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Lee K, Ji JL, Fonteneau C, Berkovitch L, Rahmati M, Pan L, Repovš G, Krystal JH, Murray JD, Anticevic A. Human brain state dynamics reflect individual neuro-phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.557763. [PMID: 37790400 PMCID: PMC10542143 DOI: 10.1101/2023.09.18.557763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.
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Affiliation(s)
- Kangjoo Lee
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Lucie Berkovitch
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Université Paris Cité, 15 Rue de l'École de Médecine, F-75006 Paris, France
| | - Masih Rahmati
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
- Department of Physics, Yale University, New Haven, CT, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University School of Medicine, New Haven, CT, USA
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Ensel S, Uhrig L, Ozkirli A, Hoffner G, Tasserie J, Dehaene S, Van De Ville D, Jarraya B, Pirondini E. Transient brain activity dynamics discriminate levels of consciousness during anesthesia. Commun Biol 2024; 7:716. [PMID: 38858589 PMCID: PMC11164921 DOI: 10.1038/s42003-024-06335-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 05/15/2024] [Indexed: 06/12/2024] Open
Abstract
The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.
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Affiliation(s)
- Scott Ensel
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lynn Uhrig
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Department of Anesthesiology and Critical Care, Necker Hospital, AP-HP, Université Paris Cité, Paris, France
| | - Ayberk Ozkirli
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Guylaine Hoffner
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
| | - Jordy Tasserie
- Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Collège de France, Paris, France
| | - Dimitri Van De Ville
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Béchir Jarraya
- NeuroSpin Center, Institute of BioImaging Commissariat à l'Energie Atomique, Gif/Yvette, France
- Cognitive Neuroimaging Unit, INSERM, U992, Gif/Yvette, France
- Université Paris-Saclay (UVSQ), Saclay, France
- Neuroscience Pole, Foch Hospital, Suresnes, France
| | - Elvira Pirondini
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
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3
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Wang S, Li T, He H, Li Y. Dynamical changes of interaction across functional brain communities during propofol-induced sedation. Cereb Cortex 2024; 34:bhae263. [PMID: 38918077 DOI: 10.1093/cercor/bhae263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.
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Affiliation(s)
- Shengpei Wang
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
| | - Tianzuo Li
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, No. 10 Yangfangdian Tieyi Rd, Haidian District, Beijing 100038, PR China
| | - Huiguang He
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 1 Yanqihu East Road, Huairou District, Beijing 101408, PR China
| | - Yun Li
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, South Fourth Ring West Road, Fengtai District, Beijing 100070, PR China
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4
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Osaka M, Minamoto T, Ikeda T, Nakae A, Hagihira S, Ito H, Fujino Y, Mashimo T. The arousal level of consciousness required for working memory performance: An anaesthesia study. Eur J Neurosci 2024; 59:3151-3161. [PMID: 38752321 DOI: 10.1111/ejn.16383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/09/2024] [Accepted: 04/21/2024] [Indexed: 06/15/2024]
Abstract
Regarding the stage of arousal level required for working memory to function properly, limited studies have been conducted on changes in working memory performance when the arousal level of consciousness decreases. This study aimed to experimentally clarify the stages of consciousness necessary for optimal working memory function. In this experiment, the sedation levels were changed step-by-step using anaesthesia, and the performance accuracy during the execution of working memory was assessed using a dual-task paradigm. Participants were required to categorize and remember words in a specific target category. Categorization performance was measured across four different sedative phases: before anaesthesia (baseline), and deep, moderate and light stages of sedation. Short-delay recognition tasks were performed under these four sedative stages, followed by long-delay recognition tasks after participants recovered from sedation. The results of the short-delay recognition task showed that the performance was lowest at the deep stage. The performance of the moderate stage was lower than the baseline. In the long-delay recognition task, the performance under moderate sedation was lower than that under baseline and light sedation. In addition, the performance under light sedation was lower than that under baseline. These results suggest that task performance becomes difficult under half sedation and that transferring information to long-term memory is difficult even under one-quarter sedation.
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Affiliation(s)
- Mariko Osaka
- Graduate School of Human Sciences, Osaka University, Suita, Osaka, Japan
| | - Takehiro Minamoto
- Graduate School of Human Sciences, Osaka University, Suita, Osaka, Japan
| | - Takashi Ikeda
- Graduate School of Human Sciences, Osaka University, Suita, Osaka, Japan
| | - Aya Nakae
- Department of Anesthesiology and Intensive Care, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Satoshi Hagihira
- Department of Anesthesiology and Intensive Care, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hiroshi Ito
- Department of Anesthesiology and Intensive Care, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yuji Fujino
- Department of Anesthesiology and Intensive Care, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takashi Mashimo
- Department of Anesthesiology and Intensive Care, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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5
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Li L, Zheng Q, Xue Y, Bai M, Mu Y. Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02474-y. [PMID: 38814465 DOI: 10.1007/s00787-024-02474-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.
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Affiliation(s)
- Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Qingyu Zheng
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, People's Republic of China
| | - Yang Xue
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Miaoshui Bai
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Yueming Mu
- Department of Dermatology, The First Hospital of Jilin University, Jilin University, 71 Xinmin Street, Changchun, 130021, People's Republic of China.
