601
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Direct Cortical Recordings Suggest Temporal Order of Task-Evoked Responses in Human Dorsal Attention and Default Networks. J Neurosci 2018; 38:10305-10313. [PMID: 30315126 DOI: 10.1523/jneurosci.0079-18.2018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 09/09/2018] [Accepted: 09/25/2018] [Indexed: 11/21/2022] Open
Abstract
The past decade has seen a large number of neuroimaging studies focused on the anticorrelated functional relationship between the default mode network (DMN) and the dorsal attention network (DAN). Due principally to the low temporal resolution of functional neuroimaging modalities, the fast-neuronal dynamics across these networks remain poorly understood. Here we report novel human intracranial electrophysiology data from six neurosurgical patients (four males) with simultaneous coverage of well characterized nodes of the DMN and DAN. Subjects performed an arithmetic processing task, shown previously to evoke reliable deactivations (below baseline) in the DMN, and activations in the DAN. In this cohort, we show that DMN deactivations lag DAN activations by approximately 200 ms. Our findings suggest a clear temporal order of processing across the two networks during the current task and place the DMN further than the DAN in a plausible information-processing hierarchy.SIGNIFICANCE STATEMENT The human brain contains an intrinsic and strictly organized network architecture. Our understanding of the interplay across association networks has relied primarily on the slow fluctuations of the hemodynamic response, and as such it has lacked essential evidence regarding the temporal dynamics of activity across these networks. The current study presents evidence from high spatiotemporal methods showing that well studied areas of the default mode network display delayed task-induced activity relative to divergent responses in dorsal attention network nodes. This finding provides direct and critical evidence regarding the temporal chronology of neuronal events across opposing brain networks.
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602
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Tahedl M, Levine SM, Greenlee MW, Weissert R, Schwarzbach JV. Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions. Front Neurol 2018; 9:828. [PMID: 30364281 PMCID: PMC6193088 DOI: 10.3389/fneur.2018.00828] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 09/14/2018] [Indexed: 02/03/2023] Open
Abstract
Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.
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Affiliation(s)
- Marlene Tahedl
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Seth M. Levine
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Mark W. Greenlee
- Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Robert Weissert
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Jens V. Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
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603
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Hallquist MN, Hillary FG. Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Netw Neurosci 2018; 3:1-26. [PMID: 30793071 PMCID: PMC6326733 DOI: 10.1162/netn_a_00054] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 04/03/2018] [Indexed: 12/15/2022] Open
Abstract
Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
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Affiliation(s)
- Michael N. Hallquist
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
- Social Life and Engineering Sciences Imaging Center, University Park, PA, USA
| | - Frank G. Hillary
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
- Social Life and Engineering Sciences Imaging Center, University Park, PA, USA
- Department of Neurology, Hershey Medical Center, Hershey, PA, USA
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604
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Bachmann C, Jacobs HIL, Porta Mana P, Dillen K, Richter N, von Reutern B, Dronse J, Onur OA, Langen KJ, Fink GR, Kukolja J, Morrison A. On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease. Front Neurosci 2018; 12:528. [PMID: 30323734 PMCID: PMC6172342 DOI: 10.3389/fnins.2018.00528] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 07/13/2018] [Indexed: 01/28/2023] Open
Abstract
The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer.
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Affiliation(s)
- Claudia Bachmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
| | - Heidi I L Jacobs
- Faculty of Health, Medicine and Life Science, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands.,Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States.,Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - PierGianLuca Porta Mana
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Kim Dillen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Boris von Reutern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Karl-Josef Langen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-4), Jülich Research Centre, Jülich, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Juraj Kukolja
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany.,Department of Neurology, Helios University Hospital Wuppertal, Wuppertal, Germany
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.,Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
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605
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Huang X, Long Z, Lei X. Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study. J Neurosci Methods 2018; 311:351-359. [PMID: 30236777 DOI: 10.1016/j.jneumeth.2018.09.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 09/10/2018] [Accepted: 09/14/2018] [Indexed: 12/24/2022]
Abstract
BACKGROUND The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined. NEW METHODS We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects. RESULTS We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz). COMPARISON WITH EXISTING METHOD(S) Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity. CONCLUSIONS These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.
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Affiliation(s)
- Xiaoli Huang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China; Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China; Chongqing Collaborative Innovation Center for Brain Science, Chongqing, 400715, China.
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606
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Mohanty R, Sinha AM, Remsik AB, Dodd KC, Young BM, Jacobson T, McMillan M, Thoma J, Advani H, Nair VA, Kang TJ, Caldera K, Edwards DF, Williams JC, Prabhakaran V. Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning. Front Neurosci 2018; 12:624. [PMID: 30271318 PMCID: PMC6142044 DOI: 10.3389/fnins.2018.00624] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 08/20/2018] [Indexed: 01/05/2023] Open
Abstract
The primary goal of this work was to apply data-driven machine learning regression to assess if resting state functional connectivity (rs-FC) could estimate measures of behavioral domains in stroke subjects who completed brain-computer interface (BCI) intervention for motor rehabilitation. The study cohort consisted of 20 chronic-stage stroke subjects exhibiting persistent upper-extremity motor deficits who received the intervention using a closed-loop neurofeedback BCI device. Over the course of this intervention, resting state functional MRI scans were collected at four distinct time points: namely, pre-intervention, mid-intervention, post-intervention and 1-month after completion of intervention. Behavioral assessments were administered outside the scanner at each time-point to collect objective measures such as the Action Research Arm Test, Nine-Hole Peg Test, and Barthel Index as well as subjective measures including the Stroke Impact Scale. The present analysis focused on neuroplasticity and behavioral outcomes measured across pre-intervention, post-intervention and 1-month post-intervention to study immediate and carry-over effects. Rs-FC, changes in rs-FC within the motor network and the behavioral measures at preceding stages were used as input features and behavioral measures and associated changes at succeeding stages were used as outcomes for machine-learning-based support vector regression (SVR) models. Potential clinical confounding factors such as age, gender, lesion hemisphere, and stroke severity were included as additional features in each of the regression models. Sequential forward feature selection procedure narrowed the search for important correlates. Behavioral outcomes at preceding time-points outperformed rs-FC-based correlates. Rs-FC and changes associated with bilateral primary motor areas were found to be important correlates of across several behavioral outcomes and were stable upon inclusion of clinical variables as well. NIH Stroke Scale and motor impairment severity were the most influential clinical variables. Comparatively, linear SVR models aided in evaluation of contribution of individual correlates and seed regions while non-linear SVR models achieved higher performance in prediction of behavioral outcomes.
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Affiliation(s)
- Rosaleena Mohanty
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Electrical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Anita M Sinha
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Alexander B Remsik
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Keith C Dodd
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany M Young
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Tyler Jacobson
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Matthew McMillan
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Jaclyn Thoma
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Hemali Advani
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Theresa J Kang
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Kristin Caldera
- Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison, Madison, WI, United States
| | - Dorothy F Edwards
- Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Justin C Williams
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
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607
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Silva JPS, Mônaco LDM, Paschoal AM, Oliveira ÍAFD, Leoni RF. Effects of global signal regression and subtraction methods on resting-state functional connectivity using arterial spin labeling data. Magn Reson Imaging 2018; 51:151-157. [DOI: 10.1016/j.mri.2018.05.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/04/2018] [Accepted: 05/14/2018] [Indexed: 11/28/2022]
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608
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Anderson KL, Anderson JS, Palande S, Wang B. Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains. CONNECTOMICS IN NEUROIMAGING : SECOND INTERNATIONAL WORKSHOP, CNI 2018, HELD IN CONJUNCTION WITH MICCAI 2018, GRANADA, SPAIN, SEPTEMBER 20, 2018 : PROCEEDINGS. CNI (WORKSHOP) (2ND : 2018 : GRANADA, SPAIN) 2018; 11083:67-77. [PMID: 32715304 PMCID: PMC7380262 DOI: 10.1007/978-3-030-00755-3_8] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The functional architecture of the brain can be described as a dynamical system where components interact in flexible ways, constrained by physical connections between regions. Using correlation, either in time or in space, as an abstraction of functional connectivity, we analyzed resting state fMRI data from 1003 subjects. We compared several data preprocessing strategies and found that independent component-based nuisance regression outperformed other strategies, with the poorest reproducibility in strategies that include global signal regression. We also found that temporal vs. spatial functional connectivity can encode different aspects of cognition and personality. Topological analyses using persistent homology show that persistence barcodes are significantly correlated to individual differences in cognition and personality, with high reproducibility. Topological data analyses, including approaches to model connectivity in the time domain, are promising tools for representing high-level aspects of cognition, development, and neuropathology.
