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Sengupta A, Wang F, Mishra A, Reed JL, Chen LM, Gore JC. Detection and characterization of resting state functional networks in squirrel monkey brain. Cereb Cortex Commun 2023; 4:tgad018. [PMID: 37753115 PMCID: PMC10518810 DOI: 10.1093/texcom/tgad018] [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: 06/02/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Resting-state fMRI based on analyzing BOLD signals is widely used to derive functional networks in the brain and how they alter during disease or injury conditions. Resting-state networks can also be used to study brain functional connectomes across species, which provides insights into brain evolution. The squirrel monkey (SM) is a non-human primate (NHP) that is widely used as a preclinical model for experimental manipulations to understand the organization and functioning of the brain. We derived resting-state networks from the whole brain of anesthetized SMs using Independent Component Analysis of BOLD acquisitions. We detected 15 anatomically constrained resting-state networks localized in the cortical and subcortical regions as well as in the white-matter. Networks encompassing visual, somatosensory, executive control, sensorimotor, salience and default mode regions, and subcortical networks including the Hippocampus-Amygdala, thalamus, basal-ganglia and brainstem region correspond well with previously detected networks in humans and NHPs. The connectivity pattern between the networks also agrees well with previously reported seed-based resting-state connectivity of SM brain. This study demonstrates that SMs share remarkable homologous network organization with humans and other NHPs, thereby providing strong support for their suitability as a translational animal model for research and additional insight into brain evolution across species.
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Affiliation(s)
- Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Jamie L Reed
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Department of Psychology, Vanderbilt University, Nashville, TN, United States of America
| | - Li Min Chen
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America
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2
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Physical activity moderates the association between executive function and functional connectivity in older adults. AGING BRAIN 2022; 2:100036. [PMID: 36908885 PMCID: PMC9999439 DOI: 10.1016/j.nbas.2022.100036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/23/2022] [Accepted: 02/08/2022] [Indexed: 11/23/2022] Open
Abstract
Recent evidence suggests that physical activity may influence the functional connectivity of the aging brain. The purpose of this study was to examine the influence of physical activity on the association between executive function and functional connectivity of key brain networks and graph theory metrics in community-dwelling older adults. Participants were 47 older adults (M = 73 years; SD = 5.92) who participated in neuropsychological testing, physical activity measurements, and magnetic resonance imaging (MRI). Seed-to-voxel moderation analyses and graph theory analyses were conducted. Physical activity was significantly positively associated with default mode network functional connectivity (DMN FC; Posterior Cingulate Gyrus, p-FDR = 0.005; Frontal Pole (L), p-FDR = 0.005; Posterior Cingulate Gyrus, p-FDR = 0.006; Superior Frontal Gyrus (L), p-FDR = 0.016) and dorsal attention network functional connectivity (DAN FC; Inferior Frontal Gyrus Pars Opercularis (R), p-FDR = 0.044). The interaction between physical activity and executive function on the DMN FC and DAN FC was analyzed. The interaction between executive function and physical activity was significantly associated with DMN FC. When this significant interaction was probed, the association between physical activity and DMN FC differed between levels of high and low executive function such that the association was only significant at levels of high executive function. These results suggest that greater physical activity in later life is associated with greater DMN and DAN FC and provides evidence for the importance of physical activity in cognitively healthy older adults.
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3
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Luo W, Constable RT. Inside information: Systematic within-node functional connectivity changes observed across tasks or groups. Neuroimage 2021; 247:118792. [PMID: 34896289 PMCID: PMC8840325 DOI: 10.1016/j.neuroimage.2021.118792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/16/2021] [Accepted: 12/07/2021] [Indexed: 11/23/2022] Open
Abstract
Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .
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Affiliation(s)
- Wenjing Luo
- Department of Biomedical Engineering, Yale University School of Medicine USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University School of Medicine USA; Radiology and Biomedical Imaging, Yale University School of Medicine USA; Interdepartmental Neuroscience Program, Yale University School of Medicine USA.