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6
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Fermin ASR, Sasaoka T, Maekawa T, Ono K, Chan HL, Yamawaki S. Insula-cortico-subcortical networks predict interoceptive awareness and stress resilience. Asian J Psychiatr 2024; 95:103991. [PMID: 38484483 DOI: 10.1016/j.ajp.2024.103991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/25/2024] [Accepted: 02/28/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Interoception, the neural sensing of visceral signals, and interoceptive awareness (IA), the conscious perception of interoception, are crucial for life survival functions and mental health. Resilience, the capacity to overcome adversity, has been associated with reduced interoceptive disturbances. Here, we sought evidence for our Insula Modular Active Control (IMAC) model that suggest that the insula, a brain region specialized in the processing of interoceptive information, realizes IA and contributes to resilience and mental health via cortico-subcortical connections. METHODS 64 healthy participants (32 females; ages 18-34 years) answered questionnaires that assess IA and resilience. Mental health was evaluated with the Beck Depression Inventory II that assesses depressive mood. Participants also underwent a 15 minute resting-state functional resonance imaging session. Pearson correlations and mediation analyses were used to investigate the relationship between IA and resilience and their contributions to depressive mood. We then performed insula seed-based functional connectivity analyzes to identify insula networks involved in IA, resilience and depressive mood. RESULTS We first demonstrated that resilience mediates the relationship between IA and depressive mood. Second, shared and distinct intra-insula, insula-cortical and insula-subcortical networks were associated with IA, resilience and also predicted the degree of experienced depressive mood. Third, while resilience was associated with stronger insula-precuneus, insula-cerebellum and insula-prefrontal networks, IA was linked with stronger intra-insula, insula-striatum and insula-motor networks. CONCLUSIONS Our findings help understand the roles of insula-cortico-subcortical networks in IA and resilience. These results also highlight the potential use of insula networks as biomarkers for depression prediction.
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Affiliation(s)
- Alan S R Fermin
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan.
| | - Takafumi Sasaoka
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Toru Maekawa
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Kentaro Ono
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Hui-Ling Chan
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
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7
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Ke M, Hou L, Liu G. The co-activation patterns of multiple brain regions in Juvenile Myoclonic Epilepsy. Cogn Neurodyn 2024; 18:337-347. [PMID: 38699614 PMCID: PMC11061087 DOI: 10.1007/s11571-022-09838-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 06/09/2022] [Accepted: 06/17/2022] [Indexed: 11/03/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, 730050 Lanzhou, China
| | - Lei Hou
- School of Computer and Communication, Lanzhou University of Technology, 730050 Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, 730030 Lanzhou, China
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8
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Apablaza-Yevenes DE, Corsi-Cabrera M, Martinez-Guerrero A, Northoff G, Romaniello C, Farinelli M, Bertoletti E, Müller MF, Muñoz-Torres Z. Stationary stable cross-correlation pattern and task specific deviations in unresponsive wakefulness syndrome as well as clinically healthy subjects. PLoS One 2024; 19:e0300075. [PMID: 38489260 PMCID: PMC10942032 DOI: 10.1371/journal.pone.0300075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Brain dynamics is highly non-stationary, permanently subject to ever-changing external conditions and continuously monitoring and adjusting internal control mechanisms. Finding stationary structures in this system, as has been done recently, is therefore of great importance for understanding fundamental dynamic trade relationships. Here we analyse electroencephalographic recordings (EEG) of 13 subjects with unresponsive wakefulness syndrome (UWS) during rest and while being influenced by different acoustic stimuli. We compare the results with a control group under the same experimental conditions and with clinically healthy subjects during overnight sleep. The main objective of this study is to investigate whether a stationary correlation pattern is also present in the UWS group, and if so, to what extent this structure resembles the one found in healthy subjects. Furthermore, we extract transient dynamical features via specific deviations from the stationary interrelation pattern. We find that (i) the UWS group is more heterogeneous than the two groups of healthy subjects, (ii) also the EEGs of the UWS group contain a stationary cross-correlation pattern, although it is less pronounced and shows less similarity to that found for healthy subjects and (iii) deviations from the stationary pattern are notably larger for the UWS than for the two groups of healthy subjects. The results suggest that the nervous system of subjects with UWS receive external stimuli but show an overreaching reaction to them, which may disturb opportune information processing.
