151
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Schumacher J, Peraza LR, Firbank M, Thomas AJ, Kaiser M, Gallagher P, O'Brien JT, Blamire AM, Taylor JP. Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease. Neuroimage Clin 2019; 22:101812. [PMID: 30991620 PMCID: PMC6462776 DOI: 10.1016/j.nicl.2019.101812] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 03/12/2019] [Accepted: 04/02/2019] [Indexed: 01/22/2023]
Abstract
We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.
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Affiliation(s)
- Julia Schumacher
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom.
| | - Luis R Peraza
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom; Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
| | - Michael Firbank
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
| | - Alan J Thomas
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom; Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Peter Gallagher
- Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge CB2 0SP, United Kingdom
| | - Andrew M Blamire
- Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
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152
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Niu H, Zhu Z, Wang M, Li X, Yuan Z, Sun Y, Han Y. Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study. NEUROPHOTONICS 2019; 6:025010. [PMID: 31205976 PMCID: PMC6548336 DOI: 10.1117/1.nph.6.2.025010] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/02/2019] [Indexed: 05/24/2023]
Abstract
Communication within the brain is highly dynamic. Alzheimer's disease (AD) exhibits dynamic progression corresponding to a decline in memory and cognition. However, little is known of whether brain dynamics are disrupted in AD and its prodromal stage, mild cognitive impairment (MCI). For our study, we acquired high sampling rate functional near-infrared spectroscopy imaging data at rest from the entire cortex of 23 patients with AD dementia, 25 patients with amnestic mild cognitive impairment (aMCI), and 30 age-matched healthy controls (HCs). Sliding-window correlation and k-means clustering analyses were used to construct dynamic functional connectivity (FC) maps for each participant. We discovered that the brain's dynamic FC variability strength ( Q ) significantly increased in both aMCI and AD group as compared to HCs. Using the Q value as a measurement, the classification performance exhibited a good power in differentiating aMCI [area under the curve ( AUC = 82.5 % )] or AD ( AUC = 86.4 % ) from HCs. Furthermore, we identified two abnormal brain FC states in the AD group, of which the occurrence frequency ( F ) exhibited a significant decrease for the first-level FC state (state 1) and a significant increase for the second-level FC state (state 2). We also found that the abnormal F in these two states significantly correlated with the cognitive impairment in patients. These findings provide the first evidence to demonstrate the disruptions of dynamic brain connectivity in aMCI and AD and extend the traditional static (i.e., time-averaged) FC findings in the disease (i.e., disconnection syndrome) and thus provide insights into understanding the pathophysiological mechanisms occurring in aMCI and AD.
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Affiliation(s)
- Haijing Niu
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing, China
| | - Zhaojun Zhu
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing, China
| | - Mengjing Wang
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing, China
| | - Xuanyu Li
- Xuan Wu Hospital of Capital Medical University, Department of Neurology, Beijing, China
| | - Zhen Yuan
- University of Macau, Faculty of Health Sciences, Macao, China
| | - Yu Sun
- Xuan Wu Hospital of Capital Medical University, Department of Neurology, Beijing, China
| | - Ying Han
- Xuan Wu Hospital of Capital Medical University, Department of Neurology, Beijing, China
- Beijing Institute for Brain Disorders, Center of Alzheimer’s Disease, Beijing, China
- Beijing Institute of Geriatrics, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
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153
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Van de Steen F, Almgren H, Razi A, Friston K, Marinazzo D. Dynamic causal modelling of fluctuating connectivity in resting-state EEG. Neuroimage 2019; 189:476-484. [PMID: 30690158 PMCID: PMC6435216 DOI: 10.1016/j.neuroimage.2019.01.055] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 01/15/2019] [Accepted: 01/21/2019] [Indexed: 11/22/2022] Open
Abstract
Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1- min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the default mode network and to a lesser extent the saliency network comprised both subject-specific components and a common component. For both networks, connections to higher order regions appear to monotonically increase during the 1- min period. These results not only establish the predictive validity of dynamic connectivity estimates - in virtue of detecting systematic changes over subjects - they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.
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Affiliation(s)
| | | | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia; The Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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154
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Vanderwal T, Eilbott J, Castellanos FX. Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging. Dev Cogn Neurosci 2019; 36:100600. [PMID: 30551970 PMCID: PMC6969259 DOI: 10.1016/j.dcn.2018.10.004] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 09/13/2018] [Accepted: 10/08/2018] [Indexed: 11/28/2022] Open
Abstract
The use of movie-watching as an acquisition state for functional connectivity (FC) MRI has recently enabled multiple groups to obtain rich data sets in younger children with both substantial sample sizes and scan durations. Using naturalistic paradigms such as movies has also provided analytic flexibility for these developmental studies that extends beyond conventional resting state approaches. This review highlights the advantages and challenges of using movies for developmental neuroimaging and explores some of the methodological issues involved in designing pediatric studies with movies. Emerging themes from movie-watching studies are discussed, including an emphasis on intersubject correlations, developmental changes in network interactions under complex naturalistic conditions, and dynamic age-related changes in both sensory and higher-order network FC even in narrow age ranges. Converging evidence suggests an enhanced ability to identify brain-behavior correlations in children when using movie-watching data relative to both resting state and conventional tasks. Future directions and cautionary notes highlight the potential and the limitations of using movies to study FC in pediatric populations.
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Affiliation(s)
- Tamara Vanderwal
- University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada; Yale Child Study Center, 230 South Frontage Road, New Haven CT, 06519, United States.
| | - Jeffrey Eilbott
- Yale Child Study Center, 230 South Frontage Road, New Haven CT, 06519, United States
| | - F Xavier Castellanos
- The Child Study Center at New York University Langone Medical Center, 1 Park Avenue, New York, NY, 10016, United States; Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY, 10962, United States
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155
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Raut RV, Mitra A, Snyder AZ, Raichle ME. On time delay estimation and sampling error in resting-state fMRI. Neuroimage 2019; 194:211-227. [PMID: 30902641 DOI: 10.1016/j.neuroimage.2019.03.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/26/2019] [Accepted: 03/08/2019] [Indexed: 01/21/2023] Open
Abstract
Accumulating evidence indicates that resting-state functional magnetic resonance imaging (rsfMRI) signals correspond to propagating electrophysiological infra-slow activity (<0.1 Hz). Thus, pairwise correlations (zero-lag functional connectivity (FC)) and temporal delays among regional rsfMRI signals provide useful, complementary descriptions of spatiotemporal structure in infra-slow activity. However, the slow nature of fMRI signals implies that practical scan durations cannot provide sufficient independent temporal samples to stabilize either of these measures. Here, we examine factors affecting sampling variability in both time delay estimation (TDE) and FC. Although both TDE and FC accuracy are highly sensitive to data quantity, we use surrogate fMRI time series to study how the former is additionally related to the magnitude of a given pairwise correlation and, to a lesser extent, the temporal sampling rate. These contingencies are further explored in real data comprising 30-min rsfMRI scans, where sampling error (i.e., limited accuracy owing to insufficient data quantity) emerges as a significant but underappreciated challenge to FC and, even more so, to TDE. Exclusion of high-motion epochs exacerbates sampling error; thus, both sides of the bias-variance (or data quality-quantity) tradeoff associated with data exclusion should be considered when analyzing rsfMRI data. Finally, we present strategies for TDE in motion-corrupted data, for characterizing sampling error in TDE and FC, and for mitigating the influence of sampling error on lag-based analyses.