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4
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Menardi A, Reineberg AE, Smith LL, Favaretto C, Vallesi A, Banich MT, Santarnecchi E. Topographical functional correlates of interindividual differences in executive functions in young healthy twins. Brain Struct Funct 2021; 227:49-62. [PMID: 34865178 PMCID: PMC8741656 DOI: 10.1007/s00429-021-02388-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 09/15/2021] [Indexed: 11/26/2022]
Abstract
Executive functions (EF) are a set of higher-order cognitive abilities that enable goal-directed behavior by controlling lower-level operations. In the brain, those functions have been traditionally associated with activity in the Frontoparietal Network, but recent neuroimaging studies have challenged this view in favor of more widespread cortical involvement. In the present study, we aimed to explore whether the network that serves as critical hubs at rest, which we term network reliance, differentiate individuals as a function of their level of EF. Furthermore, we investigated whether such differences are driven by genetic as compared to environmental factors. For this purpose, resting-state functional magnetic resonance imaging data and the behavioral testing of 453 twins from the Colorado Longitudinal Twins Study were analyzed. Separate indices of EF performance were obtained according to a bifactor unity/diversity model, distinguishing between three independent components representing: Common EF, Shifting-specific and Updating-specific abilities. Through an approach of step-wise in silico network lesioning of the individual functional connectome, we show that interindividual differences in EF are associated with different dependencies on neural networks at rest. Furthermore, these patterns show evidence of mild heritability. Such findings add knowledge to the understanding of brain states at rest and their connection with human behavior, and how they might be shaped by genetic influences.
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Affiliation(s)
- Arianna Menardi
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Padova Neuroscience Center & Department of Neuroscience, University of Padova, Padua, PD, Italy
| | - Andrew E Reineberg
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Louisa L Smith
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Chiara Favaretto
- Padova Neuroscience Center & Department of Neuroscience, University of Padova, Padua, PD, Italy
| | - Antonino Vallesi
- Padova Neuroscience Center & Department of Neuroscience, University of Padova, Padua, PD, Italy
- IRCCS San Camillo Hospital, Venice, Italy
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA
| | - Emiliano Santarnecchi
- Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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5
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Dai W, Qiu E, Chen Y, Xing X, Xi W, Zhang M, Li K, Tian L, Dong Z, Yu S. Enhanced functional connectivity between habenula and salience network in medication-overuse headache complicating chronic migraine positions it within the addiction disorders: an ICA-based resting-state fMRI study. J Headache Pain 2021; 22:107. [PMID: 34503441 PMCID: PMC8428097 DOI: 10.1186/s10194-021-01318-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 08/12/2021] [Indexed: 12/20/2022] Open
Abstract
Background Medication-overuse headache (MOH) is a relatively frequently occurring secondary headache caused by overuse of analgesics and/or acute migraine medications. It is believed that MOH is associated with dependence behaviors and substance addiction, in which the salience network (SN) and the habenula may play an important role. This study aims to investigate the resting-state (RS) functional connectivity between the habenula and the SN in patients with MOH complicating chronic migraine (CM) compared with those with episodic migraine (EM) and healthy controls (HC). Methods RS-fMRI and 3-dimensional T1-weighted images of 17 patients with MOH + CM, 18 patients with EM and 30 matched healthy HC were obtained. The RS-fMRI data were analyzed using the independent component analysis (ICA) method to investigate the group differences of functional connectivity between the habenula and the SN in three groups. Correlation analysis was performed thereafter with all clinical variables by Pearson correlation. Results Increased functional connectivity between bilateral habenula and SN was detected in patients with MOH + CM compared with patients with EM and HC respectively. Correlation analysis showed significant correlation between medication overuse duration and habenula-SN connectivity in MOH + CM patients. Conclusions The current study supported MOH to be lying within a spectrum of dependence and addiction disorder. The enhanced functional connectivity of the habenula with SN may correlate to the development or chronification of MOH. Furthermore, the habenula may be an indicator or treatment target for MOH for its integrative role involved in multiple aspects of MOH.