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609
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Morgan SE, White SR, Bullmore ET, Vértes PE. A Network Neuroscience Approach to Typical and Atypical Brain Development. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:754-766. [PMID: 29703679 PMCID: PMC6986924 DOI: 10.1016/j.bpsc.2018.03.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 02/21/2018] [Accepted: 03/01/2018] [Indexed: 12/15/2022]
Abstract
Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions.
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Affiliation(s)
- Sarah E Morgan
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
| | - Simon R White
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Edward T Bullmore
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, United Kingdom; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, United Kingdom
| | - Petra E Vértes
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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610
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Almgren H, Van de Steen F, Kühn S, Razi A, Friston K, Marinazzo D. Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study. Neuroimage 2018; 183:757-768. [PMID: 30165254 PMCID: PMC6215332 DOI: 10.1016/j.neuroimage.2018.08.053] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 08/21/2018] [Indexed: 02/08/2023] Open
Abstract
Dynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures – and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns. Across datasets, subjects’ effective connectivity patterns in the core default mode network showed hemispheric asymmetry. Differences in hemispheric asymmetry was found to be a major source of between-subject variability. In contrast, most subjects showed reliable within-subject hemispheric asymmetry. Differences in preprocessing methods had little effect on connectivity estimates. Bayesian model reduction increased the within- and between-subject consistency of connectivity patterns.
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Affiliation(s)
- Hannes Almgren
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium.
| | - Frederik Van de Steen
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Clinic and Polyclinic for Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia; The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
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611
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Xie Y, Weng J, Wang C, Xu T, Peng X, Chen F. The impact of long-term abacus training on modular properties of functional brain network. Neuroimage 2018; 183:811-817. [PMID: 30149141 DOI: 10.1016/j.neuroimage.2018.08.057] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 08/17/2018] [Accepted: 08/23/2018] [Indexed: 01/22/2023] Open
Abstract
Training induces cognitive and neural plasticity, and understanding of the neural mechanisms of training-induced brain plasticity has significant implications for improving children's academic achievement. Previous studies have indicated that training in abacus-based mental calculation (AMC) improves arithmetical capacities and results in brain plasticity within visuospatial brain regions. However, previous studies have reported alterations within distributed brain regions. Thus, it remains unclear whether and how AMC training influences the functional integration and separation between and/or within networks. The current study aimed to address these questions using graph theory, engaging 162 children, 90 of whom were given long-term AMC training. The AMC group exhibited greater local efficiency and intra-module connections within the visual network and less local efficiency and intra-module connections in the cingulo-opercular network (CON). Interestingly, in the AMC group, negative correlations were found between local efficiency and intra-module connections across the two networks. Furthermore, both network characteristics of the CON were negatively correlated with math ability in the AMC group. No such correlations were found in the control group. The current study delineated the enhanced neural mechanisms of visuospatial-related brain regions at an intermediate level and highlighted the intrinsic association between different brain ensembles in neural plasticity, thus furthering the understanding of the effects of AMC training on brain network reconfiguration.
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Affiliation(s)
- Ye Xie
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, 310027, PR China
| | - Jian Weng
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, 310027, PR China; Center of Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, PR China
| | - Chunjie Wang
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, 310027, PR China; State Key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou, PR China
| | - Tianyong Xu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, 310027, PR China
| | - Xiaogang Peng
- The First Hospital of Qiqihar, Qiqihar, Heilongjiang, PR China
| | - Feiyan Chen
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, 310027, PR China.
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612
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Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. Neuroimage 2018; 181:692-717. [PMID: 29753843 DOI: 10.1016/j.neuroimage.2018.04.076] [Citation(s) in RCA: 152] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 03/25/2018] [Accepted: 04/30/2018] [Indexed: 12/12/2022] Open
Abstract
Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.
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613
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Greene AS, Gao S, Scheinost D, Constable RT. Task-induced brain state manipulation improves prediction of individual traits. Nat Commun 2018; 9:2807. [PMID: 30022026 PMCID: PMC6052101 DOI: 10.1038/s41467-018-04920-3] [Citation(s) in RCA: 276] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 06/01/2018] [Indexed: 11/09/2022] Open
Abstract
Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.
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Affiliation(s)
- Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, 06520, CT, USA.
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, 06520, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, 06520, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, 06520, CT, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, 06520, CT, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, 06520, CT, USA
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614
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Dubois J, Galdi P, Han Y, Paul LK, Adolphs R. Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. PERSONALITY NEUROSCIENCE 2018; 1:e6. [PMID: 30225394 PMCID: PMC6138449 DOI: 10.1017/pen.2018.8] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/05/2018] [Indexed: 12/13/2022]
Abstract
Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the "Big Five", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors ("α" and "β") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.
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Affiliation(s)
- Julien Dubois
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paola Galdi
- Department of Management and Innovation Systems, University of Salerno, Fisciano, Salerno, Italy
- MRC Centre for Reproductive Health, University of Edinburgh, EH16 4TJ, UK
| | - Yanting Han
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Lynn K. Paul
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Ralph Adolphs
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Chen Neuroscience Institute, California Institute of Technology, Pasadena, CA, USA
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615
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Tommasin S, Mascali D, Moraschi M, Gili T, Hassan IE, Fratini M, DiNuzzo M, Wise RG, Mangia S, Macaluso E, Giove F. Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation. Neuroimage 2018; 179:570-581. [PMID: 29908935 DOI: 10.1016/j.neuroimage.2018.06.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 06/04/2018] [Indexed: 01/09/2023] Open
Abstract
Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation.
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Affiliation(s)
- Silvia Tommasin
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Dipartimento di Neuroscienze umane, Sapienza Università di Roma, Roma, Italy
| | - Daniele Mascali
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy
| | - Marta Moraschi
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy
| | - Tommaso Gili
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Fondazione Santa Lucia IRCCS, Roma, Italy
| | | | - Michela Fratini
- Fondazione Santa Lucia IRCCS, Roma, Italy; Istituto di Nanotecnologia, Consiglio Nazionale delle Ricerche, Roma, Italy
| | - Mauro DiNuzzo
- Center for Basic and Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Richard G Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Silvia Mangia
- Center for Magnetic Resonance Research, Dept. of Radiology, University of Minnesota, Minneapolis, USA
| | | | - Federico Giove
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Fondazione Santa Lucia IRCCS, Roma, Italy.
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616
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Belloy ME, Shah D, Abbas A, Kashyap A, Roßner S, Van der Linden A, Keilholz SD, Keliris GA, Verhoye M. Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice. Sci Rep 2018; 8:10024. [PMID: 29968786 PMCID: PMC6030071 DOI: 10.1038/s41598-018-28237-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 06/14/2018] [Indexed: 12/17/2022] Open
Abstract
Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool.
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Affiliation(s)
- Michaël E Belloy
- Department of Pharmaceutical, Veterinary and Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium.
- Department of Biomedical Engineering, Emory University, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA.
| | - Disha Shah
- Department of Pharmaceutical, Veterinary and Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
| | - Anzar Abbas
- Department of Neuroscience, Emory University, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA
| | - Amrit Kashyap
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA
| | - Steffen Roßner
- Paul Flechsig Institute for Brain Research, University of Leipzig, Liebigstraße 19. Haus C, 04103, Leipzig, Germany
| | - Annemie Van der Linden
- Department of Pharmaceutical, Veterinary and Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
| | - Shella D Keilholz
- Department of Biomedical Engineering, Emory University, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA
- Department of Neuroscience, Emory University, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1760 Haygood Dr. NE, Atlanta, GA, 30322, USA
| | - Georgios A Keliris
- Department of Pharmaceutical, Veterinary and Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
| | - Marleen Verhoye
- Department of Pharmaceutical, Veterinary and Biomedical Sciences, Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
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617
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Kim BH, Shin YB, Kyeong S, Lee SK, Kim JJ. Disrupted salience processing involved in motivational deficits for real-life activities in patients with schizophrenia. Schizophr Res 2018; 197:407-413. [PMID: 29395610 DOI: 10.1016/j.schres.2018.01.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 11/29/2017] [Accepted: 01/18/2018] [Indexed: 10/18/2022]
Abstract
Motivational deficits in patients with schizophrenia adversely affect various domains of daily living. This symptom in everyday life situations manifests in a complex behavioral pattern whose root cannot be simplified to an impaired reward-motivation scheme. This study aimed to identify impairment of the salience network that underlies motivational deficits seen in patients with schizophrenia in real-life situations. During the functional magnetic resonance imaging scan, 20 patients with schizophrenia and 20 normal controls performed a task mimicking real-life situations, in which an avatar proposed participation in a daily activity with either an intrinsic or extrinsic reward. Group and type-of-reward effects were evaluated with respect to brain activity. Further, psychophysiological interactions were analyzed for the dorsal anterior cingulate cortex (dACC) and insula, which are the key nodes of the salience network. The acceptance of the proposal was significantly higher for intrinsic than for extrinsic rewards in controls, whereas patients showed no difference. The imaging results showed a group effect in the dACC, right insula, thalamus, and lingual gyrus. The dACC showed negative contrast interaction with regions of the left dorsolateral prefrontal cortex, and the right insula showed positive contrast interaction with the occipital gyrus and precentral gyrus. These results suggest that patients exhibit no different participation behavior between activities with intrinsic and extrinsic rewards, which can be explained by the floor effect. Disrupted salience processing in schizophrenia including aberrant salience network and a disconnection of the salience and reward networks may account for the lack of motivation for daily activities.