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Affiliation(s)
- David E. Apablaza-Yevenes
- Instituto de Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Morelos, México
| | - María Corsi-Cabrera
- Unidad de Investigación en Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, People’s Republic of China
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
| | | | | | | | - Markus F. Müller
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Morelos, México
- Centro Internacional de Ciencias A.C., Morelos, México
| | - Zeidy Muñoz-Torres
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
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9
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Clancy KJ, Devignes Q, Ren B, Pollmann Y, Nielsen SR, Howell K, Kumar P, Belleau EL, Rosso IM. Spatiotemporal dynamics of hippocampal-cortical networks underlying the unique phenomenological properties of trauma-related intrusive memories. Mol Psychiatry 2024:10.1038/s41380-024-02486-9. [PMID: 38454081 DOI: 10.1038/s41380-024-02486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Trauma-related intrusive memories (TR-IMs) possess unique phenomenological properties that contribute to adverse post-traumatic outcomes, positioning them as critical intervention targets. However, transdiagnostic treatments for TR-IMs are scarce, as their underlying mechanisms have been investigated separate from their unique phenomenological properties. Extant models of more general episodic memory highlight dynamic hippocampal-cortical interactions that vary along the anterior-posterior axis of the hippocampus (HPC) to support different cognitive-affective and sensory-perceptual features of memory. Extending this work into the unique properties of TR-IMs, we conducted a study of eighty-four trauma-exposed adults who completed daily ecological momentary assessments of TR-IM properties followed by resting-state functional magnetic resonance imaging (rs-fMRI). Spatiotemporal dynamics of anterior and posterior hippocampal (a/pHPC)-cortical networks were assessed using co-activation pattern analysis to investigate their associations with different properties of TR-IMs. Emotional intensity of TR-IMs was inversely associated with the frequency and persistence of an aHPC-default mode network co-activation pattern. Conversely, sensory features of TR-IMs were associated with more frequent co-activation of the HPC with sensory cortices and the ventral attention network, and the reliving of TR-IMs in the "here-and-now" was associated with more persistent co-activation of the pHPC and the visual cortex. Notably, no associations were found between HPC-cortical network dynamics and conventional symptom measures, including TR-IM frequency or retrospective recall, underscoring the utility of ecological assessments of memory properties in identifying their neural substrates. These findings provide novel insights into the neural correlates of the unique features of TR-IMs that are critical for the development of individualized, transdiagnostic treatments for this pervasive, difficult-to-treat symptom.
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Affiliation(s)
- Kevin J Clancy
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Quentin Devignes
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA
| | - Yara Pollmann
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Sienna R Nielsen
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Kristin Howell
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Emily L Belleau
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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10
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Rangaprakash D, David O, Barry RL, Deshpande G. Comparison of hemodynamic response functions obtained from resting-state functional MRI and invasive electrophysiological recordings in rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530359. [PMID: 37961471 PMCID: PMC10634675 DOI: 10.1101/2023.02.27.530359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
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11
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Rangaprakash D, Barry RL, Deshpande G. The confound of hemodynamic response function variability in human resting-state functional MRI studies. Front Neurosci 2023; 17:934138. [PMID: 37521709 PMCID: PMC10375034 DOI: 10.3389/fnins.2023.934138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/07/2023] [Indexed: 08/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.
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Affiliation(s)
- D. Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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12
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Chamberlain TA, Rosenberg MD. Propofol selectively modulates functional connectivity signatures of sustained attention during rest and narrative listening. Cereb Cortex 2022; 32:5362-5375. [PMID: 35285485 DOI: 10.1093/cercor/bhac020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/06/2022] [Accepted: 01/08/2022] [Indexed: 12/27/2022] Open
Abstract
Sustained attention is a critical cognitive function reflected in an individual's whole-brain pattern of functional magnetic resonance imaging functional connectivity. However, sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively selective to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly, these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function.
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Affiliation(s)
- Taylor A Chamberlain
- Department of Psychology, The University of Chicago, 5848 S University Ave, IL 60637, Chicago
| | - Monica D Rosenberg
- Department of Psychology, The University of Chicago, 5848 S University Ave, IL 60637, Chicago.,Neuroscience Institute, The University of Chicago, 5812 South Ellis Ave., MC 0912, Suite P-400, IL 60637, Chicago
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13
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What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. PLoS Comput Biol 2022; 18:e1010412. [PMID: 36067227 PMCID: PMC9481177 DOI: 10.1371/journal.pcbi.1010412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 09/16/2022] [Accepted: 07/18/2022] [Indexed: 11/19/2022] Open
Abstract
The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or ‘information structure’), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision. Brain states emerge through continuously evolving dynamics of brain networks. The usual way of modelling these dynamics is by using stationary systems: there is one structure (attractor) which is responsible of the brain dynamics. We adopt a different approach by characterising the brain activity through a landscape of informational structures (IS) changing in time. We use a model transformation procedure to produce these structures and look at several properties related to how the different brain networks interact not in the observed resting-state fMRI signal but in the information structure underlying it. These properties provide measures strongly related with relevant characteristics of conscious activity, such as metastability, information integration or synchronisation. The distribution of IS measures is studied for healthy controls (HC) and two groups of post-comatose patients with disorders of consciousness (DOC): minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). Based on IS measures, machine learners classifiers identify the state of consciousness with an outstanding discrimination (precision of 95.6% por HC/DOC and 86.6% for MCS/UWS).
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14
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Matar E, Ehgoetz Martens KA, Phillips JR, Wainstein G, Halliday GM, Lewis SJG, Shine JM. Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia. NPJ Parkinsons Dis 2022; 8:16. [PMID: 35177652 PMCID: PMC8854384 DOI: 10.1038/s41531-022-00279-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 01/12/2022] [Indexed: 11/10/2022] Open
Abstract
Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson's disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom.