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Affiliation(s)
- Wei Dai
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China.,Chinese PLA Medical School, 100853, Beijing, China
| | - Enchao Qiu
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China
| | - Yun Chen
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China.,Chinese PLA Medical School, 100853, Beijing, China
| | - Xinbo Xing
- Department of Radiology, Fourth Medical Center of Chinese PLA General Hospital, 100048, Beijing, China
| | - Wei Xi
- Department of Radiology, Fourth Medical Center of Chinese PLA General Hospital, 100048, Beijing, China
| | - Meichen Zhang
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China
| | - Ke Li
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China.,Chinese PLA Medical School, 100853, Beijing, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China
| | - Zhao Dong
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China.
| | - Shengyuan Yu
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, 100853, Beijing, China.
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Hsu HM, Yao ZF, Hwang K, Hsieh S. Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition. PLoS One 2020; 15:e0242985. [PMID: 33270664 PMCID: PMC7714245 DOI: 10.1371/journal.pone.0242985] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022] Open
Abstract
The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.
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Affiliation(s)
- Howard Muchen Hsu
- Department of Psychology, National Cheng Kung University, Tainan, Taiwan
| | - Zai-Fu Yao
- Department of Psychology, Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
| | - Kai Hwang
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, United States of America
- Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa, United States of America
| | - Shulan Hsieh
- Department of Psychology, National Cheng Kung University, Tainan, Taiwan
- Institute of Allied Health Sciences, National Cheng Kung University, Tainan, Taiwan
- Department and Institute of Public Health, National Cheng Kung University, Tainan, Taiwan
- * E-mail:
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7
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Pirondini E, Goldshuv-Ezra N, Zinger N, Britz J, Soroker N, Deouell LY, Ville DVD. Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect. Neuroimage Clin 2020; 26:102237. [PMID: 32199285 PMCID: PMC7083886 DOI: 10.1016/j.nicl.2020.102237] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 02/07/2023]
Abstract
Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.
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Affiliation(s)
- Elvira Pirondini
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Nurit Goldshuv-Ezra
- Department of Neurological Rehabilitation, Loewenstein Rehabilitation Hospital, Raanana, Israel; Evoked Potentials Laboratory, Technion - Israel Institute of Technology, Haifa, Israel
| | - Nofya Zinger
- Department of Psychology and Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Israel
| | - Juliane Britz
- Department of Psychology and Neurology Unit, Medicine Section, Faculty of Science and Medicine, University of Fribourg, Fribourg 1700, Switzerland
| | - Nachum Soroker
- Department of Neurological Rehabilitation, Loewenstein Rehabilitation Hospital, Raanana, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Leon Y Deouell
- Department of Psychology and Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Israel.
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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8
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Feis RA, Bouts MJRJ, Dopper EGP, Filippini N, Heise V, Trachtenberg AJ, van Swieten JC, van Buchem MA, van der Grond J, Mackay CE, Rombouts SARB. Multimodal MRI of grey matter, white matter, and functional connectivity in cognitively healthy mutation carriers at risk for frontotemporal dementia and Alzheimer's disease. BMC Neurol 2019; 19:343. [PMID: 31881858 PMCID: PMC6933911 DOI: 10.1186/s12883-019-1567-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/11/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are associated with divergent differences in grey matter volume, white matter diffusion, and functional connectivity. However, it is unknown at what disease stage these differences emerge. Here, we investigate whether divergent differences in grey matter volume, white matter diffusion, and functional connectivity are already apparent between cognitively healthy carriers of pathogenic FTD mutations, and cognitively healthy carriers at increased AD risk. METHODS We acquired multimodal magnetic resonance imaging (MRI) brain scans in cognitively healthy subjects with (n=39) and without (n=36) microtubule-associated protein Tau (MAPT) or progranulin (GRN) mutations, and with (n=37) and without (n=38) apolipoprotein E ε4 (APOE4) allele. We evaluated grey matter volume using voxel-based morphometry, white matter diffusion using tract-based spatial statistics (TBSS), and region-to-network functional connectivity using dual regression in the default mode network and salience network. We tested for differences between the respective carriers and controls, as well as for divergence of those differences. For the divergence contrast, we additionally performed region-of-interest TBSS analyses in known areas of white matter diffusion differences between FTD and AD (i.e., uncinate fasciculus, forceps minor, and anterior thalamic radiation). RESULTS MAPT/GRN carriers did not differ from controls in any modality. APOE4 carriers had lower fractional anisotropy than controls in the callosal splenium and right inferior fronto-occipital fasciculus, but did not show grey matter volume or functional connectivity differences. We found no divergent differences between both carrier-control contrasts in any modality, even in region-of-interest analyses. CONCLUSIONS Concluding, we could not find differences suggestive of divergent pathways of underlying FTD and AD pathology in asymptomatic risk mutation carriers. Future studies should focus on asymptomatic mutation carriers that are closer to symptom onset to capture the first specific signs that may differentiate between FTD and AD.