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Affiliation(s)
- Ryan V Raut
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA.
| | - Anish Mitra
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA
| | - Abraham Z Snyder
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA; Departments of Neurology, Washington University, St. Louis, MO, 63110, USA
| | - Marcus E Raichle
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA; Departments of Neurology, Washington University, St. Louis, MO, 63110, USA
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156
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Donnelly-Kehoe P, Saenger VM, Lisofsky N, Kühn S, Kringelbach ML, Schwarzbach J, Lindenberger U, Deco G. Reliable local dynamics in the brain across sessions are revealed by whole-brain modeling of resting state activity. Hum Brain Mapp 2019; 40:2967-2980. [PMID: 30882961 DOI: 10.1002/hbm.24572] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 02/05/2019] [Accepted: 03/04/2019] [Indexed: 01/10/2023] Open
Abstract
Resting state fMRI is a tool for studying the functional organization of the human brain. Ongoing brain activity at "rest" is highly dynamic, but procedures such as correlation or independent component analysis treat functional connectivity (FC) as if, theoretically, it is stationary and therefore the fluctuations observed in FC are thought as noise. Consequently, FC is not usually used as a single-subject level marker and it is limited to group studies. Here we develop an imaging-based technique capable of reliably portraying information of local dynamics at a single-subject level by using a whole-brain model of ongoing dynamics that estimates a local parameter, which reflects if each brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting-state sessions of one single subject and single resting-state sessions from a group of 50 participants we demonstrate that brain dynamics can be quantified consistently with respect to group dynamics using a scanning time of 20 min. We show that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger heterogeneity in its values compared to other lobules. Nevertheless, frontal regions and hubs showed higher consistency within the same subject while the inter-session variability found in primary visual and motor areas was only as high as the one found across subjects. The framework presented here can be used to study functional brain dynamics at group and, more importantly, at individual level, opening new avenues for possible clinical applications.
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Affiliation(s)
- Patricio Donnelly-Kehoe
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS), National Scientific and Technical Research Council (CONICET), Rosario, Argentina.,Laboratory for System Dynamics and Signal Processing, Universidad Nacional de Rosario, Rosario, Argentina.,Laboratory of Neuroimaging and Neuroscience (LANEN), INECO Foundation Rosario, Rosario, Argentina
| | - Victor M Saenger
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nina Lisofsky
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain (MIB), Dept. of Clinical Medicine, Aarhus University, Denmark
| | - Jens Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Max Planck University College London, Centre for Computational Psychiatry and Ageing Research, London, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain
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157
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Xie H, Zheng CY, Handwerker DA, Bandettini PA, Calhoun VD, Mitra S, Gonzalez-Castillo J. Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information. Neuroimage 2019; 188:502-514. [PMID: 30576850 PMCID: PMC6401299 DOI: 10.1016/j.neuroimage.2018.12.037] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/13/2018] [Accepted: 12/17/2018] [Indexed: 10/27/2022] Open
Abstract
Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA; Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Charles Y Zheng
- Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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158
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Xia Y, Chen Q, Shi L, Li M, Gong W, Chen H, Qiu J. Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan. Hum Brain Mapp 2019; 40:717-728. [PMID: 30515914 PMCID: PMC6865727 DOI: 10.1002/hbm.24385] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 08/27/2018] [Indexed: 12/12/2022] Open
Abstract
The transition from early adulthood to the elder is marked by functional and structural brain transformations. Many previous studies examined the correlation between the functional connectivity (FC) and aging using resting-state fMRI. Results showed that the changes in FC are linked to aging as well as the cognitive ability changes. However, some researchers proposed that the FC is not static but dynamic changes during the resting-state fMRI scan. In this study, we examined the correlation between the resting-state dynamic functional network connectivity and age using resting scan data of 434 subjects. The results suggested: (a) age is negatively associated with variability of dynamic functional network connectivity state; (b) the dwell time of each age range spends in each state is different; (c) the dynamic graph metrics curve of each age ranges is different and 19-30 age range has the largest average global efficiency and average local efficiency; (d) some dynamic functional network connectivity measures were correlated to the certain cognitive ability. Overall, the results suggested the changes in dynamic functional network connectivity measures may be a characteristic of the aging process and in further investigations may provide a deep understanding of the aging process.
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Affiliation(s)
- Yunman Xia
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Liang Shi
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - MengZe Li
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Weikang Gong
- Key Laboratory of Computational BiologyCASMPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Hong Chen
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
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159
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Mokhtari F, Akhlaghi MI, Simpson SL, Wu G, Laurienti PJ. Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state. Neuroimage 2019; 189:655-666. [PMID: 30721750 DOI: 10.1016/j.neuroimage.2019.02.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/10/2019] [Accepted: 02/01/2019] [Indexed: 12/22/2022] Open
Abstract
The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.
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Affiliation(s)
- Fatemeh Mokhtari
- Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA.
| | | | - Sean L Simpson
- Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Guorong Wu
- Biomedical Research Imaging Center, Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Translational Science Center, Wake Forest University, Winston Salem, NC, USA
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160
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Demertzi A, Tagliazucchi E, Dehaene S, Deco G, Barttfeld P, Raimondo F, Martial C, Fernández-Espejo D, Rohaut B, Voss HU, Schiff ND, Owen AM, Laureys S, Naccache L, Sitt JD. Human consciousness is supported by dynamic complex patterns of brain signal coordination. SCIENCE ADVANCES 2019; 5:eaat7603. [PMID: 30775433 PMCID: PMC6365115 DOI: 10.1126/sciadv.aat7603] [Citation(s) in RCA: 227] [Impact Index Per Article: 45.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 12/19/2018] [Indexed: 05/23/2023]
Abstract
Adopting the framework of brain dynamics as a cornerstone of human consciousness, we determined whether dynamic signal coordination provides specific and generalizable patterns pertaining to conscious and unconscious states after brain damage. A dynamic pattern of coordinated and anticoordinated functional magnetic resonance imaging signals characterized healthy individuals and minimally conscious patients. The brains of unresponsive patients showed primarily a pattern of low interareal phase coherence mainly mediated by structural connectivity, and had smaller chances to transition between patterns. The complex pattern was further corroborated in patients with covert cognition, who could perform neuroimaging mental imagery tasks, validating this pattern's implication in consciousness. Anesthesia increased the probability of the less complex pattern to equal levels, validating its implication in unconsciousness. Our results establish that consciousness rests on the brain's ability to sustain rich brain dynamics and pave the way for determining specific and generalizable fingerprints of conscious and unconscious states.
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Affiliation(s)
- A. Demertzi
- GIGA-Consciousness, GIGA Institute B34, University of Liège, Avenue de l’Hôpital, 11, 4000 Sart Tilman, Belgium
- INSERM, U 1127, F-75013 Paris, France
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, 47 bd de l’Hôpital, 75013 Paris, France
| | - E. Tagliazucchi
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, 47 bd de l’Hôpital, 75013 Paris, France
- Instituto de Física de Buenos Aires and Physics Deparment (University of Buenos Aires), Buenos Aires, Argentina
| | - S. Dehaene
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Sud, Université Paris-Saclay, F-91191 Gif/Yvette, France
- Collège de France, 11, Place Marcelin Berthelot, 75005 Paris, France
| | - G. Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Calle Ramon Trias Fargas 25-27, Barcelona 08005, Spain
- Institucio Catalana de la Recerca I Estudis Avancats (ICREA), University of Pompeu Fabra, Passeig Lluis Companys 23, Barcelona 08010, Spain
| | - P. Barttfeld
- Laboratory of Integrative Neuroscience, Physics Department, FCEyN UBA and IFIBA, CONICET, Pabellón 1, Ciudad Universitaria, 1428 Buenos Aires, Argentina
| | - F. Raimondo
- GIGA-Consciousness, GIGA Institute B34, University of Liège, Avenue de l’Hôpital, 11, 4000 Sart Tilman, Belgium
- INSERM, U 1127, F-75013 Paris, France
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, 47 bd de l’Hôpital, 75013 Paris, France
- Department of Computer Science, Faculty of Exact and Natural Sciences, Intendente Güiraldes 2160–Ciudad Universitaria–C1428EGA, University of Buenos Aires, Argentina
- Sorbonne Universités, UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière, 91-105 bd de l’Hôpital, 75013 Paris, France
- CONICET–Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación, Godoy Cruz 2290, C1425FQB Ciudad Autónoma de Buenos Aires, Argentina
| | - C. Martial
- GIGA-Consciousness, GIGA Institute B34, University of Liège, Avenue de l’Hôpital, 11, 4000 Sart Tilman, Belgium
| | - D. Fernández-Espejo
- Centre for Human Brain Health, University of Birmingham, B15 2TT Birmingham, UK
- School of Psychology, University of Birmingham, B15 2TT, Birmingham, UK
- The Brain and Mind Institute, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, Ontario, Canada
| | - B. Rohaut
- INSERM, U 1127, F-75013 Paris, France
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, 47 bd de l’Hôpital, 75013 Paris, France
- Department of Neurology, Columbia University, 710 West 168th Street, New York, NY 10032-3784, USA
| | - H. U. Voss
- Radiology Department, Citigroup Biomedical Imaging Center, Weill Cornell Medical College, 516 E. 72nd Street, New York, NY 10021, USA
| | - N. D. Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA
| | - A. M. Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, Ontario, Canada
| | - S. Laureys
- GIGA-Consciousness, GIGA Institute B34, University of Liège, Avenue de l’Hôpital, 11, 4000 Sart Tilman, Belgium
| | - L. Naccache
- INSERM, U 1127, F-75013 Paris, France
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, 47 bd de l’Hôpital, 75013 Paris, France
| | - J. D. Sitt
- INSERM, U 1127, F-75013 Paris, France
- Institut du Cerveau et de la Moelle Epinière, Hôpital Pitié-Salpêtrière, 47 bd de l’Hôpital, 75013 Paris, France
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161
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Lydon-Staley DM, Ciric R, Satterthwaite TD, Bassett DS. Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity. Netw Neurosci 2019; 3:427-454. [PMID: 30793090 PMCID: PMC6370491 DOI: 10.1162/netn_a_00071] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 09/19/2018] [Indexed: 01/13/2023] Open
Abstract
Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced by participant motion. This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8-22 years). Each strategy was evaluated according to a number of benchmarks, including (a) the residual association between participant motion and edge dispersion, (b) distance-dependent effects of motion on edge dispersion, (c) the degree to which functional subnetworks could be identified by multilayer modularity maximization, and (d) measures of module reconfiguration, including node flexibility and node promiscuity. Results indicate variability in the effectiveness of the evaluated pipelines across benchmarks. Methods that included global signal regression were the most consistently effective de-noising strategies.