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Affiliation(s)
- Byung-Hoon Kim
- Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yu-Bin Shin
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sunghyon Kyeong
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seon-Koo Lee
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea
| | - Jae-Jin Kim
- Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
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618
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Cui Z, Gong G. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 2018; 178:622-637. [PMID: 29870817 DOI: 10.1016/j.neuroimage.2018.06.001] [Citation(s) in RCA: 180] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/31/2018] [Accepted: 06/01/2018] [Indexed: 12/27/2022] Open
Abstract
Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations.
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Affiliation(s)
- Zaixu Cui
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
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619
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Dong G, Li H, Wang Y, Potenza MN. Individual differences in self-reported reward-approach tendencies relate to resting-state and reward-task-based fMRI measures. Int J Psychophysiol 2018; 128:31-39. [DOI: 10.1016/j.ijpsycho.2018.03.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 01/27/2018] [Accepted: 03/20/2018] [Indexed: 11/27/2022]
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620
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Ezaki T, Sakaki M, Watanabe T, Masuda N. Age-related changes in the ease of dynamical transitions in human brain activity. Hum Brain Mapp 2018; 39:2673-2688. [PMID: 29524289 PMCID: PMC6619404 DOI: 10.1002/hbm.24033] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Revised: 02/27/2018] [Accepted: 02/27/2018] [Indexed: 01/07/2023] Open
Abstract
Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural systems, the age-related decline in executive functions should be underpinned by alteration of large-scale neural dynamics. However, the effects of age on brain dynamics have not been firmly formulated. Here, we investigate such age-related changes in brain dynamics by applying "energy landscape analysis" to publicly available functional magnetic resonance imaging data from healthy younger and older human adults. We quantified the ease of dynamical transitions between different major patterns of brain activity, and estimated it for the default mode network (DMN) and the cingulo-opercular network (CON) separately. We found that the two age groups shared qualitatively the same trajectories of brain dynamics in both the DMN and CON. However, in both of networks, the ease of transitions was significantly smaller in the older than the younger group. Moreover, the ease of transitions was associated with the performance in executive function tasks in a doubly dissociated manner: for the younger adults, the ability of executive functions was mainly correlated with the ease of transitions in the CON, whereas that for the older adults was specifically associated with the ease of transitions in the DMN. These results provide direct biological evidence for age-related changes in macroscopic brain dynamics and suggest that such neural dynamics play key roles when individuals carry out cognitively demanding tasks.
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Affiliation(s)
- Takahiro Ezaki
- PRESTO, JST, 4‐1‐8 HonchoKawaguchiSaitamaJapan
- National Institute of Informatics, HitotsubashiChiyoda‐kuTokyoJapan
- Kawarabayashi Large Graph Project, ERARO, JST, c/o Global Research Center for Big Data Mathematics, NIIChiyoda‐kuTokyoJapan
| | - Michiko Sakaki
- School of Psychology and Clinical Language SciencesUniversity of Reading, Earley Gate, Whiteknights RoadReadingUnited Kingdom
- Research Institute, Kochi University of TechnologyKamiKochiJapan
| | - Takamitsu Watanabe
- Institute of Cognitive Neuroscience, University College London, 17 Queen SquareLondonWC1N 3AZUnited Kingdom
| | - Naoki Masuda
- Department of Engineering MathematicsUniversity of BristolCliftonBristolUnited Kingdom
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621
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Mattar MG, Thompson-Schill SL, Bassett DS. The network architecture of value learning. Netw Neurosci 2018; 2:128-149. [PMID: 30215030 PMCID: PMC6130435 DOI: 10.1162/netn_a_00021] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 07/03/2017] [Indexed: 01/11/2023] Open
Abstract
Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of four consecutive days. We show that connections between regions of the visual, frontal, and cingulate cortices become stronger as learning progresses, with some of these changes being specific to the type of feedback received during learning. These results demonstrate that functional networks dynamically track behavioral improvement in value judgments, and that interactions between network communities form predictive biomarkers of learning. Rational human behavior is the pursuit of actions that maximize expected reward. These rewards can be understood as stimulus-value contingencies, learned by experience throughout our lives. Various structures have been recognized to participate in these learning processes. Yet, an understanding of how these structures interact with one another and with other brain regions remains vastly unexplored. Here, we propose a novel analytical framework utilizing and extending techniques from the dynamic network neuroscience to ask “How do our brains change when we learn values?” We find that interactions between sensory and fronto-cingulate structures grow stronger as learning progresses, bringing together several isolated findings in the cognitive neuroscience of value-based behavior and extending our understanding of human learning in general.
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Affiliation(s)
- Marcelo G Mattar
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
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622
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Mohanty R, Sinha AM, Remsik AB, Dodd KC, Young BM, Jacobson T, McMillan M, Thoma J, Advani H, Nair VA, Kang TJ, Caldera K, Edwards DF, Williams JC, Prabhakaran V. Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity. Front Neurosci 2018; 12:353. [PMID: 29896082 PMCID: PMC5986965 DOI: 10.3389/fnins.2018.00353] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 05/07/2018] [Indexed: 01/19/2023] Open
Abstract
Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.
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Affiliation(s)
- Rosaleena Mohanty
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Electrical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Anita M Sinha
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Alexander B Remsik
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Keith C Dodd
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany M Young
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Tyler Jacobson
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Deparment of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Matthew McMillan
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Jaclyn Thoma
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Hemali Advani
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Theresa J Kang
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Kristin Caldera
- Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison, Madison, WI, United States
| | - Dorothy F Edwards
- Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Justin C Williams
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
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623
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Dynamic functional connectivity and its behavioral correlates beyond vigilance. Neuroimage 2018; 177:1-10. [PMID: 29704612 DOI: 10.1016/j.neuroimage.2018.04.049] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Accepted: 04/21/2018] [Indexed: 11/22/2022] Open
Abstract
Fluctuations in resting-state functional connectivity and global signal have been found to correspond with vigilance fluctuations, but their associations with other behavioral measures are unclear. We evaluated 52 healthy adolescents after a week of adequate sleep followed by five nights of sleep restriction to unmask inter-individual differences in cognition and mood. Resting state scans obtained at baseline only, analyzed using sliding window analysis, consistently yielded two polar dynamic functional connectivity states (DCSs) corresponding to previously reported 'low arousal' and 'high arousal' states. We found that the relative temporal preponderance of two dynamic connectivity states (DCS) in well-rested participants, indexed by a median split of participants, based on the relative time spent in these DCS, revealed highly significant group differences in vigilance at baseline and its decline following multiple nights of sleep restriction. Group differences in processing speed and working memory following manipulation but not at baseline suggest utility of DCS in predicting cognitive vulnerabilities unmasked by a stressor like sleep restriction. DCS temporal predominance was uninformative about mood and sleepiness speaking to specificity in its behavioral predictions. Global signal fluctuation provided information confined to vigilance. This appears to be related to head motion, which increases during periods of low arousal.