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Affiliation(s)
- Rogier A. Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Mark J. R. J. Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Elise G. P. Dopper
- Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Nicola Filippini
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Verena Heise
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Aaron J. Trachtenberg
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Mark A. van Buchem
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Clare E. Mackay
- FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Serge A. R. B. Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
- LIBC, Leiden Institute for Brain and Cognition, Leiden, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands
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9
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Ji B, Wang S, Liu Z, Weinberg BD, Yang X, Liu T, Wang L, Mao H. Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI. NEUROIMAGE-CLINICAL 2019; 23:101864. [PMID: 31176951 PMCID: PMC6558214 DOI: 10.1016/j.nicl.2019.101864] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 04/30/2019] [Accepted: 05/19/2019] [Indexed: 01/25/2023]
Abstract
Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions. Signal profiles of dynamic susceptibility contrast MRI data of brain tumors reflect hemodynamic properties of tumor tissue. Features in signal profiles extracted by machine learning methods revealed the hemodynamic heterogeneity of the gliomas. The reported approach is a new strategy to characterize the intra-tumor heterogeneity and physiological properties of gliomas.
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Affiliation(s)
- Bing Ji
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States of America
| | - Silun Wang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States of America
| | - Zhou Liu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States of America; Medical College of Nanchang University, Nanchang, Jiangxi, China
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA, United States of America
| | - Tianming Liu
- Department of Computer Sciences, University of Georgia, Athens, GA, United States of America
| | - Liya Wang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States of America; Medical College of Nanchang University, Nanchang, Jiangxi, China; Department of Radiology, The People's Hospital of Longhua, Shenzhen, Guangdong, China.
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States of America.
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10
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Reineberg AE, Gustavson DE, Benca C, Banich MT, Friedman NP. The Relationship Between Resting State Network Connectivity and Individual Differences in Executive Functions. Front Psychol 2018; 9:1600. [PMID: 30233455 PMCID: PMC6134071 DOI: 10.3389/fpsyg.2018.01600] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 08/13/2018] [Indexed: 12/13/2022] Open
Abstract
The brain is organized into a number of large networks based on shared function, for example, high-level cognitive functions (frontoparietal network), attentional capabilities (dorsal and ventral attention networks), and internal mentation (default network). The correlations of these networks during resting-state fMRI scans varies across individuals and is an indicator of individual differences in ability. Prior work shows higher cognitive functioning (as measured by working memory and attention tasks) is associated with stronger negative correlations between frontoparietal/attention and default networks, suggesting that increased ability may depend upon the diverging activation of networks with contrasting function. However, these prior studies lack specificity with regard to the higher-level cognitive functions involved, particularly with regards to separable components of executive function (EF). Here we decompose EF into three factors from the unity/diversity model of EFs: Common EF, Shifting-specific EF, and Updating-specific EF, measuring each via factor scores derived from a battery of behavioral tasks completed by 250 adult participants (age 28) at the time of a resting-state scan. We found the hypothesized segregated pattern only for Shifting-specific EF. Specifically, after accounting for one’s general EF ability (Common EF), individuals better able to fluidly switch between task sets have a stronger negative correlation between the ventral attention network and the default network. We also report non-predicted novel findings in that individuals with higher Shifting-specific abilities exhibited more positive connectivity between frontoparietal and visual networks, while those individuals with higher Common EF exhibited increased connectivity between sensory and default networks. Overall, these results reveal a new degree of specificity with regard to connectivity/EF relationships.