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Affiliation(s)
- Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia. .,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia. .,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
| | - Kaylena A Ehgoetz Martens
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
| | - Joseph R Phillips
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW, Australia
| | - Gabriel Wainstein
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Centro de Investigaciones Médicas, Pontifical Catholic University of Chile, Santiago, Chile
| | - Glenda M Halliday
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Simon J G Lewis
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
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15
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Ceglarek A, Ochab JK, Cifre I, Fafrowicz M, Sikora-Wachowicz B, Lewandowska K, Bohaterewicz B, Marek T, Chialvo DR. Non-linear Functional Brain Co-activations in Short-Term Memory Distortion Tasks. Front Neurosci 2021; 15:778242. [PMID: 34924944 PMCID: PMC8678091 DOI: 10.3389/fnins.2021.778242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 ± 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.
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Affiliation(s)
- Anna Ceglarek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Jeremi K Ochab
- M. Kac Complex Systems Research Center and M. Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland
| | - Ignacio Cifre
- Facultat de Psicologia, Ciències l'Educació i de l'Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Barbara Sikora-Wachowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Koryna Lewandowska
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Bartosz Bohaterewicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Dante R Chialvo
- Center for Complex Systems and Brain Sciences (CEMSC3), Instituto de Ciencias Físicas (ICIFI), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín (UNSAM), Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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16
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Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity. Neuroimage 2021; 243:118555. [PMID: 34492293 PMCID: PMC10018461 DOI: 10.1016/j.neuroimage.2021.118555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/24/2021] [Accepted: 09/03/2021] [Indexed: 02/04/2023] Open
Abstract
Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.
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17
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Cifre I, Miller Flores MT, Penalba L, Ochab JK, Chialvo DR. Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed. Front Neurosci 2021; 15:700171. [PMID: 34712111 PMCID: PMC8546168 DOI: 10.3389/fnins.2021.700171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022] Open
Abstract
The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.
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Affiliation(s)
- Ignacio Cifre
- Facultat de Psicologia, Ciències de l'Educació i de l'Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain.,Center for Complex Systems and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Maria T Miller Flores
- Center for Complex Systems and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Lucia Penalba
- Facultat de Psicologia, Ciències de l'Educació i de l'Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain
| | - Jeremi K Ochab
- Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research, Jagiellonian University, Krakow, Poland
| | - Dante R Chialvo
- Center for Complex Systems and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Buenos Aires, Argentina
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18
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López-González A, Panda R, Ponce-Alvarez A, Zamora-López G, Escrichs A, Martial C, Thibaut A, Gosseries O, Kringelbach ML, Annen J, Laureys S, Deco G. Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Commun Biol 2021; 4:1037. [PMID: 34489535 PMCID: PMC8421429 DOI: 10.1038/s42003-021-02537-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 08/11/2021] [Indexed: 01/07/2023] Open
Abstract
Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states.
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Affiliation(s)
- Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Rajanikant Panda
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Charlotte Martial
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Jitka Annen
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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19
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Chen Y, Zhang J. How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis. Front Syst Neurosci 2021; 15:648860. [PMID: 34295226 PMCID: PMC8291083 DOI: 10.3389/fnsys.2021.648860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 05/26/2021] [Indexed: 11/13/2022] Open
Abstract
Consciousness is considered a result of specific neuronal processes and mechanisms in the brain. Various suggested neuronal mechanisms, including the information integration theory (IIT), global neuronal workspace theory (GNWS), and neuronal construction of time and space as in the context of the temporospatial theory of consciousness (TTC), have been laid forth. However, despite their focus on different neuronal mechanisms, these theories neglect the energetic-metabolic basis of the neuronal mechanisms that are supposed to yield consciousness. Based on the findings of physiology-induced (sleep), pharmacology-induced (general anesthesia), and pathology-induced [vegetative state/unresponsive wakeful syndrome (VS/UWS)] loss of consciousness in both human subjects and animals, we, in this study, suggest that the energetic-metabolic processes focusing on ATP, glucose, and γ-aminobutyrate/glutamate are indispensable for functional connectivity (FC) of normal brain networks that renders consciousness possible. Therefore, we describe the energetic-metabolic predispositions of consciousness (EPC) that complement the current theories focused on the neural correlates of consciousness (NCC).
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Affiliation(s)
- Yali Chen
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jun Zhang
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical college, Fudan University, Shanghai, China
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20
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Paakki J, Rahko JS, Kotila A, Mattila M, Miettunen H, Hurtig TM, Jussila KK, Kuusikko‐Gauffin S, Moilanen IK, Tervonen O, Kiviniemi VJ. Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study. Brain Behav 2021; 11:e02174. [PMID: 33998178 PMCID: PMC8213933 DOI: 10.1002/brb3.2174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 04/05/2021] [Accepted: 04/23/2021] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION There has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common. METHODS Inspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns. RESULTS We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation. CONCLUSION Nonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.