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Affiliation(s)
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
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162
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Mash LE, Linke AC, Olson LA, Fishman I, Liu TT, Müller RA. Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study. Hum Brain Mapp 2019; 40:2377-2389. [PMID: 30681228 DOI: 10.1002/hbm.24529] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 01/09/2019] [Indexed: 01/17/2023] Open
Abstract
There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns ("states"), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole-brain, data-driven regions of interest were derived from group independent component analysis. Sliding window analysis and k-means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher-order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.
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Affiliation(s)
- Lisa E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Lindsay A Olson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - Inna Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Thomas T Liu
- Center for Functional MRI, Department of Radiology, University of California San Diego, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
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163
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Kottaram A, Johnston LA, Cocchi L, Ganella EP, Everall I, Pantelis C, Kotagiri R, Zalesky A. Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Hum Brain Mapp 2019; 40:2212-2228. [PMID: 30664285 DOI: 10.1002/hbm.24519] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/06/2018] [Accepted: 12/26/2018] [Indexed: 02/03/2023] Open
Abstract
Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Victoria, Australia
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Eleni P Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Schizophrenia Research Group, Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia
| | - Ian Everall
- Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Psychology and Neuroscience, Institute of Psychiatry, Kings College London, London, United Kingdom.,South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Schizophrenia Research Group, Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia.,Department of Electrical and Electronic Engineering, Centre for Neural Engineering, The University of Melbourne, Victoria, Australia.,North Western Mental Health, Melbourne Health, Victoria, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia
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164
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Vergara VM, Damaraju E, Turner JA, Pearlson G, Belger A, Mathalon DH, Potkin SG, Preda A, Vaidya JG, van Erp TGM, McEwen S, Calhoun VD. Altered Domain Functional Network Connectivity Strength and Randomness in Schizophrenia. Front Psychiatry 2019; 10:499. [PMID: 31396111 PMCID: PMC6664085 DOI: 10.3389/fpsyt.2019.00499] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 06/24/2019] [Indexed: 01/12/2023] Open
Abstract
Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. Domain level refers to the integration of data from several brain networks grouped by their functional relationship. Data integration provides more consistent and accurate information compared to an individual brain network. This work considers two domain level measures: functional connectivity strength and randomness. The first measure is simply an average of connectivities within the domain. The second measure assesses the unpredictability and lack of pattern of functional connectivity within the domain. Domains with less random connectivity have higher chance of exhibiting a biologically meaningful connectivity pattern. Consistent with prior observations, individuals with schizophrenia showed aberrant domain connectivity strength between subcortical, cerebellar, and sensorial brain areas. Compared to healthy volunteers, functional connectivity between cognitive and default mode domains showed less randomness, while connectivity between default mode-sensorial areas showed more randomness in schizophrenia patients. These differences in connectivity patterns suggest deleterious rewiring trade-offs among important brain networks.
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Affiliation(s)
- Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,2The Mind Research Network, Albuquerque, NM, United States.,Psychology Department Georgia State University, Atlanta, GA, United States
| | - Eswar Damaraju
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jessica A Turner
- Psychology Department Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, United States.,Olin Neuropsychiatry Research Center, Institute of Living, HHC, Hartford, CT, United States
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States
| | - Daniel H Mathalon
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.,Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa, IA, United States
| | - Theo G M van Erp
- Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Sarah McEwen
- Pacific Neuroscience Institute, Santa Monica, CA, United States
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,2The Mind Research Network, Albuquerque, NM, United States.,Psychology Department Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, United States
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165
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Camchong J, Collins PF, Becker MP, Lim KO, Luciana M. Longitudinal Alterations in Prefrontal Resting Brain Connectivity in Non-Treatment-Seeking Young Adults With Cannabis Use Disorder. Front Psychiatry 2019; 10:514. [PMID: 31404267 PMCID: PMC6670783 DOI: 10.3389/fpsyt.2019.00514] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 07/01/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Cannabis is increasingly perceived as a harmless drug by recreational users, yet chronic use may impact brain changes into adulthood. Repeated cannabis exposure has been associated with enduring synaptic changes in executive control and reward networks. It is important to determine whether there are brain functional alterations within these networks in individuals that do not seek treatment for chronic cannabis abuse. Methods: This longitudinal study compared resting-state functional connectivity changes in executive control and reward networks between 23 non-treatment-seeking young adults with cannabis use disorder (6 females; baseline age M = 19.3 ± 1.18) and 21 age-matched controls (10 females; baseline age M = 19.4 ± 0.65) to determine group differences in the temporal trajectories of resting-state functional connectivity across a 2-year span. Results: Results showed i) significant increases in resting-state functional connectivity between the caudal anterior cingulate cortex and precentral and parietal regions over time in the control group, but not in the cannabis use disorder group, and ii) sustained lower resting-state functional connectivity of anterior cingulate cortex seeds with frontal and thalamic regions in the cannabis use disorder group vs. the age-matched controls. Resting-state functional connectivity strength was correlated with cannabis use patterns in the cannabis use disorder sample. Conclusion: Longitudinal alterations in intrinsic functional organization of executive control networks found in non-treatment-seeking young adults with cannabis use disorder (when compared to age-matched controls) may impact regulatory control over substance use behavior. Current findings were limited to examining executive control and reward networks seeded in ACC and NAcc, respectively. Future studies with larger sample sizes and enough power are needed to conduct exploratory analyses examining rsFC of other networks beyond those within the scope of the current study.
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Affiliation(s)
- Jazmin Camchong
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Paul F Collins
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Mary P Becker
- Department of Psychiatry, Hennepin Healthcare, Richfield, MN, United States
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Monica Luciana
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
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166
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Zhang Y, Guo G, Tian Y. Increased Temporal Dynamics of Intrinsic Brain Activity in Sensory and Perceptual Network of Schizophrenia. Front Psychiatry 2019; 10:484. [PMID: 31354546 PMCID: PMC6639429 DOI: 10.3389/fpsyt.2019.00484] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 06/19/2019] [Indexed: 12/12/2022] Open
Abstract
Schizophrenic subject is thought as a self-disorder patient related with abnormal brain functional network. It has been hypothesized that self-disorder is associated with the deficient functional integration of multisensory body signals in schizophrenic subjects. To further verify this assumption, 53 chronic schizophrenic subjects and 67 healthy subjects were included in this study and underwent resting-state functional magnetic resonance imaging. The data-driven methods, whole-brain temporal variability of fractional amplitude of low-frequency fluctuations and regional homogeneity (ReHo), were used to investigate dynamic local functional connectivity and dynamic local functional activity changes in schizophrenic subjects. Patients with schizophrenia exhibited increased temporal variability ReHo and fractional amplitude of low-frequency fluctuations across time windows within sensory and perception network (such as occipital gyrus, precentral and postcentral gyri, superior temporal gyrus, and thalamus). Critically, the increased dynamic ReHo of thalamus is significantly correlated with positive and total symptom of schizophrenic subjects. Our findings revealed that deficit in sensory and perception functional networks might contribute to neural physiopathology of self-disorder in schizophrenic subjects.