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Affiliation(s)
- Andrew E Reineberg
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States.,Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
| | - Daniel E Gustavson
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Chelsie Benca
- Department of Psychology, Emory University, Atlanta, GA, United States
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States.,Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States.,Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
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11
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Poser BA, Setsompop K. Pulse sequences and parallel imaging for high spatiotemporal resolution MRI at ultra-high field. Neuroimage 2018; 168:101-118. [PMID: 28392492 PMCID: PMC5630499 DOI: 10.1016/j.neuroimage.2017.04.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 04/01/2017] [Accepted: 04/03/2017] [Indexed: 12/18/2022] Open
Abstract
The SNR and CNR benefits of ultra-high field (UHF) have helped push the envelope of achievable spatial resolution in MRI. For applications based on susceptibility contrast where there is a large CNR gain, high quality sub-millimeter resolution imaging is now being routinely performed, particularly in fMRI and phase imaging/QSM. This has enabled the study of structure and function of very fine-scale structures in the brain. UHF has also helped push the spatial resolution of many other MRI applications as will be outlined in this review. However, this push in resolution comes at a cost of a large encoding burden leading to very lengthy scans. Developments in parallel imaging with controlled aliasing and the move away from 2D slice-by-slice imaging to much more SNR-efficient simultaneous multi-slice (SMS) and 3D acquisitions have helped address this issue. In particular, these developments have revolutionized the efficiency of UHF MRI to enable high spatiotemporal resolution imaging at an order of magnitude faster acquisition. In addition to describing the main approaches to these techniques, this review will also outline important key practical considerations in using these methods in practice. Furthermore, new RF pulse design to tackle the B1+ and SAR issues of UHF and the increased SAR and power requirement of SMS RF pulses will also be touched upon. Finally, an outlook into new developments of smart encoding in more dimensions, particularly through using better temporal/across-contrast encoding and reconstruction will be described. Just as controlled aliasing fully exploits spatial encoding in parallel imaging to provide large multiplicative gains in accelerations, the complimentary use of these new approaches in temporal and across-contrast encoding are expected to provide exciting opportunities for further large gains in efficiency to further push the spatiotemporal resolution of MRI.
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Affiliation(s)
- Benedikt A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
| | - Kawin Setsompop
- Department of Radiology, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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12
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Bukhari Q, Schroeter A, Cole DM, Rudin M. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions. Front Neural Circuits 2017; 11:5. [PMID: 28217085 PMCID: PMC5289996 DOI: 10.3389/fncir.2017.00005] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 01/16/2017] [Indexed: 01/29/2023] Open
Abstract
fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under specific physiological and pathological conditions.
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Affiliation(s)
- Qasim Bukhari
- Institute of Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Aileen Schroeter
- Institute of Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - David M Cole
- Institute of Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of PsychiatryZurich, Switzerland
| | - Markus Rudin
- Institute of Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Institute of Pharmacology and Toxicology, University of ZurichZurich, Switzerland
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13
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Khalili-Mahani N, Rombouts SARB, van Osch MJP, Duff EP, Carbonell F, Nickerson LD, Becerra L, Dahan A, Evans AC, Soucy JP, Wise R, Zijdenbos AP, van Gerven JM. Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry. Hum Brain Mapp 2017; 38:2276-2325. [PMID: 28145075 DOI: 10.1002/hbm.23516] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 11/21/2016] [Accepted: 01/04/2017] [Indexed: 12/11/2022] Open
Abstract
A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,PERFORM Centre, Concordia University, Montreal, Canada
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | | | - Eugene P Duff
- Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Oxford Centre for Functional MRI of the Brain, Oxford University, Oxford, United Kingdom
| | | | - Lisa D Nickerson
- McLean Hospital, Belmont, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Lino Becerra
- Center for Pain and the Brain, Harvard Medical School & Boston Children's Hospital, Boston, Massachusetts
| | - Albert Dahan
- Department of Anesthesiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Paul Soucy
- PERFORM Centre, Concordia University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Richard Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Alex P Zijdenbos