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Affiliation(s)
- Jyri‐Johan Paakki
- Faculty of Medicine, Health and Biosciences Doctoral ProgrammeUniversity of Oulu Graduate SchoolUniversity of OuluOuluFinland
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
| | - Jukka S. Rahko
- Faculty of Medicine, Health and Biosciences Doctoral ProgrammeUniversity of Oulu Graduate SchoolUniversity of OuluOuluFinland
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Aija Kotila
- Faculty of HumanitiesResearch Unit of LogopedicsUniversity of OuluOuluFinland
| | - Marja‐Leena Mattila
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Helena Miettunen
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Tuula M. Hurtig
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
- Research Unit of Clinical Neuroscience, PsychiatryUniversity of OuluOuluFinland
| | - Katja K. Jussila
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Sanna Kuusikko‐Gauffin
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Irma K. Moilanen
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Osmo Tervonen
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
| | - Vesa J. Kiviniemi
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
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21
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Rangaprakash D, Tadayonnejad R, Deshpande G, O'Neill J, Feusner JD. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging Behav 2021; 15:1622-1640. [PMID: 32761566 PMCID: PMC7865013 DOI: 10.1007/s11682-020-00358-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling. Since HRF is influenced by non-neural factors, to date it has largely been considered as a confound or has been ignored in many analyses. However, underlying biophysics suggests that the HRF may contain meaningful correlates of neural activity, which might be unavailable through conventional fMRI metrics. Here, we estimated the HRF by performing deconvolution on resting-state fMRI data from a longitudinal sample of 25 healthy controls scanned twice and 44 adults with obsessive-compulsive disorder (OCD) before and after 4-weeks of intensive cognitive-behavioral therapy (CBT). HRF response height, time-to-peak and full-width at half-maximum (FWHM) in OCD were abnormal before treatment and normalized after treatment in regions including the caudate. Pre-treatment HRF predicted treatment outcome (OCD symptom reduction) with 86.4% accuracy, using machine learning. Pre-treatment HRF response height in the caudate head and time-to-peak in the caudate tail were top-predictors of treatment response. Time-to-peak in the caudate tail, a region not typically identified in OCD studies using conventional fMRI activation or connectivity measures, may carry novel importance. Additionally, pre-treatment response height in caudate head predicted post-treatment OCD severity (R = -0.48, P = 0.001), and was associated with treatment-related OCD severity changes (R = -0.44, P = 0.0028), underscoring its relevance. With HRF being a reliable marker sensitive to brain function, OCD pathology, and intervention-related changes, these results could guide future studies towards novel discoveries not possible through conventional fMRI approaches like standard BOLD activation or connectivity.
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Affiliation(s)
- D Rangaprakash
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School and Harvard-MIT Health Sciences and Technology, Cambridge, MA, 02129, USA
| | - Reza Tadayonnejad
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, 36849, USA
- Department of Psychological Sciences, Auburn University, Auburn, AL, 36849, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Auburn, AL, USA
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
- School of Psychology, Capital Normal University, Beijing, China
- Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joseph O'Neill
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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22
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Pujol J, Blanco-Hinojo L, Gallart L, Moltó L, Martínez-Vilavella G, Vilà E, Pacreu S, Adalid I, Deus J, Pérez-Sola V, Fernández-Candil J. Largest scale dissociation of brain activity at propofol-induced loss of consciousness. Sleep 2021; 44:5894260. [PMID: 32813022 DOI: 10.1093/sleep/zsaa152] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/06/2020] [Indexed: 11/14/2022] Open
Abstract
The brain is a functional unit made up of multilevel connected elements showing a pattern of synchronized activity that varies in different states. The wake-sleep cycle is a major variation of brain functional condition that is ultimately regulated by subcortical arousal- and sleep-promoting cell groups. We analyzed the evolution of functional MRI (fMRI) signal in the whole cortex and in a deep region including most sleep- and wake-regulating subcortical nuclei at loss of consciousness induced by the hypnotic agent propofol. Optimal data were obtained in 21 of the 30 healthy participants examined. A dynamic analysis of fMRI time courses on a time-scale of seconds was conducted to characterize consciousness transition, and functional connectivity maps were generated to detail the anatomy of structures showing different dynamics. Inside the magnet, loss of consciousness was marked by the participants ceasing to move their hands. We observed activity synchronization after loss of consciousness within both the cerebral cortex and subcortical structures. However, the evolution of fMRI signal was dissociated, showing a transient reduction of global cortico-subcortical coupling that was restored during the unconscious state. An exception to cortico-subcortical decoupling was a brain network related to self-awareness (i.e. the default mode network) that remained connected to subcortical brain structures. Propofol-induced unconsciousness is thus characterized by an initial, transitory dissociated synchronization at the largest scale of brain activity. Such cortico-subcortical decoupling and subsequent recoupling may allow the brain to detach from waking activity and reorganize into a functionally distinct state.