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624
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Liu Z, Zhang J, Zhang K, Zhang J, Li X, Cheng W, Li M, Zhao L, Deng W, Guo W, Ma X, Wang Q, Matthews PM, Feng J, Li T. Distinguishable brain networks relate disease susceptibility to symptom expression in schizophrenia. Hum Brain Mapp 2018; 39:3503-3515. [PMID: 29691943 DOI: 10.1002/hbm.24190] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 03/18/2018] [Accepted: 04/06/2018] [Indexed: 02/05/2023] Open
Abstract
Disease association studies have characterized altered resting-state functional connectivities describing schizophrenia, but failed to model symptom expression well. We developed a model that could account for symptom severity and meanwhile relate this to disease-related functional pathology. We correlated BOLD signal across brain regions and tested separately for associations with disease (disease edges) and with symptom severity (symptom edges) in a prediction-based scheme. We then integrated them in an "edge bi-color" graph, and adopted mediation analysis to test for causality between the disease and symptom networks and symptom scores. For first-episode schizophrenics (FES, 161 drug-naïve patients and 150 controls), the disease network (with inferior frontal gyrus being the hub) and the symptom-network (posterior occipital-parietal cortex being the hub) were found to overlap in the temporal lobe. For chronic schizophrenis (CS, 69 medicated patients and 62 controls), disease network was dominated by thalamocortical connectivities, and overlapped with symptom network in the middle frontal gyrus. We found that symptom network mediates the relationship between disease network and symptom scores in FEP, but was unable to define a relationship between them for the smaller CS population. Our results suggest that the disease network distinguishing core functional pathology in resting-state brain may be responsible for symptom expression in FES through a wider brain network associated with core symptoms. We hypothesize that top-down control from heteromodal prefrontal cortex to posterior transmodal cortex contributes to positive symptoms of schizophrenia. Our work also suggests differences in mechanisms of symptom expression between FES and CS, highlighting a need to distinguish between these groups.
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Affiliation(s)
- Zhaowen Liu
- School of Computer Science and Technology, Xidian University, Xi'an, Shannxi, 710071, People's Republic of China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China.,Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, People's Republic of China
| | - Kai Zhang
- Department of Computer and Information Sciences, Temple University, 1801 North Broad Street, Philadelphia, Pennsylvania, 1912
| | - Junying Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, Shannxi, 710071, People's Republic of China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wanjun Guo
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Paul M Matthews
- Division of Brain Sciences, Department of Medicine and Centre for Neurotechnology, Imperial College, London, W12 0NN, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China.,Shanghai Center for Mathematical Sciences, Shanghai, 200433, People's Republic of China.,Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.,Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, People's Republic of China.,Zhongshan Hosipital, Fudan University, Shanghai, 200433, People's Republic of China
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
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625
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van Timmeren T, Zhutovsky P, van Holst RJ, Goudriaan AE. Connectivity networks in gambling disorder: a resting-state fMRI study. INTERNATIONAL GAMBLING STUDIES 2018. [DOI: 10.1080/14459795.2018.1449884] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Tim van Timmeren
- Department of Psychiatry, Academic Medical Center, University of Amsterdam , Amsterdam, The Netherlands
- Amsterdam Institute for Addiction Research (AIAR) , Amsterdam, The Netherlands
| | - Paul Zhutovsky
- Department of Psychiatry, Academic Medical Center, University of Amsterdam , Amsterdam, The Netherlands
| | - Ruth J. van Holst
- Department of Psychiatry, Academic Medical Center, University of Amsterdam , Amsterdam, The Netherlands
- Amsterdam Institute for Addiction Research (AIAR) , Amsterdam, The Netherlands
- Donders Institute for Cognition, Brain and Behaviour, Radboud University , Nijmegen, The Netherlands
| | - Anna E. Goudriaan
- Department of Psychiatry, Academic Medical Center, University of Amsterdam , Amsterdam, The Netherlands
- Amsterdam Institute for Addiction Research (AIAR) , Amsterdam, The Netherlands
- Arkin , Amsterdam, The Netherlands
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626
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Philip NS, Barredo J, Aiken E, Carpenter LL. Neuroimaging Mechanisms of Therapeutic Transcranial Magnetic Stimulation for Major Depressive Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:211-222. [PMID: 29486862 PMCID: PMC5856477 DOI: 10.1016/j.bpsc.2017.10.007] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/06/2017] [Accepted: 10/29/2017] [Indexed: 01/11/2023]
Abstract
Research into therapeutic transcranial magnetic stimulation (TMS) for major depression has dramatically increased in the last decade. Understanding the mechanism of action of TMS is crucial to improve efficacy and develop the next generation of therapeutic stimulation. Early imaging research provided initial data supportive of widely held assumptions about hypothesized inhibitory or excitatory consequences of stimulation. Early work also indicated that while TMS modulated brain activity under the stimulation site, effects at deeper regions, in particular, the subgenual anterior cingulate cortex, were associated with clinical improvement. Concordant with earlier findings, functional connectivity studies also demonstrated that clinical improvements were related to changes distal, rather than proximal, to the site of stimulation. Moreover, recent work suggests that TMS modulates and potentially normalizes functional relationships between neural networks. An important observation that emerged from this review is that similar patterns of connectivity changes are observed across studies regardless of TMS parameters. Though promising, we stress that these imaging findings must be evaluated cautiously given the widespread reliance on modest sample sizes and little implementation of statistical validation. Additional limitations included use of imaging before and after a course of TMS, which provided little insight into changes that might occur during the weeks of stimulation. Furthermore, as studies to date have focused on depression, it is unclear whether our observations were related to mechanisms of action of TMS for depression or represented broader patterns of functional brain changes associated with clinical improvement.
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Affiliation(s)
- Noah S Philip
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island; Mood Disorders Research Program and Neuromodulation Research Facility, Butler Hospital, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island.
| | - Jennifer Barredo
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island
| | - Emily Aiken
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island
| | - Linda L Carpenter
- Mood Disorders Research Program and Neuromodulation Research Facility, Butler Hospital, Providence, Rhode Island; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island
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627
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Disrupted functional connectivity of the amygdala is associated with depressive mood in type 2 diabetes patients. J Affect Disord 2018; 228:207-215. [PMID: 29272791 DOI: 10.1016/j.jad.2017.12.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 11/18/2017] [Accepted: 12/06/2017] [Indexed: 02/03/2023]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) and mood disorders share pathophysiological commonalities in the central nervous system. The purpose of this study was to investigate the alterations in amygdala-based emotional processing circuits in T2DM patients with depressive mood using resting-state functional magnetic resonance imaging (rs-fMRI). METHODS T2DM patients with depressive mood (n = 25), T2DM patients without depressive mood (n = 28) and matched healthy controls (n = 25) underwent neuropsychological testing and rs-fMRI scanning. A seed-based correlation analysis was conducted to reveal the altered functional connectivity (FC) of the amygdala. The bilateral amygdala FC was compared among the three groups. Pearson correlation analyses were performed in a voxel-wise manner to investigate the relationship between amygdala FC and the clinical characteristics. RESULTS The depressed T2DM patients exhibited the worst performance on the neuropsychological tests among the three groups. Compared to the non-depressed T2DM patients, the depressed T2DM patients showed decreased amygdala FC in the cingulate cortex, inferior frontal gyrus, fusiform gyrus, and precentral gyrus. Moreover, the amygdala FC in the cingulate cortex was associated with the Self-Rating Depression Scale (SDS) score in the T2DM patients. LIMITATIONS Cross-sectional design. CONCLUSIONS The current study revealed the cognitive changes and alterations in the amygdala-cingulate functional disconnections in T2DM patients with depressive mood, which will advance the understanding of the neural mechanisms underlying depression in T2DM patients.
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628
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Johnson FK, Delpech JC, Thompson GJ, Wei L, Hao J, Herman P, Hyder F, Kaffman A. Amygdala hyper-connectivity in a mouse model of unpredictable early life stress. Transl Psychiatry 2018; 8:49. [PMID: 29463821 PMCID: PMC5820270 DOI: 10.1038/s41398-018-0092-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 12/06/2017] [Accepted: 12/13/2017] [Indexed: 01/09/2023] Open
Abstract
Childhood maltreatment is associated with a wide range of psychopathologies including anxiety that emerge in childhood and in many cases persist in adulthood. Increased amygdala activation in response to threat and abnormal amygdala connectivity with frontolimbic brain regions, such as the hippocampus and the prefrontal cortex, are some of the most consistent findings seen in individuals exposed to childhood maltreatment. The underlying mechanisms responsible for these changes are difficult to study in humans but can be elucidated using animal models of early-life stress. Such studies are especially powerful in the mouse where precise control of the genetic background and the stress paradigm can be coupled with resting-state fMRI (rsfMRI) to map abnormal connectivity in circuits that regulate anxiety. To address this issue we first compared the effects of two models of early-life stress, limited bedding (LB) and unpredictable postnatal stress (UPS), on anxiety-like behavior in juvenile and adult mice. We found that UPS, but not LB, causes a robust increase in anxiety in juvenile and adult male mice. Next, we used rsfMRI to compare frontolimbic connectivity in control and UPS adult male mice. We found increased amygdala-prefrontal cortex and amygdala-hippocampus connectivity in UPS. The strength of the amygdala-hippocampal and amygdala-prefrontal cortex connectivity was highly correlated with anxiety-like behavior in the open-field test and elevated plus maze. These findings are the first to link hyperconnectivity in frontolimbic circuits and increased anxiety in a mouse model of early-life stress, allowing for more mechanistic understanding of parallel findings in humans.