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,Biospective Inc, Montreal, Quebec, Canada
| | - Joop M van Gerven
- Centre for Human Drug Research, Leiden University Medical Centre, Leiden, The Netherlands
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14
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Ipsilateral Alteration of Resting State Activity Suggests That Cortical Dysfunction Contributes to the Pathogenesis of Cluster Headache. Brain Topogr 2016; 30:281-289. [DOI: 10.1007/s10548-016-0535-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 10/25/2016] [Indexed: 12/29/2022]
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15
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Muetzel RL, Blanken LME, Thijssen S, van der Lugt A, Jaddoe VWV, Verhulst FC, Tiemeier H, White T. Resting-state networks in 6-to-10 year old children. Hum Brain Mapp 2016; 37:4286-4300. [PMID: 27417416 DOI: 10.1002/hbm.23309] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 06/24/2016] [Accepted: 06/27/2016] [Indexed: 01/17/2023] Open
Abstract
Resting-state functional magnetic resonance imaging provides a non-invasive approach to the study of intrinsic functional brain networks. When applied to the study of brain development, most studies consist of relatively small samples that are not always representative of the general population. Descriptions of these networks in the general population offer important insight for clinical studies examining, for instance, psychopathology or neurological conditions. Thus our goal was to characterize resting-state networks in a large sample of children using independent component analysis (ICA). The study further aimed to describe the robustness of these networks by examining which networks occur frequently after repeated ICA. Resting-state networks were obtained from a sample of 536 6-to-10 year old children. Distributions of networks were built from repeated subsampling and group ICA analyses, and meta-ICA was used to construct a representative set of components. Within- and between-network properties were tested for age-related developmental associations using spatio-temporal regression. After repeated ICA, many networks were present over 95% of the time suggesting the components are highly reproducible. Some networks were less robust, and were observed less than 70% of the time. Age-related associations were also observed in a selection of networks, including the default-mode network, offering further evidence of development in these networks at an early age. ICA-derived resting-state networks appear to be robust, although some networks should further scrutinized if subjected to group-level statistical analyses, such as spatiotemporal regression. The final set of ICA-derived networks and an age-appropriate T1 -weighted template are made available to the neuroimaging community, https://www.nitrc.org/projects/genr. Hum Brain Mapp 37:4286-4300, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ryan L Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
| | - Laura M E Blanken
- Department of Child and Adolescent Psychiatry, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
| | - Sandra Thijssen
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
- School of Pedagogical and Educational Sciences, Erasmus University, Rotterdam, The Netherlands
- Center for Child and Family Studies, Leiden University, Leiden, the Netherlands
| | | | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Frank C Verhulst
- Department of Child and Adolescent Psychiatry, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
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16
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Frank LR, Galinsky VL. Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition. Neural Comput 2016; 28:1769-811. [PMID: 27391678 DOI: 10.1162/neco_a_00871] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The ability of functional magnetic resonance imaging (FMRI) to noninvasively measure fluctuations in brain activity in the absence of an applied stimulus offers the possibility of discerning functional networks in the resting state of the brain. However, the reconstruction of brain networks from these signal fluctuations poses a significant challenge because they are generally nonlinear and nongaussian and can overlap in both their spatial and temporal extent. Moreover, because there is no explicit input stimulus, there is no signal model with which to compare the brain responses. A variety of techniques have been devised to address this problem, but the predominant approaches are based on the presupposition of statistical properties of complex brain signal parameters, which are unprovable but facilitate the analysis. In this article, we address this problem with a new method, entropy field decomposition, for estimating structure within spatiotemporal data. This method is based on a general information field-theoretic formulation of Bayesian probability theory incorporating prior coupling information that allows the enumeration of the most probable parameter configurations without the need for unjustified statistical assumptions. This approach facilitates the construction of brain activation modes directly from the spatial-temporal correlation structure of the data. These modes and their associated spatial-temporal correlation structure can then be used to generate space-time activity probability trajectories, called functional connectivity pathways, which provide a characterization of functional brain networks.