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Affiliation(s)
- Youxue Zhang
- School of Psychology, Chengdu Normal University, Chengdu, China
| | - Gang Guo
- School of Psychology, Chengdu Normal University, Chengdu, China
| | - Yuan Tian
- School of Foreign Languages, Chengdu Normal University, Chengdu, China
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167
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Development of Neuroimaging-Based Biomarkers in Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1192:159-195. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.
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168
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Modular reconfiguration of an auditory control brain network supports adaptive listening behavior. Proc Natl Acad Sci U S A 2018; 116:660-669. [PMID: 30587584 PMCID: PMC6329957 DOI: 10.1073/pnas.1815321116] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
How do brain networks shape our listening behavior? We here develop and test the hypothesis that, during challenging listening situations, intrinsic brain networks are reconfigured to adapt to the listening demands and thus, to enable successful listening. We find that, relative to a task-free resting state, networks of the listening brain show higher segregation of temporal auditory, ventral attention, and frontal control regions known to be involved in speech processing, sound localization, and effortful listening. Importantly, the relative change in modularity of this auditory control network predicts individuals’ listening success. Our findings shed light on how cortical communication dynamics tune selection and comprehension of speech in challenging listening situations and suggest modularity as the network principle of auditory attention. Speech comprehension in noisy, multitalker situations poses a challenge. Successful behavioral adaptation to a listening challenge often requires stronger engagement of auditory spatial attention and context-dependent semantic predictions. Human listeners differ substantially in the degree to which they adapt behaviorally and can listen successfully under such circumstances. How cortical networks embody this adaptation, particularly at the individual level, is currently unknown. We here explain this adaptation from reconfiguration of brain networks for a challenging listening task (i.e., a linguistic variant of the Posner paradigm with concurrent speech) in an age-varying sample of n = 49 healthy adults undergoing resting-state and task fMRI. We here provide evidence for the hypothesis that more successful listeners exhibit stronger task-specific reconfiguration (hence, better adaptation) of brain networks. From rest to task, brain networks become reconfigured toward more localized cortical processing characterized by higher topological segregation. This reconfiguration is dominated by the functional division of an auditory and a cingulo-opercular module and the emergence of a conjoined auditory and ventral attention module along bilateral middle and posterior temporal cortices. Supporting our hypothesis, the degree to which modularity of this frontotemporal auditory control network is increased relative to resting state predicts individuals’ listening success in states of divided and selective attention. Our findings elucidate how fine-tuned cortical communication dynamics shape selection and comprehension of speech. Our results highlight modularity of the auditory control network as a key organizational principle in cortical implementation of auditory spatial attention in challenging listening situations.
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169
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Fong AHC, Yoo K, Rosenberg MD, Zhang S, Li CSR, Scheinost D, Constable RT, Chun MM. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. Neuroimage 2018; 188:14-25. [PMID: 30521950 DOI: 10.1016/j.neuroimage.2018.11.057] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 11/26/2018] [Accepted: 11/30/2018] [Indexed: 11/30/2022] Open
Abstract
Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10-60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.
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Affiliation(s)
| | | | | | - Sheng Zhang
- Department of Psychiatry, Yale School of Medicine, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, USA; Department of Neuroscience, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale University, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, USA; Department of Neuroscience, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale University, USA
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170
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Smith RX, Jann K, Dapretto M, Wang DJJ. Imbalance of Functional Connectivity and Temporal Entropy in Resting-State Networks in Autism Spectrum Disorder: A Machine Learning Approach. Front Neurosci 2018; 12:869. [PMID: 30542259 PMCID: PMC6277800 DOI: 10.3389/fnins.2018.00869] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 11/07/2018] [Indexed: 12/24/2022] Open
Abstract
Background: Two approaches to understanding the etiology of neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) involve network level functional connectivity (FC) and the dynamics of neuronal signaling. The former approach has revealed both increased and decreased FC in individuals with ASD. The latter approach has found high frequency EEG oscillations and higher levels of epilepsy in children with ASD. Together, these findings have led to the hypothesis that atypical excitatory-inhibitory neural signaling may lead to imbalanced association pathways. However, simultaneously reconciling local temporal dynamics with network scale spatial connectivity remains a difficult task and thus empirical support for this hypothesis is lacking. Methods: We seek to fill this gap by combining two powerful resting-state functional MRI (rs-fMRI) methods-functional connectivity (FC) and wavelet-based regularity analysis. Wavelet-based regularity analysis is an entropy measure of the local rs-fMRI time series signal. We examined the relationship between the RSN entropy and integrity in individuals with ASD and controls from the Autism Brain Imaging Data Exchange (ABIDE) cohort using a putative set of 264 functional brain regions-of-interest (ROI). Results: We observed that an imbalance in intra- and inter-network FC across 11 RSNs in ASD individuals (p = 0.002) corresponds to a weakened relationship with RSN temporal entropy (p = 0.02). Further, we observed that an estimated RSN entropy model significantly distinguished ASD from controls (p = 0.01) and was associated with level of ASD symptom severity (p = 0.003). Conclusions: Imbalanced brain connectivity and dynamics at the network level coincides with their decoupling in ASD. The association with ASD symptom severity presents entropy as a potential biomarker.
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Affiliation(s)
- Robert X. Smith
- NeuroImaging Laboratories (NIL) at Washington University School of Medicine, Washington University in Saint Louis, Saint Louis, MO, United States
| | - Kay Jann
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Danny J. J. Wang
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
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171
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Wang H, Xie K, Lian Z, Cui Y, Chen Y, Zhang J, Xie L, Tsien J, Liu T. Large-Scale Circuitry Interactions Upon Earthquake Experiences Revealed by Recurrent Neural Networks. IEEE Trans Neural Syst Rehabil Eng 2018; 26:2115-2125. [PMID: 30296236 PMCID: PMC6298947 DOI: 10.1109/tnsre.2018.2872919] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain dynamics has recently received increasing interest due to its significant importance in basic and clinical neurosciences. However, due to inherent difficulties in both data acquisition and data analysis methods, studies on large-scale brain dynamics of mouse with local field potential (LFP) recording are very rare. In this paper, we did a series of works on modeling large-scale mouse brain dynamic activities responding to fearful earthquake. Based on LFP recording data from 13 brain regions that are closely related to fear learning and memory and the effective Bayesian connectivity change point model, we divided the response time series into four stages: "Before," "Earthquake," "Recovery," and "After." We first reported the changes in power and theta-gamma coupling during stage transitions. Then, a recurrent neural network model was designed to model the functional dynamics in these thirteen brain regions and six frequency bands in response to the fear stimulus. Interestingly, our results showed that the functional brain connectivities in theta and gamma bands exhibited distinct response processes: in theta band, there is a separated-united-separated alternation in whole-brain connectivity and a low-high-low change in connectivity strength; however, gamma bands have a united-separated-united transition and a high-low-high alternation in connectivity pattern and strength. In general, our results offer a novel perspective in studying functional brain dynamics under fearful stimulus and reveal its relationship to the brain's structural connectivity substrates.