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Affiliation(s)
- Frances K Johnson
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
| | - Jean-Christophe Delpech
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
- Department of Newborn Medicine, Boston Children's Hospital, Harvard medical school, Boston, MA, 02115, USA
| | - Garth J Thompson
- Department of Radiology & Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Ren Building, Room B204, Zhangjiang, Pudong, Shanghai, 201210, China
| | - Lan Wei
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
| | - Jin Hao
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA
| | - Peter Herman
- Department of Radiology & Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Department of Radiology & Biomedical Imaging and Magnetic Resonance Research Center, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06519, USA
| | - Arie Kaffman
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 901, New Haven, CT, 06511, USA.
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629
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Li Z, Vidorreta M, Katchmar N, Alsop DC, Wolf DH, Detre JA. Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI. Neuroimage 2018; 173:165-175. [PMID: 29454933 PMCID: PMC5957091 DOI: 10.1016/j.neuroimage.2018.02.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 02/06/2018] [Accepted: 02/14/2018] [Indexed: 12/22/2022] Open
Abstract
Resting state fMRI (rs-fMRI) provides imaging biomarkers of task-independent brain function that can be associated with clinical variables or modulated by interventions such as behavioral training or pharmacological manipulations. These biomarkers include time-averaged regional brain function as manifested by regional cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion MRI and correlated temporal fluctuations of function across brain networks with either ASL or blood oxygenation level dependent (BOLD) fMRI. Resting-state studies are typically carried out using just one of several prescribed state conditions such as eyes closed (EC), eyes open (EO), or visual fixation on a cross-hair (FIX), which may affect the reliability and specificity of rs-fMRI. In this study, we collected test-retest ASL MRI data during 4 resting-state task conditions: EC, EO, FIX and PVT (low-frequency psychomotor vigilance task), and examined the effects of these task conditions on reliability and reproducibility as well as trait specificity of regional brain function. We also acquired resting-state BOLD fMRI under FIX and compared the network connectivity reliabilities between the four ASL conditions and the BOLD FIX condition. For resting-state ASL data, EC provided the highest CBF reliability, reproducibility, trait specificity, and network connectivity reliability, followed by EO, while FIX was lowest on all of these measures. PVT demonstrated lower CBF reliability, reproducibility and trait specificity than EO and EC. Overall network connectivity reliability was comparable between ASL and BOLD. Our findings confirm ASL CBF as a reliable, stable, and consistent measure of resting-state regional brain function and support the use of EC or EO over FIX and PVT as the resting-state condition.
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Affiliation(s)
- Zhengjun Li
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, USA
| | - Marta Vidorreta
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, USA
| | - Natalie Katchmar
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, USA
| | - David C Alsop
- Department of Radiology, Beth Israel Deaconess Medical Center, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, USA; Department of Radiology, University of Pennsylvania Perelman School of Medicine, USA.
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630
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Du H, Xia M, Zhao K, Liao X, Yang H, Wang Y, He Y. PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data. Hum Brain Mapp 2018; 39:1869-1885. [PMID: 29417688 DOI: 10.1002/hbm.23996] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 12/12/2017] [Accepted: 01/29/2018] [Indexed: 11/10/2022] Open
Abstract
The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.
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Affiliation(s)
- Haixiao Du
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Kang Zhao
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huazhong Yang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yu Wang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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631
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Zhou Y, Friston KJ, Zeidman P, Chen J, Li S, Razi A. The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults. Cereb Cortex 2018; 28:726-737. [PMID: 29161362 PMCID: PMC5929108 DOI: 10.1093/cercor/bhx307] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 10/03/2017] [Accepted: 10/26/2017] [Indexed: 12/26/2022] Open
Abstract
An important characteristic of spontaneous brain activity is the anticorrelation between the core default network (cDN) and the dorsal attention network (DAN) and the salience network (SN). This anticorrelation may constitute a key aspect of functional anatomy and is implicated in several brain disorders. We used dynamic causal modeling to assess the hypothesis that a causal hierarchy underlies this anticorrelation structure, using resting-state fMRI of healthy adolescent and young adults (N = 404). Our analysis revealed an asymmetric effective connectivity, such that the regions in the SN and DAN exerted an inhibitory influence on the cDN regions; whereas the cDN exerted an excitatory influence on the SN and DAN regions. The relative strength of efferent versus afferent connections places the SN at the apex of the hierarchy, suggesting that the SN modulates anticorrelated networks with descending hierarchical connections. In short, this study of directed neuronal coupling reveals a causal hierarchical architecture that generates or orchestrates anticorrelation of brain activity. These new findings shed light on functional integration of intrinsic brain networks at rest and speak to future dynamic causal modeling studies of large-scale networks.
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Affiliation(s)
- Yuan Zhou
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Peter Zeidman
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
| | - Jie Chen
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
| | - Shu Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Adeel Razi
- The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
- Monash Biomedical Imaging and Monash Institute of Cognitive & Clinical Neurosciences, Monash University, Clayton 3800, Australia
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan
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632
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Shah P, Bassett DS, Wisse LE, Detre JA, Stein JM, Yushkevich PA, Shinohara RT, Pluta JB, Valenciano E, Daffner M, Wolk DA, Elliott MA, Litt B, Davis KA, Das SR. Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI. Hum Brain Mapp 2018; 39:851-865. [PMID: 29159960 PMCID: PMC5764800 DOI: 10.1002/hbm.23887] [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: 06/01/2017] [Revised: 10/31/2017] [Accepted: 11/06/2017] [Indexed: 12/13/2022] Open
Abstract
Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL-focused T2-weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi-atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter-hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra-hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease-based research.
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Affiliation(s)
- Preya Shah
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of Electrical & Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Laura E.M. Wisse
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - John A. Detre
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Center for Functional Neuroimaging, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Joel M. Stein
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Paul A. Yushkevich
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Russell T. Shinohara
- Department of BiostatisticsEpidemiology and Informatics, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - John B. Pluta
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Elijah Valenciano
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Molly Daffner
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Penn Memory Center, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Penn Memory Center, University of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Mark A. Elliott
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Brian Litt
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Kathryn A. Davis
- Center for Neuroengineering and TherapeuticsUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
| | - Sandhitsu R. Das
- Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvania19104
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633
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The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations. Neuron 2018; 97:940-952.e4. [PMID: 29398365 DOI: 10.1016/j.neuron.2018.01.032] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Revised: 12/10/2017] [Accepted: 01/12/2018] [Indexed: 01/06/2023]
Abstract
Patterns of spontaneous brain activity, typically measured in humans at rest with fMRI, are used routinely to assess the brain's functional organization. The mechanisms that generate and coordinate the underlying neural fluctuations are largely unknown. Here we investigate the hypothesis that the nucleus basalis of Meynert (NBM), the principal source of widespread cholinergic and GABAergic projections to the cortex, contributes critically to such activity. We reversibly inactivated two distinct sites of the NBM in macaques while measuring fMRI activity across the brain. We found that inactivation led to strong, regionalized suppression of shared or "global" signal components of cortical fluctuations ipsilateral to the injection. At the same time, the commonly studied resting-state networks retained their spatial structure under this suppression. The results indicate that the NBM contributes selectively to the global component of functional connectivity but plays little if any role in the specific correlations that define resting-state networks.
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634
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Yang FC, Chou KH, Hsu AL, Fuh JL, Lirng JF, Kao HW, Lin CP, Wang SJ. Altered Brain Functional Connectome in Migraine with and without Restless Legs Syndrome: A Resting-State Functional MRI Study. Front Neurol 2018; 9:25. [PMID: 29441041 PMCID: PMC5797592 DOI: 10.3389/fneur.2018.00025] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 01/12/2018] [Indexed: 01/03/2023] Open
Abstract
Background Migraine is frequently comorbid with restless legs syndrome (RLS), both displaying functional connectivity (FC) alterations in multiple brain networks, although the neurological basis of this association is unknown. Methods We performed resting-state functional magnetic resonance imaging and network-wise analysis of FC in migraine patients with and without RLS and healthy controls (CRL). Network-based statistics (NBS) and composite FC matrix analyses were performed to identify the patterns of FC changes. Correlation analyses were performed to identify associations between alterations in FC and clinical profiles. Results NBS results revealed that both migraine patients with and without RLS exhibited lower FC than CRL in the dorsal attention, salience, default mode, cingulo-opercular, visual, frontoparietal, auditory, and sensory/somatomotor networks. Further composite FC matrix analyses revealed differences in FC of the salience, default mode to subcortical and frontoparietal, auditory to salience, and memory retrieval networks between migraine patients with and without RLS. There was a trend toward a negative association between RLS severity and cross-network abnormalities in the default mode to subcortical network. Discussion Migraine patients with and without RLS exhibit disruptions of brain FC. Such findings suggest that these disorders are associated with differential neuropathological mechanisms and may aid in the future development of neuroimaging-driven biomarkers for these conditions.