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Affiliation(s)
- Jesus Pujol
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain.,Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain
| | - Laura Blanco-Hinojo
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain.,Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain
| | - Lluís Gallart
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain.,Department of Surgery, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Luís Moltó
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | | | - Esther Vilà
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Susana Pacreu
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Irina Adalid
- Department of Anesthesiology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Joan Deus
- MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain.,Department of Psychobiology and Methodology in Health Sciences, Autonomous University of Barcelona, Barcelona, Spain
| | - Víctor Pérez-Sola
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain.,Institute of Neuropsychiatry and Addictions, Hospital del Mar-IMIM and Department of Psychiatry, Autonomous University of Barcelona, Barcelona, Spain
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23
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Eyre M, Fitzgibbon SP, Ciarrusta J, Cordero-Grande L, Price AN, Poppe T, Schuh A, Hughes E, O'Keeffe C, Brandon J, Cromb D, Vecchiato K, Andersson J, Duff EP, Counsell SJ, Smith SM, Rueckert D, Hajnal JV, Arichi T, O'Muircheartaigh J, Batalle D, Edwards AD. The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity. Brain 2021; 144:2199-2213. [PMID: 33734321 PMCID: PMC8370420 DOI: 10.1093/brain/awab118] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 12/11/2020] [Accepted: 12/16/2020] [Indexed: 12/23/2022] Open
Abstract
The Developing Human Connectome Project is an Open Science project that provides the
first large sample of neonatal functional MRI data with high temporal and spatial
resolution. These data enable mapping of intrinsic functional connectivity between
spatially distributed brain regions under normal and adverse perinatal circumstances,
offering a framework to study the ontogeny of large-scale brain organization in humans.
Here, we characterize in unprecedented detail the maturation and integrity of resting
state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm).
First, we applied group independent component analysis to define 11 RSNs in term-born
infants scanned at 43.5–44.5 weeks postmenstrual age (PMA). Adult-like topography was
observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among
six higher-order, association RSNs, analogues of the adult networks for language and
ocular control were identified, but a complete default mode network precursor was not.
Next, we regressed the subject-level datasets from an independent cohort of infants
scanned at 37–43.5 weeks PMA against the group-level RSNs to test for the effects of age,
sex and preterm birth. Brain mapping in term-born infants revealed areas of positive
association with age across four of six association RSNs, indicating active maturation in
functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased
connectivity in inferotemporal regions of the visual association network. Preterm birth
was associated with striking impairments of functional connectivity across all RSNs in a
dose-dependent manner; conversely, connectivity of the superior parietal lobules within
the lateral motor network was abnormally increased in preterm infants, suggesting a
possible mechanism for specific difficulties such as developmental coordination disorder,
which occur frequently in preterm children. Overall, we found a robust, modular,
symmetrical functional brain organization at normal term age. A complete set of
adult-equivalent primary RSNs is already instated, alongside emerging connectivity in
immature association RSNs, consistent with a primary-to-higher order ontogenetic sequence
of brain development. The early developmental disruption imposed by preterm birth is
associated with extensive alterations in functional connectivity.
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Affiliation(s)
- Michael Eyre
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Tanya Poppe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Imperial College London, London SW7 2AZ, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Jakki Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK.,Department of Paediatrics, University of Oxford, Oxford OX3 9DU, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London SW7 2AZ, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
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24
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Basso MA, Frey S, Guerriero KA, Jarraya B, Kastner S, Koyano KW, Leopold DA, Murphy K, Poirier C, Pope W, Silva AC, Tansey G, Uhrig L. Using non-invasive neuroimaging to enhance the care, well-being and experimental outcomes of laboratory non-human primates (monkeys). Neuroimage 2021; 228:117667. [PMID: 33359353 PMCID: PMC8005297 DOI: 10.1016/j.neuroimage.2020.117667] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 02/09/2023] Open
Abstract
Over the past 10-20 years, neuroscience witnessed an explosion in the use of non-invasive imaging methods, particularly magnetic resonance imaging (MRI), to study brain structure and function. Simultaneously, with access to MRI in many research institutions, MRI has become an indispensable tool for researchers and veterinarians to guide improvements in surgical procedures and implants and thus, experimental as well as clinical outcomes, given that access to MRI also allows for improved diagnosis and monitoring for brain disease. As part of the PRIMEatE Data Exchange, we gathered expert scientists, veterinarians, and clinicians who treat humans, to provide an overview of the use of non-invasive imaging tools, primarily MRI, to enhance experimental and welfare outcomes for laboratory non-human primates engaged in neuroscientific experiments. We aimed to provide guidance for other researchers, scientists and veterinarians in the use of this powerful imaging technology as well as to foster a larger conversation and community of scientists and veterinarians with a shared goal of improving the well-being and experimental outcomes for laboratory animals.