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Affiliation(s)
- Han Wang
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (
| | - Kun Xie
- The Brain Decoding Center, Banna Biomedical Research Institute, Yunnan Academy of Science and Technology, Yunnan, China; and Brain and Behavior Discovery Institute, Medical College of Georgia at Augusta University, Augusta, GA, USA ()
| | - Zhichao Lian
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ()
| | - Yan Cui
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China )
| | - Yaowu Chen
- Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Hangzhou, China; and Zhejiang University Embedded System Engineering Research Center, Ministry of Education of China, Hangzhou, China ()
| | - Jing Zhang
- Department of Math and Statistics, Georgia State University, Atlanta, GA ()
| | - Li Xie
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China ()
| | - Joe Tsien
- Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta University, Augusta, GA, USA ()
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, 30602 USA (phone: (706) 542-3478; )
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172
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Matsui T, Murakami T, Ohki K. Mouse optical imaging for understanding resting-state functional connectivity in human fMRI. Commun Integr Biol 2018; 11:e1528821. [PMID: 30534348 PMCID: PMC6284571 DOI: 10.1080/19420889.2018.1528821] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 09/17/2018] [Accepted: 09/19/2018] [Indexed: 11/17/2022] Open
Abstract
Resting-state functional connectivity (FC), which measures the temporal correlation of spontaneous hemodynamic activity between distant brain areas, is a widely accepted method in functional magnetic resonance imaging (fMRI) to assess the connectome of healthy and diseased human brains. A common assumption underlying FC is that it reflects the temporal structure of large-scale neuronal activity that is converted into large-scale hemodynamic activity. However, direct observation of such relationship has been difficult. In this commentary, we describe our recent progress regarding this topic. Recently, transgenic mice that express a genetically encoded calcium indicator (GCaMP) in neocortical neurons are enabling the optical recording of neuronal activity in large-scale with high spatiotemporal resolution. Using these mice, we devised a method to simultaneously monitor neuronal and hemodynamic activity and addressed some key issues related to the neuronal basis of FC. We propose that many important questions about human resting-state fMRI can be answered using GCaMP expressing transgenic mice as a model system.
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Affiliation(s)
- Teppei Matsui
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
| | - Tomonari Murakami
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
| | - Kenichi Ohki
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan.,International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan
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173
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Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS Comput Biol 2018; 14:e1006359. [PMID: 30335761 PMCID: PMC6193609 DOI: 10.1371/journal.pcbi.1006359] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 07/12/2018] [Indexed: 11/28/2022] Open
Abstract
Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Causal interactions depend on both cortical structure and the dynamical state of populations. Activity propagates mainly in the feedback direction, similar to experimental results associated with visual imagery and sleep. The model unifies local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales. Based on our simulations, we hypothesize that in the spontaneous condition the brain operates in a metastable regime where cortico-cortical projections target excitatory and inhibitory populations in a balanced manner that produces substantial inter-area interactions while maintaining global stability. The mammalian cortex fulfills its complex tasks by operating on multiple temporal and spatial scales from single cells to entire areas comprising millions of cells. These multi-scale dynamics are supported by specific network structures at all levels of organization. Since models of cortex hitherto tend to concentrate on a single scale, little is known about how cortical structure shapes the multi-scale dynamics of the network. We here present dynamical simulations of a multi-area network model at neuronal and synaptic resolution with population-specific connectivity based on extensive experimental data which accounts for a wide range of dynamical phenomena. Our model elucidates relationships between local and global scales in cortex and provides a platform for future studies of cortical function.
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Affiliation(s)
- Maximilian Schmidt
- Laboratory for Neural Coding and Brain Computing, RIKEN Center for Brain Science, Wako-Shi, Saitama, Japan
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Rembrandt Bakker
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Kelly Shen
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Gleb Bezgin
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Department of Physics, RWTH Aachen University, Aachen, Germany
| | - Sacha Jennifer van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- * E-mail:
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174
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Khambhati AN, Sizemore AE, Betzel RF, Bassett DS. Modeling and interpreting mesoscale network dynamics. Neuroimage 2018; 180:337-349. [PMID: 28645844 PMCID: PMC5738302 DOI: 10.1016/j.neuroimage.2017.06.029] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/12/2017] [Accepted: 06/14/2017] [Indexed: 11/28/2022] Open
Abstract
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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175
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Vidaurre D, Abeysuriya R, Becker R, Quinn AJ, Alfaro-Almagro F, Smith SM, Woolrich MW. Discovering dynamic brain networks from big data in rest and task. Neuroimage 2018; 180:646-656. [PMID: 28669905 PMCID: PMC6138951 DOI: 10.1016/j.neuroimage.2017.06.077] [Citation(s) in RCA: 163] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 06/12/2017] [Accepted: 06/28/2017] [Indexed: 11/29/2022] Open
Abstract
Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.
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Affiliation(s)
- Diego Vidaurre
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK.
| | - Romesh Abeysuriya
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Robert Becker
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Fidel Alfaro-Almagro
- Oxford University Centre for Functional MRI of the Brain (FMRIB), University of Oxford, UK
| | - Stephen M Smith
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, UK; Oxford University Centre for Functional MRI of the Brain (FMRIB), University of Oxford, UK
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176
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Kucyi A, Tambini A, Sadaghiani S, Keilholz S, Cohen JR. Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity. Netw Neurosci 2018; 2:397-417. [PMID: 30465033 PMCID: PMC6195165 DOI: 10.1162/netn_a_00037] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/23/2017] [Indexed: 01/20/2023] Open
Abstract
In cognitive neuroscience, focus is commonly placed on associating brain function with changes in objectively measured external stimuli or with actively generated cognitive processes. In everyday life, however, many forms of cognitive processes are initiated spontaneously, without an individual's active effort and without explicit manipulation of behavioral state. Recently, there has been increased emphasis, especially in functional neuroimaging research, on spontaneous correlated activity among spatially segregated brain regions (intrinsic functional connectivity) and, more specifically, on intraindividual fluctuations of such correlated activity on various time scales (time-varying functional connectivity). In this Perspective, we propose that certain subtypes of spontaneous cognitive processes are detectable in time-varying functional connectivity measurements. We define these subtypes of spontaneous cognitive processes and review evidence of their representations in time-varying functional connectivity from studies of attentional fluctuations, memory reactivation, and effects of baseline states on subsequent perception. Moreover, we describe how these studies are critical to validating the use of neuroimaging tools (e.g., fMRI) for assessing ongoing brain network dynamics. We conclude that continued investigation of the behavioral relevance of time-varying functional connectivity will be beneficial both in the development of comprehensive neural models of cognition, and in informing on best practices for studying brain network dynamics.
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Affiliation(s)
- Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Arielle Tambini
- Department of Psychology, and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Sepideh Sadaghiani
- Department of Psychology, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL, USA
| | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, NC, USA
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177
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Cavanna F, Vilas MG, Palmucci M, Tagliazucchi E. Dynamic functional connectivity and brain metastability during altered states of consciousness. Neuroimage 2018; 180:383-395. [DOI: 10.1016/j.neuroimage.2017.09.065] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/01/2017] [Accepted: 09/29/2017] [Indexed: 11/16/2022] Open
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178
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Xie H, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Calhoun VD, Chen G, Damaraju E, Liu X, Mitra S. Time-varying whole-brain functional network connectivity coupled to task engagement. Netw Neurosci 2018; 3:49-66. [PMID: 30793073 PMCID: PMC6326730 DOI: 10.1162/netn_a_00051] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 03/16/2018] [Indexed: 11/30/2022] Open
Abstract
Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A. Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Xiangyu Liu
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
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179
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Fukushima M, Sporns O. Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity. PLoS Comput Biol 2018; 14:e1006497. [PMID: 30252835 PMCID: PMC6173440 DOI: 10.1371/journal.pcbi.1006497] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 10/05/2018] [Accepted: 09/10/2018] [Indexed: 11/19/2022] Open
Abstract
Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity. Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging, as well as its time-resolved fluctuations on a time scale of tens of seconds. However, comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity, such as fluctuations between segregated and integrated topology or between high and low modularity topology. Since these global network-level fluctuations have been shown to be related to human cognition and behavior, there is an emerging need for clarifying their reproducibility with computational models. To address this problem, we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data, and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations. Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity. By performing model parameter search, we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data. Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible, although their spatial patterns in functional connectivity did not perfectly matched. These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration, whereas additional factors, such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity, would be necessary for fully reproducing the spatial patterning associated with these fluctuations. In human neuroscience, there is growing interest in temporal fluctuations in coactivation patterns of resting-state brain activity. To elucidate generative mechanisms of these fluctuations, theoretical studies try to reproduce their empirical properties by simulations using dynamic models of large-scale spontaneous brain activity. However, evaluations of the reproducibility have not been extended so far to the fluctuations in global network topology of coactivation patterns, recently shown to be related to human cognition and behavior. Here we examine the extent to which a stationary model typically used for simulating resting-state activity can reproduce spatial and temporal patterns of the empirically observed fluctuations in global network topology. We found that such a model successfully reproduced the magnitude of empirical fluctuations as well as their temporal dynamics, whereas their spatial patterning was not fully accounted for by the simulation. Our results suggest that stationary models can explain many empirical properties in the fluctuations in global network topology, while modeling of non-stationary dynamics and/or greater estimation accuracy of anatomical connections underlying the simulation would be required for complete replication. This finding provides new insights into how fluctuations in global network topology of coactivation patterns emerge in the human brain.