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Affiliation(s)
- Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Ai-Ling Hsu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan.,Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Jiing-Feng Lirng
- Department of Radiology, National Yang-Ming University, Taipei, Taiwan.,Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hung-Wen Kao
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Shuu-Jiun Wang
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan.,Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
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635
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Chen Y, Zhao X, Zhang X, Liu Y, Zhou P, Ni H, Ma J, Ming D. Age-related early/late variations of functional connectivity across the human lifespan. Neuroradiology 2018; 60:403-412. [PMID: 29383434 DOI: 10.1007/s00234-017-1973-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 12/28/2017] [Indexed: 12/18/2022]
Abstract
PURPOSE Many questions remain regarding how the brain develops, matures, and ages across the lifespan. The functional connectivity networks in the resting-state brain can reflect many of the characteristic changes in the brain that are associated with increasing age. Functional connectivity has been shown to be time-dependent over the course of a lifespan and even over the course of minutes. The lifespan strategies of all cognitive networks and how dynamic functional connectivity is associated with age are unclear. METHODS In this paper, studies employing both linear and quadratic models to define new specific lifespan strategies, including early/late increase/decrease models, were conducted to explore the lifespan functional changes. A large data sample was retrieved from the publicly available data from the Nathan Kline Institute (N = 149 and ages 9-85). Both static and dynamic functional connectivity indexes were calculated including the static functional connectivity, the mean of the dynamic functional connectivity and variations in dynamic functional connectivity. RESULTS The between-network connectivity results revealed early increases in the default-mode (DF) and cingulo-opercular network (CO)-associated network connectivities and a late increase in the fronto-parietal (FP)-associated network connectivity. These results depicted various lifespan strategies for different development stages and different cognitive networks across the lifespan. Additionally, the static FC and mean dynamic FC exhibited consistent results, and their variation exhibited a constant decrease with age across the entire age range. CONCLUSION These results (FDR-corrected p value < 0.05) suggest that the early/late variations in lifespan strategies could reflect an association between varied and complex circumstances and brain development.
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Affiliation(s)
- Yuanyuan Chen
- College of Microelectronics, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xin Zhao
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Xiong Zhang
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Ya'nan Liu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Peng Zhou
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Hongyan Ni
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Jianguo Ma
- College of Microelectronics, Tianjin University, Tianjin, China
| | - Dong Ming
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China. .,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
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636
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Li Z, Chen R, Guan M, Wang E, Qian T, Zhao C, Zou Z, Beck T, Shi D, Wang M, Zhang H, Li Y. Disrupted brain network topology in chronic insomnia disorder: A resting-state fMRI study. NEUROIMAGE-CLINICAL 2018; 18:178-185. [PMID: 29387533 PMCID: PMC5789127 DOI: 10.1016/j.nicl.2018.01.012] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 12/26/2017] [Accepted: 01/15/2018] [Indexed: 01/01/2023]
Abstract
This study investigated the topological characteristics of brain functional networks in chronic insomnia disorder (CID) patients. The resting-state functional magnetic resonance imaging and graph theory analysis method were applied to investigate the brain functional connectome patterns among 45 CID patients and 32 healthy controls. The brain functional connectome was constructed by thresholding partial correlation matrices of 90 brain regions from an automated anatomical labeling atlas. The topologic properties of brain functional connectomes at both global and nodal levels were tested. The CID patients had decreased number of module (p = .014) and hierarchy (p = .038), and increased assortativity (p = .035). Furthermore, some brain regions located in the default mode network, dorsal attention network, and sensory-motor network in these patients showed altered nodal centralities. Within these areas, the node betweenness of right central paracentral lobule had positive correlation with the Pittsburgh Sleep Quality Index score (R = 0.319, p = .039). The results imply that functional disruptions of CID patients may be related to disruptions in global and regional topological organization of the brain functional connectome, and provide new and important insights to understand the pathophysiological mechanisms of CID. Chronic insomnia disorder (CID) patients had disrupted global topological properties. Many brain regions of CID patients showed altered nodal centralities involving three different resting networks. Functional disruption of right central paracentral lobule may be a target for therapeutic intervention in pediatric CID. The disruption of topological organization might be explain the mechanism/reason of functional disruptions of CID.
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Affiliation(s)
- Zhonglin Li
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Rui Chen
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Min Guan
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Enfeng Wang
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Tianyi Qian
- Siemens Healthcare, MR Collaboration, NEA, Beijing, China
| | - Cuihua Zhao
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Zhi Zou
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Thomas Beck
- Siemens Healthcare, MR Strategy and Innovation, Erlangen, Germany
| | - Dapeng Shi
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Meiyun Wang
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China
| | - Hongju Zhang
- People's Hospital of Zhengzhou University, Department of Neurology, China.
| | - Yongli Li
- People's Hospital of Zhengzhou University, Department of Radiology, China; Henan Key Laboratory for Medical Imaging of Neurological Diseases, Department of Functional Imaging, China.
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637
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Subcortical evidence for a contribution of arousal to fMRI studies of brain activity. Nat Commun 2018; 9:395. [PMID: 29374172 PMCID: PMC5786066 DOI: 10.1038/s41467-017-02815-3] [Citation(s) in RCA: 112] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 12/29/2017] [Indexed: 12/12/2022] Open
Abstract
Cortical activity during periods of rest is punctuated by widespread, synchronous events in both electrophysiological and hemodynamic signals, but their behavioral relevance remains unclear. Here we report that these events correspond to momentary drops in cortical arousal and are associated with activity changes in the basal forebrain and thalamus. Combining fMRI and electrophysiology in macaques, we first establish that fMRI transients co-occur with spectral shifts in local field potentials (LFPs) toward low frequencies. Applying this knowledge to fMRI data from the human connectome project, we find that the fMRI transients are strongest in sensory cortices. Surprisingly, the positive cortical transients occur together with negative transients in focal subcortical areas known to be involved with arousal regulation, most notably the basal forebrain. This subcortical involvement, combined with the prototypical pattern of LFP spectral shifts, suggests that commonly observed widespread variations in fMRI cortical activity are associated with momentary drops in arousal. Resting cortical activity fluctuates, but it is unclear what underlies these variations in activity. Here, the authors show that large-scale fluctuations in fMRI cortical activity are associated with momentary decreases in cortical arousal and opposite activity changes in the basal forebrain and thalamus.
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638
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Ma X, Jiang G, Fu S, Fang J, Wu Y, Liu M, Xu G, Wang T. Enhanced Network Efficiency of Functional Brain Networks in Primary Insomnia Patients. Front Psychiatry 2018; 9:46. [PMID: 29515469 PMCID: PMC5826384 DOI: 10.3389/fpsyt.2018.00046] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Accumulating evidence from neuroimaging studies suggests that primary insomnia (PI) affects interregional neural coordination of multiple interacting functional brain networks. However, a complete understanding of the whole-brain network organization from a system-level perspective in PI is still lacking. To this end, we investigated in topological organization changes in brain functional networks in PI. 36 PI patients and 38 age-, sex-, and education-matched healthy controls were recruited. All participants underwent a series of neuropsychological assessments and resting-state functional magnetic resonance imaging scans. Individual whole-brain functional network were constructed and analyzed using graph theory-based network approaches. There were no significant differences with respect to age, sex, or education between groups (P > 0.05). Graph-based analyses revealed that participants with PI had a significantly higher total number of edges (P = 0.022), global efficiency (P = 0.014), and normalized global efficiency (P = 0.002), and a significantly lower normalized local efficiency (P = 0.042) compared with controls. Locally, several prefrontal and parietal regions, the superior temporal gyrus, and the thalamus exhibited higher nodal efficiency in participants with PI (P < 0.05, false discovery rate corrected). In addition, most of these regions showed increased functional connectivity in PI patients (P < 0.05, corrected). Finally, altered network efficiency was correlated with neuropsychological variables of the Epworth Sleepiness Scale and Insomnia Severity Index in patients with PI. PI is associated with abnormal organization of large-scale functional brain networks, which may account for memory and emotional dysfunction in people with PI. These findings provide novel implications for neural substrates associated with PI.
