathological tau may spread 'prion-like' across neuronal connections in an activity-dependent manner. Supporting this notion, functional brain networks show a spatial correspondence to tau deposition patterns. However, it remains unclear whether higher network-connectivity facilitates tau propagation. To address this, we included 55 normal aged elderly (i.e. cognitively normal, amyloid-negative), 50 Alzheimer's disease patients (i.e. amyloid-positive) covering the preclinical to dementia spectrum, as well as 36 patients with pure (i.e. amyloid-negative) vascular cognitive impairment due to small vessel disease. All subjects were assessed with AV1451 tau-PET and resting-state functional MRI. Within each group, we computed atlas-based resting-state functional MRI functional connectivity across 400 regions of interest covering the entire neocortex. Using the same atlas, we also assessed within each group the covariance of tau-PET levels among the 400 regions of interest. We found that higher resting-state functional MRI assessed functional connectivity between any given region of interest pair was associated with higher covariance in tau-PET binding in corresponding regions of interest. This result was consistently found in normal ageing, Alzheimer's disease and vascular cognitive impairment. In particular, inferior temporal tau-hotspots, as defined by highest tau-PET uptake, showed high predictive value of tau-PET levels in functionally closely connected regions of interest. These associations between functional connectivity and tau-PET uptake were detected regardless of presence of dementia symptoms (mild cognitive impairment or dementia), amyloid deposition (as assessed by amyloid-PET) or small vessel disease. Our findings suggest that higher functional connectivity between brain regions is associated with shared tau-levels, supporting the view of prion-like tau spreading facilitated by neural activity.
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Affiliation(s)
- Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
| | - Julia Neitzel
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
| | - Yeshin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Alexander Damm
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hana Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sofia Finsterwalder
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
- Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Feodor-Lynen Straße 17, Munich, Germany
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24
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Straathof M, Sinke MRT, Dijkhuizen RM, Otte WM. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J Cereb Blood Flow Metab 2019; 39:189-209. [PMID: 30375267 PMCID: PMC6360487 DOI: 10.1177/0271678x18809547] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/28/2018] [Indexed: 12/29/2022]
Abstract
The mammalian brain is composed of densely connected and interacting regions, which form structural and functional networks. An improved understanding of the structure-function relation is crucial to understand the structural underpinnings of brain function and brain plasticity after injury. It is currently unclear how functional connectivity strength relates to structural connectivity strength. We obtained an overview of recent papers that report on correspondences between quantitative functional and structural connectivity measures in the mammalian brain. We included network studies in which functional connectivity was measured with resting-state fMRI, and structural connectivity with either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven of the 28 included studies showed a positive structure-function relationship. Large inter-study variations were found comparing functional connectivity strength with either quantitative diffusion-based (correlation coefficient (r) ranges: 0.18-0.82) or neuronal tracer-based structural connectivity measures (r = 0.24-0.74). Two functional datasets demonstrated lower structure-function correlations with neuronal tracer-based (r = 0.22 and r = 0.30) than with diffusion-based measures (r = 0.49 and r = 0.65). The robust positive quantitative structure-function relationship supports the hypothesis that structural connectivity provides the hardware from which functional connectivity emerges. However, methodological differences between the included studies complicate the comparison across studies, which emphasize the need for validation and standardization in brain structure-function studies.
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Affiliation(s)
- Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Michel RT Sinke
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
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25
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Dell'Italia J, Johnson MA, Vespa PM, Monti MM. Network Analysis in Disorders of Consciousness: Four Problems and One Proposed Solution (Exponential Random Graph Models). Front Neurol 2018; 9:439. [PMID: 29946293 PMCID: PMC6005847 DOI: 10.3389/fneur.2018.00439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 05/24/2018] [Indexed: 12/24/2022] Open
Abstract
In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the neuroimaging literature, suffer from four important shortcomings. First, they require arbitrary fixing of the number of connections (i.e., density) across networks which are likely to have different "natural" (i.e., stable) density (e.g., patients vs. controls, vegetative state vs. minimally conscious state patients). Second, when describing networks, they do not control for the fact that many characteristics are interrelated, particularly some of the most popular metrics employed (e.g., nodal degree, clustering coefficient)-which can lead to spurious results. Third, in the clinical domain of disorders of consciousness, there currently are no methods for incorporating structural connectivity in the characterization of functional networks which clouds the interpretation of functional differences across groups with different underlying pathology as well as in longitudinal approaches where structural reorganization processes might be operating. Finally, current methods do not allow assessing the dynamics of network change over time. We present a different framework for network analysis, based on Exponential Random Graph Models, which overcomes the above limitations and is thus particularly well suited for clinical populations with disorders of consciousness. We demonstrate this approach in the context of the longitudinal study of recovery from coma. First, our data show that throughout recovery from coma, brain graphs vary in their natural level of connectivity (from 10.4 to 14.5%), which conflicts with the standard approach of imposing arbitrary and equal density thresholds across networks (e.g., time-points, subjects, groups). Second, we show that failure to consider the interrelation between network measures does lead to spurious characterization of both inter- and intra-regional brain connectivity. Finally, we show that Separable Temporal ERGM can be employed to describe network dynamics over time revealing the specific pattern of formation and dissolution of connectivity that accompany recovery from coma.
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Affiliation(s)
- John Dell'Italia
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Micah A. Johnson
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Paul M. Vespa
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Martin M. Monti
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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26
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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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