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Affiliation(s)
- Makoto Fukushima
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- * E-mail:
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Indiana University Network Science Institute, Bloomington, Indiana, United States of America
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180
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Harms MP, Somerville LH, Ances BM, Andersson J, Barch DM, Bastiani M, Bookheimer SY, Brown TB, Buckner RL, Burgess GC, Coalson TS, Chappell MA, Dapretto M, Douaud G, Fischl B, Glasser MF, Greve DN, Hodge C, Jamison KW, Jbabdi S, Kandala S, Li X, Mair RW, Mangia S, Marcus D, Mascali D, Moeller S, Nichols TE, Robinson EC, Salat DH, Smith SM, Sotiropoulos SN, Terpstra M, Thomas KM, Tisdall MD, Ugurbil K, van der Kouwe A, Woods RP, Zöllei L, Van Essen DC, Yacoub E. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects. Neuroimage 2018; 183:972-984. [PMID: 30261308 DOI: 10.1016/j.neuroimage.2018.09.060] [Citation(s) in RCA: 263] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/17/2018] [Accepted: 09/20/2018] [Indexed: 12/21/2022] Open
Abstract
The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5-21), and HCP-A is enrolling 1200+ healthy adults (ages 36-100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22-35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.
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Affiliation(s)
- Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
| | - Leah H Somerville
- Department of Psychology, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Beau M Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Timothy B Brown
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Randy L Buckner
- Department of Psychology, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gregory C Burgess
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy S Coalson
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael A Chappell
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA; St. Luke's Hospital, St. Louis, MO, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Cynthia Hodge
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Xiufeng Li
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Ross W Mair
- Center for Brain Science, Harvard University, Cambridge, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Daniel Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniele Mascali
- Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche "Enrico Fermi", Rome, Italy
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Department of Statistics, University of Warwick, Coventry, UK
| | - Emma C Robinson
- Department of Biomedical Engineering, King's College London, London, UK
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Kathleen M Thomas
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Roger P Woods
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA; Department of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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181
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Kaiser RH, Snyder HR, Goer F, Clegg R, Ironside M, Pizzagalli DA. Attention Bias in Rumination and Depression: Cognitive Mechanisms and Brain Networks. Clin Psychol Sci 2018; 6:765-782. [PMID: 31106040 DOI: 10.1177/2167702618797935] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Depressed individuals exhibit biased attention to negative emotional information. However, much remains unknown about (1) the neurocognitive mechanisms of attention bias (e.g., qualities of negative information that evoke attention bias, or functional brain network dynamics that may reflect a propensity for biased attention) and (2) distinctions in the types of attention bias related to different dimensions of depression (e.g., ruminative depression). Here, in 50 women, clinical depression was associated with facilitated processing of negative information only when such information was self-descriptive and task-relevant. However, among depressed individuals, trait rumination was associated with biases towards negative self-descriptive information regardless of task goals, especially when negative self-descriptive material was paired with self-referential images that should be ignored. Attention biases in ruminative depression were mediated by dynamic variability in frontoinsular resting-state functional connectivity. These findings highlight potential cognitive and functional network mechanisms of attention bias specifically related to the ruminative dimension of depression.
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Affiliation(s)
- Roselinde H Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder
| | | | - Franziska Goer
- Center for Depression, Anxiety and Stress Research, McLean Hospital
| | - Rachel Clegg
- Center for Depression, Anxiety and Stress Research, McLean Hospital
| | - Manon Ironside
- Center for Depression, Anxiety and Stress Research, McLean Hospital
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital.,Mclean Imaging Center, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School
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182
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Nalci A, Rao BD, Liu TT. Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI. Neuroimage 2018; 184:1005-1031. [PMID: 30223062 DOI: 10.1016/j.neuroimage.2018.09.024] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/04/2018] [Accepted: 09/08/2018] [Indexed: 11/16/2022] Open
Abstract
In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.
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Affiliation(s)
- Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Bhaskar D Rao
- Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Departments of Radiology, Psychiatry and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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183
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Miller RL, Abrol A, Adali T, Levin-Schwarz Y, Calhoun VD. Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations. Front Neurosci 2018; 12:551. [PMID: 30237758 PMCID: PMC6135983 DOI: 10.3389/fnins.2018.00551] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 07/20/2018] [Indexed: 12/16/2022] Open
Abstract
Studies of resting state functional MRI (rs-fRMI) are increasingly focused on “dynamics”, or on those properties of brain activation that manifest and vary on timescales shorter than the scan's full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this article. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e., their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e., timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the type of epochal signal variation that is often viewed as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations vs. across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence of functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.
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Affiliation(s)
- Robyn L Miller
- The Mind Research Network, Albuquerque, NM, United States
| | - Anees Abrol
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Yuri Levin-Schwarz
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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184
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Kottaram A, Johnston L, Ganella E, Pantelis C, Kotagiri R, Zalesky A. Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis. Hum Brain Mapp 2018; 39:3663-3681. [PMID: 29749660 PMCID: PMC6866493 DOI: 10.1002/hbm.24202] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 04/18/2018] [Accepted: 04/19/2018] [Indexed: 02/01/2023] Open
Abstract
Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
| | - Leigh Johnston
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
- Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3010, Australia
- Florey Institute for Neurosciences and Mental health, Parkville, Victoria, 3052, Australia
| | - Eleni Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
- Cooperative Research Centre for Mental Health, Carlton, Victoria, 3053, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
- Department of Psychiatry, The University of Melbourne, Victoria, 3010, Australia
- Florey Institute for Neurosciences and Mental health, Parkville, Victoria, 3052, Australia
- North Western Mental Health, Melbourne Health, Parkville, Victoria, Australia
- Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3053, Australia
- Cooperative Research Centre for Mental Health, Carlton, Victoria, 3053, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
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185
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Chen RH, Ito T, Kulkarni KR, Cole MW. The Human Brain Traverses a Common Activation-Pattern State Space Across Task and Rest. Brain Connect 2018; 8:429-443. [PMID: 29999413 PMCID: PMC6152856 DOI: 10.1089/brain.2018.0586] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Much of our lives are spent in unconstrained rest states, yet cognitive brain processes are primarily investigated using task-constrained states. It may be possible to utilize the insights gained from experimental control of task processes as reference points for investigating unconstrained rest. To facilitate comparison of rest and task functional magnetic resonance imaging data, we focused on activation amplitude patterns, commonly used for task but not rest analyses. During rest, we identified spontaneous changes in temporally extended whole-brain activation-pattern states. This revealed a hierarchical organization of rest states. The top consisted of two competing states consistent with previously identified "task-positive" and "task-negative" activation patterns. These states were composed of more specific states that repeated over time and across individuals. Contrasting with the view that rest consists of only task-negative states, task-positive states occurred 40% of the time while individuals "rested," suggesting task-focused activity may occur during rest. Together our results suggest that brain activation dynamics form a general hierarchy across task and rest, with a small number of dominant general states reflecting basic functional modes and a variety of specific states potentially reflecting a wide variety of cognitive processes.