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Affiliation(s)
- Xiaofen Ma
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Shishun Fu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Jin Fang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yunfan Wu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Mengchen Liu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Guang Xu
- Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Tianyue Wang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
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639
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Clark DL, Konduru N, Kemp A, Bray S, Brown EC, Goodyear B, Ramasubbu R. The impact of age of onset on amygdala intrinsic connectivity in major depression. Neuropsychiatr Dis Treat 2018; 14:343-352. [PMID: 29403280 PMCID: PMC5784751 DOI: 10.2147/ndt.s145042] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Early-onset major depressive disorder (EO-MDD), beginning during childhood and adolescence, is associated with more illness burden and a worse prognosis than adult-onset MDD (AO-MDD), but little is known about the neural features distinguishing these subgroup phenotypes. Functional abnormalities of the amygdala are central to major depressive disorder (MDD) neurobiology; therefore, we examined whether amygdala intrinsic connectivity (IC) can differentiate EO-MDD from AO-MDD in a cohort of adult MDD patients. SUBJECTS AND METHODS Twenty-one EO-MDD (age of onset ≤18 years), 31 AO-MDD patients (age of onset ≥19 years), and 19 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (7 minutes). Amygdala seed-based resting-state functional connectivity was compared between groups. RESULTS AO-MDD patients showed loss of inverse left amygdala-left dorsolateral prefrontal cortex IC and increased inverse left amygdala-left inferior parietal IC, compared to both HCs and EO-MDD. EO-MDD showed a switch from inverse to positive IC with right dorsomedial prefrontal cortex, compared to HCs and AO-MDD. This effect was removed when we controlled for illness burden. CONCLUSION Alterations in amygdala IC with the default-mode network were specifically related to EO-MDD, whereas amygdala IC with executive cognitive control regions was preferentially disrupted in AO-MDD. Increased illness burden, an important clinical marker of EO-MDD, accounted for its specific effects on amygdala IC. Brain imaging has the potential for validation of clinical subtypes and can provide markers of prognostic value in MDD patients.
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Affiliation(s)
- Darren L Clark
- Department of Psychiatry.,Department of Clinical Neuroscience.,Mathison Centre for Mental Health Research and Education.,Hotchkiss Brain Institute, University of Calgary, Calgary
| | | | - Anne Kemp
- School of Medicine, University of Alberta, Edmonton
| | - Signe Bray
- Department of Radiology.,Department of Pediatrics, University of Calgary.,Child and Adolescent Imaging Research Program, Alberta Children's Hospital, Calgary, AB, Canada
| | - Elliot C Brown
- Department of Psychiatry.,Department of Clinical Neuroscience.,Mathison Centre for Mental Health Research and Education.,Hotchkiss Brain Institute, University of Calgary, Calgary
| | - Bradley Goodyear
- Department of Psychiatry.,Department of Clinical Neuroscience.,Hotchkiss Brain Institute, University of Calgary, Calgary.,Department of Radiology
| | - Rajamannar Ramasubbu
- Department of Psychiatry.,Department of Clinical Neuroscience.,Mathison Centre for Mental Health Research and Education.,Hotchkiss Brain Institute, University of Calgary, Calgary
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640
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Huang H, Tanner J, Parvataneni H, Rice M, Horgas A, Ding M, Price C. Impact of Total Knee Arthroplasty with General Anesthesia on Brain Networks: Cognitive Efficiency and Ventricular Volume Predict Functional Connectivity Decline in Older Adults. J Alzheimers Dis 2018; 62:319-333. [PMID: 29439328 PMCID: PMC5827939 DOI: 10.3233/jad-170496] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Using resting state functional magnetic resonance imaging (RS-fMRI), we explored: 1) pre- to post-operative changes in functional connectivity in default mode, salience, and central executive networks after total knee arthroplasty (TKA) with general anesthesia, and 2) the contribution of cognitive/brain reserve metrics these resting state functional declines. Individuals age 60 and older electing unilateral total knee arthroplasty (TKA; n = 48) and non-surgery peers with osteoarthritis (n = 45) completed baseline cognitive testing and baseline and post-surgery (post-baseline, 48-h post-surgery) brain MRI. We acquired cognitive and brain estimates for premorbid (vocabulary, reading, education, intracranial volume) and current (working memory, processing speed, declarative memory, ventricular volume) reserve. Functional network analyses corrected for pain severity and pain medication. The surgery group declined in every functional network of interest (p < 0.001). Relative to non-surgery peers, 23% of surgery participants declined in at least one network and 15% of the total TKA sample declined across all networks. Larger preoperative ventricular volume and lower scores on preoperative metrics of processing speed and working memory predicted default mode network connectivity decline. Premorbid cognitive and premorbid brain reserve did not predict decline. Within 48 hours after surgery, at least one fourth of the older adult sample showed significant functional network decline. Metrics of current brain status (ventricular volume), working memory, and processing speed predicted the severity of default mode network connectivity decline. These findings demonstrate the relevance of preoperative cognition and brain integrity on acute postoperative functional network change.
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Affiliation(s)
- Haiqing Huang
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Jared Tanner
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Hari Parvataneni
- Department of Orthopedic Surgery, University of Florida, Gainesville, FL, USA
| | - Mark Rice
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
| | - Ann Horgas
- College of Nursing, University of Florida, Gainesville, FL, USA
| | - Mingzhou Ding
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Catherine Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
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641
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Identifying and characterizing systematic temporally-lagged BOLD artifacts. Neuroimage 2017; 171:376-392. [PMID: 29288128 DOI: 10.1016/j.neuroimage.2017.12.082] [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: 08/11/2017] [Revised: 12/20/2017] [Accepted: 12/22/2017] [Indexed: 01/08/2023] Open
Abstract
Residual noise in the BOLD signal remains problematic for fMRI - particularly for techniques such as functional connectivity, where findings can be spuriously influenced by noise sources that can covary with individual differences. Many such potential noise sources - for instance, motion and respiration - can have a temporally lagged effect on the BOLD signal. Thus, here we present a tool for assessing residual lagged structure in the BOLD signal that is associated with nuisance signals, using a construction similar to a peri-event time histogram. Using this method, we find that framewise displacements - both large and very small - were followed by structured, prolonged, and global changes in the BOLD signal that depend on the magnitude of the preceding displacement and extend for tens of seconds. This residual lagged BOLD structure was consistent across datasets, and independently predicted considerable variance in the global cortical signal (as much as 30-40% in some subjects). Mean functional connectivity estimates varied similarly as a function of displacements occurring many seconds in the past, even after strict censoring. Similar patterns of residual lagged BOLD structure were apparent following respiratory fluctuations (which covaried with framewise displacements), implicating respiration as one likely mechanism underlying the displacement-linked structure observed. Global signal regression largely attenuates this artifactual structure. These findings suggest the need for caution in interpreting results of individual difference studies where noise sources might covary with the individual differences of interest, and highlight the need for further development of preprocessing techniques for mitigating such structure in a more nuanced and targeted manner.
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642
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Melie-Garcia L, Slater D, Ruef A, Sanabria-Diaz G, Preisig M, Kherif F, Draganski B, Lutti A. Networks of myelin covariance. Hum Brain Mapp 2017; 39:1532-1554. [PMID: 29271053 PMCID: PMC5873432 DOI: 10.1002/hbm.23929] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 12/02/2017] [Accepted: 12/11/2017] [Indexed: 01/05/2023] Open
Abstract
Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, ). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these "networks of myelin covariance" (Myelin-Nets). The Myelin-Nets were built from quantitative Magnetization Transfer data-an in-vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin-Nets. We therefore selected two age groups: Young-Age (20-31 years old) and Old-Age (60-71 years old) and a pool of participants from 48 to 87 years old for a Myelin-Nets aging trajectory study. We found that the topological organization of the Myelin-Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin-Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging.
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Affiliation(s)
- Lester Melie-Garcia
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland
| | - David Slater
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland
| | - Anne Ruef
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland
| | - Gretel Sanabria-Diaz
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital (CHUV), Switzerland
| | - Ferath Kherif
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland
| | - Bogdan Draganski
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland.,Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Antoine Lutti
- LREN, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Switzerland
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643
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Yousefi B, Shin J, Schumacher EH, Keilholz SD. Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal. Neuroimage 2017; 167:297-308. [PMID: 29175200 DOI: 10.1016/j.neuroimage.2017.11.043] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 11/17/2017] [Accepted: 11/20/2017] [Indexed: 12/14/2022] Open
Abstract
Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual's global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.