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Affiliation(s)
- M A Basso
- Fuster Laboratory of Cognitive Neuroscience, Department of Psychiatry and Biobehavioral Sciences UCLA Los Angeles CA 90095 USA
| | - S Frey
- Rogue Research, Inc. Montreal, QC, Canada
| | - K A Guerriero
- Washington National Primate Research Center University of Washington Seattle, WA USA
| | - B Jarraya
- Cognitive Neuroimaging Unit, INSERM, CEA, NeuroSpin center, 91191 Gif/Yvette, France; Université Paris-Saclay, UVSQ, Foch hospital, Paris, France
| | - S Kastner
- Princeton Neuroscience Institute & Department of Psychology Princeton University Princeton, NJ USA
| | - K W Koyano
- National Institute of Mental Health NIH Bethesda MD 20892 USA
| | - D A Leopold
- National Institute of Mental Health NIH Bethesda MD 20892 USA
| | - K Murphy
- Biosciences Institute and Centre for Behaviour and Evolution, Faculty of Medical Sciences Newcastle University Newcastle upon Tyne NE2 4HH United Kingdom UK
| | - C Poirier
- Biosciences Institute and Centre for Behaviour and Evolution, Faculty of Medical Sciences Newcastle University Newcastle upon Tyne NE2 4HH United Kingdom UK
| | - W Pope
- Department of Radiology UCLA Los Angeles, CA 90095 USA
| | - A C Silva
- Department of Neurobiology University of Pittsburgh, Pittsburgh PA 15261 USA
| | - G Tansey
- National Eye Institute NIH Bethesda MD 20892 USA
| | - L Uhrig
- Cognitive Neuroimaging Unit, INSERM, CEA, NeuroSpin center, 91191 Gif/Yvette, France
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25
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Higher-order sensorimotor circuit of the brain's global network supports human consciousness. Neuroimage 2021; 231:117850. [PMID: 33582277 DOI: 10.1016/j.neuroimage.2021.117850] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 12/29/2020] [Accepted: 02/08/2021] [Indexed: 12/17/2022] Open
Abstract
Consciousness is a mental characteristic of the human mind, whose exact neural features remain unclear. We aimed to identify the critical nodes within the brain's global functional network that support consciousness. To that end, we collected a large fMRI resting state dataset with subjects in at least one of the following three consciousness states: preserved (including the healthy awake state, and patients with a brain injury history (BI) that is fully conscious), reduced (including the N1-sleep state, and minimally conscious state), and lost (including the N3-sleep state, anesthesia, and unresponsive wakefulness state). We also included a unique dataset of subjects in rapid eye movement sleep state (REM-sleep) to test for the presence of consciousness with minimum movements and sensory input. To identify critical nodes, i.e., hubs, within the brain's global functional network, we used a graph-theoretical measure of degree centrality conjoined with ROI-based functional connectivity. Using these methods, we identified various higher-order sensory and motor regions including the supplementary motor area, bilateral supramarginal gyrus (part of inferior parietal lobule), supragenual/dorsal anterior cingulate cortex, and left middle temporal gyrus, that could be important hubs whose degree centrality was significantly reduced when consciousness was reduced or absent. Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.
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26
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Peran P, Malagurski B, Nemmi F, Sarton B, Vinour H, Ferre F, Bounes F, Rousset D, Mrozeck S, Seguin T, Riu B, Minville V, Geeraerts T, Lotterie JA, Deboissezon X, Albucher JF, Fourcade O, Olivot JM, Naccache L, Silva S. Functional and Structural Integrity of Frontoparietal Connectivity in Traumatic and Anoxic Coma. Crit Care Med 2020; 48:e639-e647. [PMID: 32697504 PMCID: PMC7365681 DOI: 10.1097/ccm.0000000000004406] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Recovery from coma might critically depend on the structural and functional integrity of frontoparietal networks. We aimed to measure this integrity in traumatic brain injury and anoxo-ischemic (cardiac arrest) coma patients by using an original multimodal MRI protocol. DESIGN Prospective cohort study. SETTING Three Intensive Critical Care Units affiliated to the University in Toulouse (France). PATIENTS We longitudinally recruited 43 coma patients (Glasgow Coma Scale at the admission < 8; 29 cardiac arrest and 14 traumatic brain injury) and 34 age-matched healthy volunteers. Exclusion criteria were disorders of consciousness lasting more than 30 days and focal brain damage within the explored brain regions. Patient assessments were conducted at least 2 days (5 ± 2 d) after complete withdrawal of sedation. All patients were followed up (Coma Recovery Scale-Revised) 3 months after acute brain injury. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Functional and structural MRI data were recorded, and the analysis was targeted on the posteromedial cortex, the medial prefrontal cortex, and the cingulum. Univariate analyses and machine learning techniques were used to assess diagnostic and predictive values. Coma patients displayed significantly lower medial prefrontal cortex-posteromedial cortex functional connectivity (area under the curve, 0.94; 95% CI, 0.93-0.95). Cardiac arrest patients showed specific structural disturbances within posteromedial cortex. Significant cingulum architectural disturbances were observed in traumatic brain injury patients. The machine learning medial prefrontal cortex-posteromedial cortex multimodal classifier had a significant predictive value (area under the curve, 0.96; 95% CI, 0.95-0.97), best combination of subregions that discriminates a binary outcome based on Coma Recovery Scale-Revised). CONCLUSIONS This exploratory study suggests that frontoparietal functional disconnections are specifically observed in coma and their structural counterpart provides information about brain injury mechanisms. Multimodal MRI biomarkers of frontoparietal disconnection predict 3-month outcome in our sample. These findings suggest that fronto-parietal disconnection might be particularly relevant for coma outcome prediction and could inspire innovative precision medicine approaches.