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Affiliation(s)
- Richard H. Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey
| | - Kaustubh R. Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey
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186
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Shine JM, van den Brink RL, Hernaus D, Nieuwenhuis S, Poldrack RA. Catecholaminergic manipulation alters dynamic network topology across cognitive states. Netw Neurosci 2018; 2:381-396. [PMID: 30294705 PMCID: PMC6145851 DOI: 10.1162/netn_a_00042] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 01/02/2018] [Indexed: 12/29/2022] Open
Abstract
The human brain is able to flexibly adapt its information processing capacity to meet a variety of cognitive challenges. Recent evidence suggests that this flexibility is reflected in the dynamic reorganization of the functional connectome. The ascending catecholaminergic arousal systems of the brain are a plausible candidate mechanism for driving alterations in network architecture, enabling efficient deployment of cognitive resources when the environment demands them. We tested this hypothesis by analyzing both resting-state and task-based fMRI data following the administration of atomoxetine, a noradrenaline reuptake inhibitor, compared with placebo, in two separate human fMRI studies. Our results demonstrate that the manipulation of central catecholamine levels leads to a reorganization of the functional connectome in a manner that is sensitive to ongoing cognitive demands. There is emerging evidence that the flexible network structure of the brain is related to activity within the ascending arousal systems of the brain, such as the noradrenergic locus coeruleus. Here, we explored the role of catecholaminergic activity on network architecture by analyzing the graph structure of the brain measured using functional MRI following the administration of atomoxetine, a selective noradrenaline reuptake inhibitor. We estimated functional network topology in two double-blind, placebo-controlled datasets: one from the resting state and another from a parametric N-back task. Our results demonstrate that the nature of catecholaminergic network reconfiguration is differentially related to cognitive state and provide confirmatory evidence for the hypothesis that the functional network signature of the brain is sensitive to the ascending catecholaminergic arousal system.
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Affiliation(s)
- James M Shine
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Dennis Hernaus
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, MD, USA
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187
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Somerville LH, Bookheimer SY, Buckner RL, Burgess GC, Curtiss SW, Dapretto M, Elam JS, Gaffrey MS, Harms MP, Hodge C, Kandala S, Kastman EK, Nichols TE, Schlaggar BL, Smith SM, Thomas KM, Yacoub E, Van Essen DC, Barch DM. The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds. Neuroimage 2018; 183:456-468. [PMID: 30142446 DOI: 10.1016/j.neuroimage.2018.08.050] [Citation(s) in RCA: 147] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/18/2018] [Accepted: 08/20/2018] [Indexed: 12/14/2022] Open
Abstract
Recent technological and analytical progress in brain imaging has enabled the examination of brain organization and connectivity at unprecedented levels of detail. The Human Connectome Project in Development (HCP-D) is exploiting these tools to chart developmental changes in brain connectivity. When complete, the HCP-D will comprise approximately ∼1750 open access datasets from 1300 + healthy human participants, ages 5-21 years, acquired at four sites across the USA. The participants are from diverse geographical, ethnic, and socioeconomic backgrounds. While most participants are tested once, others take part in a three-wave longitudinal component focused on the pubertal period (ages 9-17 years). Brain imaging sessions are acquired on a 3 T Siemens Prisma platform and include structural, functional (resting state and task-based), diffusion, and perfusion imaging, physiological monitoring, and a battery of cognitive tasks and self-reports. For minors, parents additionally complete a battery of instruments to characterize cognitive and emotional development, and environmental variables relevant to development. Participants provide biological samples of blood, saliva, and hair, enabling assays of pubertal hormones, health markers, and banked DNA samples. This paper outlines the overarching aims of the project, the approach taken to acquire maximally informative data while minimizing participant burden, preliminary analyses, and discussion of the intended uses and limitations of the dataset.
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Affiliation(s)
- Leah H Somerville
- Department of Psychology, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA.
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Randy L Buckner
- Department of Psychology, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Gregory C Burgess
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Sandra W Curtiss
- Department of Neuroscience, Washington University Medical School, St. Louis, MO, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Jennifer Stine Elam
- Department of Neuroscience, Washington University Medical School, St. Louis, MO, USA
| | - Michael S Gaffrey
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Cynthia Hodge
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Erik K Kastman
- Department of Psychology, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Department of Statistics, University of Warwick, Coventry, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Bradley L Schlaggar
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA; Department of Neuroscience, Washington University Medical School, St. Louis, MO, USA; Department of Neurology, Washington University Medical School, St. Louis, MO, USA; Department of Pediatrics, Washington University Medical School, St. Louis, MO, USA; Department of Radiology, Washington University Medical School, St. Louis, MO, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Kathleen M Thomas
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University Medical School, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA; Department of Radiology, Washington University Medical School, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
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188
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Du Y, Fu Z, Calhoun VD. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging. Front Neurosci 2018; 12:525. [PMID: 30127711 PMCID: PMC6088208 DOI: 10.3389/fnins.2018.00525] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022] Open
Abstract
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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189
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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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190
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Schwab S, Harbord R, Zerbi V, Elliott L, Afyouni S, Smith JQ, Woolrich MW, Smith SM, Nichols TE. Directed functional connectivity using dynamic graphical models. Neuroimage 2018; 175:340-353. [PMID: 29625233 PMCID: PMC6153304 DOI: 10.1016/j.neuroimage.2018.03.074] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 03/26/2018] [Accepted: 03/30/2018] [Indexed: 10/30/2022] Open
Abstract
There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.
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Affiliation(s)
- Simon Schwab
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, United Kingdom; Department of Statistics, University of Warwick, United Kingdom; Institute of Digital Healthcare, WMG, University of Warwick, United Kingdom.
| | - Ruth Harbord
- MOAC Doctoral Training Centre, University of Warwick, United Kingdom
| | - Valerio Zerbi
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Switzerland
| | - Lloyd Elliott
- Department of Statistics, University of Oxford, United Kingdom
| | - Soroosh Afyouni
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Jim Q Smith
- Department of Statistics, University of Warwick, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, United Kingdom; Department of Statistics, University of Warwick, United Kingdom; Institute of Digital Healthcare, WMG, University of Warwick, United Kingdom
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191
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Gozdas E, Parikh NA, Merhar SL, Tkach JA, He L, Holland SK. Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments? Brain Struct Funct 2018; 223:3665-3680. [PMID: 29992470 DOI: 10.1007/s00429-018-1707-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 06/24/2018] [Indexed: 12/12/2022]
Abstract
Very preterm infants (≤ 31 weeks gestational age) are at high risk for brain injury and delayed development. Applying functional connectivity and graph theory methods to resting state MRI data (fcMRI), we tested the hypothesis that preterm infants would demonstrate alterations in connectivity measures both globally and in specific networks related to motor, language and cognitive function, even when there is no anatomical imaging evidence of injury. Fifty-one healthy full-term controls and 24 very preterm infants without significant neonatal brain injury, were evaluated at term-equivalent age with fcMRI. Preterm subjects showed lower functional connectivity from regions associated with motor, cognitive, language and executive function, than term controls. Examining brain networks using graph theory measures of functional connectivity, very preterm infants also exhibited lower rich-club coefficient and assortativity but higher small-worldness and no significant difference in modularity when compared to term infants. The findings provide evidence that functional connectivity exhibits deficits soon after birth in very preterm infants in key brain networks responsible for motor, language and executive functions, even in the absence of anatomical lesions. These functional network measures could serve as prognostic biomarkers for later developmental disabilities and guide decisions about early interventions.
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Affiliation(s)
- Elveda Gozdas
- Department of Physics, University of Cincinnati, Cincinnati, OH, USA.,Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Nehal A Parikh
- Department of Pediatrics, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, Center for Perinatal Research, Nationwide Children's Hospital, Columbus, OH, USA
| | - Stephanie L Merhar
- Department of Pediatrics, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jean A Tkach
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Lili He
- Department of Pediatrics, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Medpace Inc., Cincinnati, OH, USA
| | - Scott K Holland
- Department of Physics, University of Cincinnati, Cincinnati, OH, USA. .,Medpace Inc., Cincinnati, OH, USA.
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192
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Tompson SH, Falk EB, Vettel JM, Bassett DS. Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience. PERSONALITY NEUROSCIENCE 2018; 1:e5. [PMID: 30221246 PMCID: PMC6133307 DOI: 10.1017/pen.2018.4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/06/2018] [Indexed: 12/11/2022]
Abstract
Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from non-invasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.