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Affiliation(s)
- Lawrence R Frank
- Center for Scientific Computation in Imaging, and Department of Radiology, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A., and VA San Diego Healthcare System, San Diego, CA 92161, U.S.A.
| | - Vitaly L Galinsky
- Center for Scientific Computation in Imaging, and Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093, U.S.A.
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17
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Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis. Neuroinformatics 2016; 13:277-95. [PMID: 25501275 DOI: 10.1007/s12021-014-9241-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer's Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19 % and the sensitivity of 0.9167.
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18
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Bey K, Montag C, Reuter M, Weber B, Markett S. Susceptibility to everyday cognitive failure is reflected in functional network interactions in the resting brain. Neuroimage 2015. [PMID: 26210814 DOI: 10.1016/j.neuroimage.2015.07.041] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
The proneness to minor errors and slips in everyday life as assessed by the Cognitive Failures Questionnaire (CFQ) constitutes a trait characteristic and is reflected in stable features of brain structure and function. It is unclear, however, how dynamic interactions of large-scale brain networks contribute to this disposition. To address this question, we performed a high model order independent component analysis (ICA) with subsequent dual regression on resting-state fMRI data from 71 subjects to extract temporal time courses describing the dynamics of 17 resting-state networks (RSN). Dynamic network interactions between all 17 RSN were assessed by linear correlations between networks' time courses. On this basis, we investigated the relationship between subject-level RSN interactions and the susceptibility to everyday cognitive failure. We found that CFQ scores were significantly correlated with the interplay of the cingulo-opercular network (CON) and a posterior parietal network which unifies clusters in the posterior cingulate, precuneus, intraparietal lobules and middle temporal regions. Specifically, a higher positive functional connectivity between these two RSN was indicative of higher proneness to cognitive failure. Both the CON and posterior parietal network are implicated in cognitive functions, such as tonic alertness and executive control. Results indicate that proper checks and balances between the two networks are needed to protect against cognitive failure. Furthermore, we demonstrate that the study of temporal network dynamics in the resting state is a feasible tool to investigate individual differences in cognitive ability and performance.
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Affiliation(s)
- Katharina Bey
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany.
| | | | - Martin Reuter
- Department of Psychology, University of Bonn, Bonn, Germany; Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany; Department of Epileptology, University of Bonn, Bonn, Germany; Life & Brain Center, Bonn, Germany
| | - Sebastian Markett
- Department of Psychology, University of Bonn, Bonn, Germany; Center for Economics and Neuroscience, University of Bonn, Bonn, Germany.
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19
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Abstract
In blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI), current methods typically acquire ∼ 500,000 imaging voxels at each time point, and then use computer algorithms to reduce this data to the coefficients of a few hundred parcels or networks. This suggests that the amount of relevant information present in the fMRI signal is relatively small, and presents an opportunity to greatly improve the speed and signal to noise ratio (SNR) of the fMRI process. In this work, a theoretical framework is presented for calculating the coefficients of functional networks directly from highly undersampled fMRI data. Using predefined functional parcellations or networks and a compact k-space trajectory that samples data at optimal spatial scales, the problem of estimating network coefficients is reformulated to allow for direct least squares estimation, without Fourier encoding. By simulation, this approach is shown to allow for acceleration of the imaging process under ideal circumstances by nearly three orders of magnitude.
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Affiliation(s)
- Eric C Wong
- Departments of Radiology and Psychiatry, University of California , San Diego, La Jolla, California
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20
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Mark CI, Mazerolle EL, Chen JJ. Metabolic and vascular origins of the BOLD effect: Implications for imaging pathology and resting-state brain function. J Magn Reson Imaging 2015; 42:231-46. [DOI: 10.1002/jmri.24786] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 09/02/2014] [Indexed: 01/08/2023] Open
Affiliation(s)
- Clarisse I. Mark
- Centre for Neuroscience Studies; Queen's University; Kingston ON Canada
| | | | - J. Jean Chen
- Rotman Research Institute, Baycrest, University of Toronto; Toronto ON Canada
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21
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Dipasquale O, Griffanti L, Clerici M, Nemni R, Baselli G, Baglio F. High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer's Disease. Front Hum Neurosci 2015; 9:43. [PMID: 25691865 PMCID: PMC4315015 DOI: 10.3389/fnhum.2015.00043] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 01/16/2015] [Indexed: 12/21/2022] Open
Abstract
High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN.