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Affiliation(s)
- Behnaz Yousefi
- Biomedical Engineering, Emory University/Georgia Institute of Technology, 1760 Haygood Dr, HSRB W200, Atlanta, GA 30322, United States.
| | - Jaemin Shin
- Center for Advanced Brain Imaging, Georgia Institute of Technology, 831 Marietta St NW, Atlanta, GA 30318, United States.
| | - Eric H Schumacher
- School of Psychology, Georgia Institute of Technology, 654 Cherry Street, JS Coon Bldg R224, Atlanta, GA 30332, United States.
| | - Shella D Keilholz
- Biomedical Engineering, Emory University/Georgia Institute of Technology, 1760 Haygood Dr, HSRB W200, Atlanta, GA 30322, United States.
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644
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Mitra A, Snyder AZ, Tagliazucchi E, Laufs H, Elison J, Emerson RW, Shen MD, Wolff JJ, Botteron KN, Dager S, Estes AM, Evans A, Gerig G, Hazlett HC, Paterson SJ, Schultz RT, Styner MA, Zwaigenbaum L, Schlaggar BL, Piven J, Pruett JR, Raichle M. Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness. PLoS One 2017; 12:e0188122. [PMID: 29149191 PMCID: PMC5693436 DOI: 10.1371/journal.pone.0188122] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 11/01/2017] [Indexed: 11/19/2022] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs. sleep are well documented. To investigate this question, we compared functional connectivity and BOLD signal propagation patterns in 6, 12, and 24 month old sleeping infants with patterns in adult wakefulness and non-REM sleep. We find that important functional connectivity features seen during infant sleep closely resemble those seen during adult sleep, including reduced default mode network functional connectivity. However, we also find differences between infant and adult sleep, especially in thalamic BOLD signal propagation patterns. These findings highlight the importance of considering sleep state when drawing developmental inferences in infant rs-fMRI.
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Affiliation(s)
- Anish Mitra
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- * E-mail:
| | - Abraham Z. Snyder
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Enzo Tagliazucchi
- Departamento de Fisica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts-University Kiel, Germany
| | - Jed Elison
- Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Robert W. Emerson
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Mark D. Shen
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jason J. Wolff
- Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Kelly N. Botteron
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Stephen Dager
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Annette M. Estes
- Department of Psychology, University of Washington, Seattle, Washington, United States of America
| | - Alan Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Guido Gerig
- Department of Psychiatry, New York University, New York, New York, United States of America
| | - Heather C. Hazlett
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Sarah J. Paterson
- Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Robert T. Schultz
- Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Martin A. Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lonnie Zwaigenbaum
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | | | - Bradley L. Schlaggar
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Joseph Piven
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - John R. Pruett
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Marcus Raichle
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
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645
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Liu J, Liao X, Xia M, He Y. Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns. Hum Brain Mapp 2017; 39:902-915. [PMID: 29143409 DOI: 10.1002/hbm.23890] [Citation(s) in RCA: 135] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/03/2017] [Accepted: 11/07/2017] [Indexed: 12/23/2022] Open
Abstract
The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.
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Affiliation(s)
- Jin Liu
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
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646
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Henry TR, Dichter GS, Gates K. Age and Gender Effects on Intrinsic Connectivity in Autism Using Functional Integration and Segregation. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 3:414-422. [PMID: 29735152 DOI: 10.1016/j.bpsc.2017.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 10/29/2017] [Accepted: 10/30/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND The objective of this study was to examine intrinsic whole-brain functional connectivity in autism spectrum disorder (ASD) using the framework of functional segregation and integration. Emphasis was given to potential gender and developmental effects as well as identification of specific networks that may contribute to the global results. METHODS We leveraged an open data resource (N = 1587) of resting-state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange (ABIDE) initiative, combining data from more than 2100 unique cross-sectional datasets in ABIDE I and ABIDE II collected at different sites. Modularity and global efficiency were utilized to assess functional segregation and integration, respectively. A meta-analytic approach for handling site differences was used. The effects of age, gender, and diagnostic category on segregation and integration were assessed using linear regression. RESULTS Modularity decreased nonlinearly in the ASD group with age, as evidenced by an increase and then decrease over development. Global efficiency had an opposite relationship with age by first decreasing and then increasing in the ASD group. Both modularity and global efficiency remained largely stable in the typically developing control group during development, representing a significantly different effect than seen in the ASD group. Age effects on modularity were localized to the somatosensory network. Finally, a marginally significant interaction between age, gender, and diagnostic category was found for modularity. CONCLUSIONS Our results support prior work that suggested a quadratic effect of age on brain development in ASD, while providing new insights about the specific characteristics of developmental and gender effects on intrinsic connectivity in ASD.
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Affiliation(s)
- Teague Rhine Henry
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
| | - Gabriel S Dichter
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kathleen Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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647
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Sha Z, Xia M, Lin Q, Cao M, Tang Y, Xu K, Song H, Wang Z, Wang F, Fox PT, Evans AC, He Y. Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders. Cereb Cortex 2017; 28:4179-4194. [PMID: 29136110 DOI: 10.1093/cercor/bhx273] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Zhiqiang Sha
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qixiang Lin
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Miao Cao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ke Xu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Department of Radiology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX, USA
- Department of Radiology, University of Texas Health Science Center at San Antonio, TX, USA
- South Texas Veterans Health Care System at San Antonio, TX, USA
- Shenzhen University School of Medicine, Shenzhen, China
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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648
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The neuronal network involved in self-attribution of an artificial hand: A lesion network-symptom-mapping study. Neuroimage 2017; 166:317-324. [PMID: 29122723 DOI: 10.1016/j.neuroimage.2017.11.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 10/31/2017] [Accepted: 11/04/2017] [Indexed: 12/20/2022] Open
Abstract
The feeling of body-ownership can be experimentally manipulated using the rubber hand illusion (RHI) paradigm. Participants experience a sense of ownership over an artificial hand when their hidden real hand and the visible artificial hand are synchronously stroked. Using lesion masks and behavioral data from a previous study on RHI failure in acute stroke patients, we here employed lesion network-symptom-mapping (LNSM) based on normative functional connectome data to identify lesion-dependent network connectivity related to the experience of self-attribution of an artificial hand in the RHI paradigm. We found that failure to experience the RHI was associated with higher normative lesion-dependent network connectivity to the right temporoparietal junction (rTPJ), right anterior Insula (raI) and right inferior frontal gyrus (rIFG). Since these areas were spared by the infarction in most patients with RHI failure (89% for rTPJ and 94% for raI/rIFG), the analysis suggests that remote dysfunction in rTPJ, raI, and rIFG accounted for RHI failure. These results highlight the potential role of rTPJ, raI, and rIFG in bodily self-consciousness. LNSM is a powerful tool capable of delineating the architecture of functional networks underlying complex cognitive function.
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649
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Chen X, Lu B, Yan CG. Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Hum Brain Mapp 2017; 39:300-318. [PMID: 29024299 DOI: 10.1002/hbm.23843] [Citation(s) in RCA: 229] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/01/2017] [Accepted: 10/02/2017] [Indexed: 12/18/2022] Open
Abstract
Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect "true" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, School of Medicine, New York, NY, USA
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650
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Preti MG, Van De Ville D. Dynamics of functional connectivity at high spatial resolution reveal long-range interactions and fine-scale organization. Sci Rep 2017; 7:12773. [PMID: 28986564 PMCID: PMC5630612 DOI: 10.1038/s41598-017-12993-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/14/2017] [Indexed: 12/18/2022] Open
Abstract
Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging sheds light onto moment-to-moment reconfigurations of large-scale functional brain networks. Due to computational limits, connectivity is typically computed using pre-defined atlases, a non-trivial choice that might influence results. Here, we leverage new computational methods to retrieve dFC at the voxel level in terms of dominant patterns of fluctuations, and demonstrate that this new representation is informative to derive meaningful brain parcellations, capturing both long-range interactions and fine-scale local organization. Specifically, voxelwise dFC dominant patterns were captured through eigenvector centrality followed by clustering across time/subjects to yield most representative dominant patterns (RDPs). Voxel-wise labeling according to positive/negative contributions to RDPs, led to 37 unique labels identifying strikingly symmetric dFC long-range patterns. These included 449 contiguous regions, defining a fine-scale parcellation consistent with known cortical/subcortical subdivisions. Our contribution provides an alternative to obtain a whole-brain parcellation that is for the first time driven by voxel-level dFC and bridges the gap between voxel-based approaches and graph theoretical analysis.
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Affiliation(s)
- Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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