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Affiliation(s)
- Steven H. Tompson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- US Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, USA
| | - Emily B. Falk
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Jean M. Vettel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- US Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA
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193
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Solo V, Poline JB, Lindquist MA, Simpson SL, Bowman FD, Chung MK, Cassidy B. Connectivity in fMRI: Blind Spots and Breakthroughs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1537-1550. [PMID: 29969406 PMCID: PMC6291757 DOI: 10.1109/tmi.2018.2831261] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.
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194
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Noble S, Spann MN, Tokoglu F, Shen X, Constable RT, Scheinost D. Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility. Cereb Cortex 2018; 27:5415-5429. [PMID: 28968754 PMCID: PMC6248395 DOI: 10.1093/cercor/bhx230] [Citation(s) in RCA: 225] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 08/23/2017] [Indexed: 12/15/2022] Open
Abstract
Best practices are currently being developed for the acquisition and processing of
resting-state magnetic resonance imaging data used to estimate brain functional
organization—or “functional connectivity.” Standards have been proposed based on
test–retest reliability, but open questions remain. These include how amount of data per
subject influences whole-brain reliability, the influence of increasing runs versus
sessions, the spatial distribution of reliability, the reliability of multivariate
methods, and, crucially, how reliability maps onto prediction of behavior. We collected a
dataset of 12 extensively sampled individuals (144 min data each across 2 identically
configured scanners) to assess test–retest reliability of whole-brain connectivity within
the generalizability theory framework. We used Human Connectome Project data to replicate
these analyses and relate reliability to behavioral prediction. Overall, the historical
5-min scan produced poor reliability averaged across connections. Increasing the number of
sessions was more beneficial than increasing runs. Reliability was lowest for subcortical
connections and highest for within-network cortical connections. Multivariate reliability
was greater than univariate. Finally, reliability could not be used to improve prediction;
these findings are among the first to underscore this distinction for functional
connectivity. A comprehensive understanding of test–retest reliability, including its
limitations, supports the development of best practices in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
| | - Marisa N Spann
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Fuyuze Tokoglu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
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195
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Keerativittayayut R, Aoki R, Sarabi MT, Jimura K, Nakahara K. Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance. eLife 2018; 7:32696. [PMID: 29911970 PMCID: PMC6039182 DOI: 10.7554/elife.32696] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 06/16/2018] [Indexed: 12/19/2022] Open
Abstract
Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.
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Affiliation(s)
| | - Ryuta Aoki
- Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan
| | | | - Koji Jimura
- Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan.,Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Kiyoshi Nakahara
- School of Information, Kochi University of Technology, Kochi, Japan.,Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan
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196
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Mohammadi-Nejad AR, Mahmoudzadeh M, Hassanpour MS, Wallois F, Muzik O, Papadelis C, Hansen A, Soltanian-Zadeh H, Gelovani J, Nasiriavanaki M. Neonatal brain resting-state functional connectivity imaging modalities. PHOTOACOUSTICS 2018; 10:1-19. [PMID: 29511627 PMCID: PMC5832677 DOI: 10.1016/j.pacs.2018.01.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 01/12/2018] [Accepted: 01/27/2018] [Indexed: 05/12/2023]
Abstract
Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.
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Affiliation(s)
- Ali-Reza Mohammadi-Nejad
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA
| | - Mahdi Mahmoudzadeh
- INSERM, U1105, Université de Picardie, CURS, F80036, Amiens, France
- INSERM U1105, Exploration Fonctionnelles du Système Nerveux Pédiatrique, South University Hospital, F80054, Amiens Cedex, France
| | | | - Fabrice Wallois
- INSERM, U1105, Université de Picardie, CURS, F80036, Amiens, France
- INSERM U1105, Exploration Fonctionnelles du Système Nerveux Pédiatrique, South University Hospital, F80054, Amiens Cedex, France
| | - Otto Muzik
- Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Christos Papadelis
- Boston Children’s Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anne Hansen
- Boston Children’s Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Juri Gelovani
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA
- Molecular Imaging Program, Barbara Ann Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
| | - Mohammadreza Nasiriavanaki
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
- Molecular Imaging Program, Barbara Ann Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
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197
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Heitmann S, Breakspear M. Putting the "dynamic" back into dynamic functional connectivity. Netw Neurosci 2018; 2:150-174. [PMID: 30215031 PMCID: PMC6130444 DOI: 10.1162/netn_a_00041] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Accepted: 12/30/2017] [Indexed: 01/17/2023] Open
Abstract
The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term "dynamic functional connectivity" implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.
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198
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Sitnikova TA, Hughes JW, Ahlfors SP, Woolrich MW, Salat DH. Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease. NEUROIMAGE-CLINICAL 2018; 20:128-152. [PMID: 30094163 PMCID: PMC6077178 DOI: 10.1016/j.nicl.2018.05.028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 04/20/2018] [Accepted: 05/20/2018] [Indexed: 10/28/2022]
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative condition that can lead to severe cognitive and functional deterioration. Functional magnetic resonance imaging (fMRI) revealed abnormalities in AD in intrinsic synchronization between spatially separate regions in the so-called default mode network (DMN) of the brain. To understand the relationship between this disruption in large-scale synchrony and the cognitive impairment in AD, it is critical to determine whether and how the deficit in the low frequency hemodynamic fluctuations recorded by fMRI translates to much faster timescales of memory and other cognitive processes. The present study employed magnetoencephalography (MEG) and a Hidden Markov Model (HMM) approach to estimate spontaneous synchrony variations in the functional neural networks with high temporal resolution. In a group of cognitively healthy (CH) older adults, we found transient (mean duration of 150-250 ms) network activity states, which were visited in a rapid succession, and were characterized by spatially coordinated changes in the amplitude of source-localized electrophysiological oscillations. The inferred states were similar to those previously observed in younger participants using MEG, and the estimated cortical source distributions of the state-specific activity resembled the classic functional neural networks, such as the DMN. In patients with AD, inferred network states were different from those of the CH group in short-scale timing and oscillatory features. The state of increased oscillatory amplitudes in the regions overlapping the DMN was visited less often in AD and for shorter periods of time, suggesting that spontaneous synchronization in this network was less likely and less stable in the patients. During the visits to this state, in some DMN nodes, the amplitude change in the higher-frequency (8-30 Hz) oscillations was less robust in the AD than CH group. These findings highlight relevance of studying short-scale temporal evolution of spontaneous activity in functional neural networks to understanding the AD pathophysiology. Capacity of flexible intrinsic synchronization in the DMN may be crucial for memory and other higher cognitive functions. Our analysis yielded metrics that quantify distinct features of the neural synchrony disorder in AD and may offer sensitive indicators of the neural network health for future investigations.
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Affiliation(s)
- Tatiana A Sitnikova
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Jeremy W Hughes
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.
| | - Seppo P Ahlfors
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Mark W Woolrich
- Oxford Center for Human Brain Activity, University of Oxford, Oxford OX3 7JX, UK.
| | - David H Salat
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
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199
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Keilholz S, Caballero-Gaudes C, Bandettini P, Deco G, Calhoun V. Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions. Brain Connect 2018; 7:465-481. [PMID: 28874061 DOI: 10.1089/brain.2017.0543] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.
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Affiliation(s)
- Shella Keilholz
- 1 Department of Biomedical Engineering, Emory University/Georgia Institute of Technology , Atlanta, Georgia
| | | | - Peter Bandettini
- 3 Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland.,4 Functional MRI Core Facility, NIMH, NIH, Bethesda, Maryland
| | - Gustavo Deco
- 5 Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain .,6 Institució Catalana de la Recerca i Estudis Avançats (ICREA) , Barcelona, Spain.,7 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, Germany .,8 School of Psychological Sciences, Monash University , Melbourne, Australia
| | - Vince Calhoun
- 9 The Mind Research Network, Albuquerque, New Mexico.,10 Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, New Mexico
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200
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Mash LE, Reiter MA, Linke AC, Townsend J, Müller RA. Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective. Dev Neurobiol 2018; 78:456-473. [PMID: 29266810 PMCID: PMC5897150 DOI: 10.1002/dneu.22570] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/18/2017] [Accepted: 12/18/2017] [Indexed: 12/22/2022]
Abstract
Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.
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Affiliation(s)
- Lisa E. Mash
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Maya A. Reiter
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Annika C. Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Jeanne Townsend
- University of California, San Diego, Department of Neurosciences
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
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