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Affiliation(s)
- Ottavia Dipasquale
- IRCCS, Don Gnocchi Foundation , Milan , Italy ; Department of Electronics, Information and Bioengineering, Politecnico di Milano , Milan , Italy
| | - Ludovica Griffanti
- IRCCS, Don Gnocchi Foundation , Milan , Italy ; Department of Electronics, Information and Bioengineering, Politecnico di Milano , Milan , Italy ; FMRIB (Oxford University Centre for Functional MRI of the Brain) , Oxford , UK
| | - Mario Clerici
- IRCCS, Don Gnocchi Foundation , Milan , Italy ; Università degli Studi di Milano , Milan , Italy
| | - Raffaello Nemni
- IRCCS, Don Gnocchi Foundation , Milan , Italy ; Università degli Studi di Milano , Milan , Italy
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano , Milan , Italy
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22
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Li C, Tian L. Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence. AJNR Am J Neuroradiol 2014; 35:1150-6. [PMID: 24557703 DOI: 10.3174/ajnr.a3850] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE A number of studies have associated the adult intelligence quotient with the structure and function of the bilateral parieto-frontal networks, whereas the relationship between intelligence quotient and parieto-frontal network function has been found to be relatively weak in early childhood. Because both human intelligence and brain function undergo protracted development into adulthood, the purpose of the present study was to provide a better understanding of the development of the parieto-frontal network-intelligence quotient relationship. MATERIALS AND METHODS We performed independent component analysis of resting-state fMRI data of 84 children and 50 adolescents separately and then correlated full-scale intelligence quotient with the spatial maps of the bilateral parieto-frontal networks of each group. RESULTS In children, significant positive spatial-map versus intelligence quotient correlations were detected in the right angular gyrus and inferior frontal gyrus in the right parieto-frontal network, and no significant correlation was observed in the left parieto-frontal network. In adolescents, significant positive correlation was detected in the left inferior frontal gyrus in the left parieto-frontal network, and the correlations in the frontal pole in the 2 parieto-frontal networks were only marginally significant. CONCLUSIONS The present findings not only support the critical role of the parieto-frontal networks for intelligence but indicate that the relationship between intelligence quotient and the parieto-frontal network in the right hemisphere has been well established in late childhood, and that the relationship in the left hemisphere was also established in adolescence.
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Affiliation(s)
- C Li
- From the Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - L Tian
- From the Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.
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23
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Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, Zsoldos E, Ebmeier KP, Filippini N, Mackay CE, Moeller S, Xu J, Yacoub E, Baselli G, Ugurbil K, Miller KL, Smith SM. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage 2014; 95:232-47. [PMID: 24657355 DOI: 10.1016/j.neuroimage.2014.03.034] [Citation(s) in RCA: 830] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Revised: 02/14/2014] [Accepted: 03/12/2014] [Indexed: 01/22/2023] Open
Abstract
The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.
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Affiliation(s)
- Ludovica Griffanti
- FMRIB (Oxford University Centre for Functional MRI of the Brain), UK; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; MR Laboratory, IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy.
| | | | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Edward J Auerbach
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Gwenaëlle Douaud
- FMRIB (Oxford University Centre for Functional MRI of the Brain), UK
| | | | - Enikő Zsoldos
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Nicola Filippini
- FMRIB (Oxford University Centre for Functional MRI of the Brain), UK; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Clare E Mackay
- FMRIB (Oxford University Centre for Functional MRI of the Brain), UK; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Junqian Xu
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Karla L Miller
- FMRIB (Oxford University Centre for Functional MRI of the Brain), UK
| | - Stephen M Smith
- FMRIB (Oxford University Centre for Functional MRI of the Brain), UK
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