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Affiliation(s)
- Patrice Peran
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
| | - Briguitta Malagurski
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
| | - Federico Nemmi
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
| | - Benjamine Sarton
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
- Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Hélène Vinour
- Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Fabrice Ferre
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
- Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Fanny Bounes
- Critical Care Unit, University Teaching Hospital of Rangueil, Avenue Pr Jean Poulhès, Toulouse, France
| | - David Rousset
- Neurocritical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Segolène Mrozeck
- Neurocritical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Thierry Seguin
- Critical Care Unit, University Teaching Hospital of Rangueil, Avenue Pr Jean Poulhès, Toulouse, France
| | - Béatrice Riu
- Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Vincent Minville
- Anesthesiology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Thomas Geeraerts
- Neurocritical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Jean Albert Lotterie
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
| | - Xavier Deboissezon
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
- Physical Medicine and Rehabilitation Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Jean François Albucher
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
- Neurology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Olivier Fourcade
- Neurocritical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Jean Marc Olivot
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
- Neurology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
| | - Lionel Naccache
- Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, Paris, France
| | - Stein Silva
- Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, Toulouse, France
- Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse, France
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27
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Rahal L, Thibaut M, Rivals I, Claron J, Lenkei Z, Sitt JD, Tanter M, Pezet S. Ultrafast ultrasound imaging pattern analysis reveals distinctive dynamic brain states and potent sub-network alterations in arthritic animals. Sci Rep 2020; 10:10485. [PMID: 32591574 PMCID: PMC7320008 DOI: 10.1038/s41598-020-66967-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 05/29/2020] [Indexed: 01/20/2023] Open
Abstract
Chronic pain pathologies, which are due to maladaptive changes in the peripheral and/or central nervous systems, are debilitating diseases that affect 20% of the European adult population. A better understanding of the mechanisms underlying this pathogenesis would facilitate the identification of novel therapeutic targets. Functional connectivity (FC) extracted from coherent low-frequency hemodynamic fluctuations among cerebral networks has recently brought light on a powerful approach to study large scale brain networks and their disruptions in neurological/psychiatric disorders. Analysis of FC is classically performed on averaged signals over time, but recently, the analysis of the dynamics of FC has also provided new promising information. Keeping in mind the limitations of animal models of persistent pain but also the powerful tool they represent to improve our understanding of the neurobiological basis of chronic pain pathogenicity, this study aimed at defining the alterations in functional connectivity, in a clinically relevant animal model of sustained inflammatory pain (Adjuvant-induced Arthritis) in rats by using functional ultrasound imaging, a neuroimaging technique with a unique spatiotemporal resolution (100 μm and 2 ms) and sensitivity. Our results show profound alterations of FC in arthritic animals, such as a subpart of the somatomotor (SM) network, occurring several weeks after the beginning of the disease. Also, we demonstrate for the first time that dynamic functional connectivity assessed by ultrasound can provide quantitative and robust information on the dynamic pattern that we define as brain states. While the main state consists of an overall synchrony of hemodynamic fluctuations in the SM network, arthritic animal spend statistically more time in two other states, where the fluctuations of the primary sensory cortex of the inflamed hind paws show asynchrony with the rest of the SM network. Finally, correlating FC changes with pain behavior in individual animals suggest links between FC alterations and either the cognitive or the emotional aspects of pain. Our study introduces fUS as a new translational tool for the enhanced understanding of the dynamic pain connectome and brain plasticity in a major preclinical model of chronic pain.
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Affiliation(s)
- Line Rahal
- Laboratory of Brain Plasticity, ESPCI Paris, PSL Research University, CNRS UMR 8249, 10 rue Vauquelin, 75005, Paris, France
- Physics for Medicine Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Paris, France
| | - Miguel Thibaut
- Laboratory of Brain Plasticity, ESPCI Paris, PSL Research University, CNRS UMR 8249, 10 rue Vauquelin, 75005, Paris, France
| | - Isabelle Rivals
- Equipe de Statistique Appliquée, ESPCI Paris, PSL Research University, UMRS 1158, 10 rue Vauquelin, 75005, Paris, France
| | - Julien Claron
- Physics for Medicine Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Paris, France
| | - Zsolt Lenkei
- Laboratory of Brain Plasticity, ESPCI Paris, PSL Research University, CNRS UMR 8249, 10 rue Vauquelin, 75005, Paris, France
- Center of Psychiatry and Neurosciences, INSERM U894, 102 rue de la Santé, 75014, Paris, France
| | - Jacobo D Sitt
- Institut du Cerveau et de la Moelle, INSERM U1127, CNRS UMR 7225, Sorbonne University, UPMC Univ Paris 06 UMR, S 1127, Paris, France
| | - Mickael Tanter
- Physics for Medicine Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Paris, France
| | - Sophie Pezet
- Laboratory of Brain Plasticity, ESPCI Paris, PSL Research University, CNRS UMR 8249, 10 rue Vauquelin, 75005, Paris, France.
- Physics for Medicine Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Paris, France.
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28
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Crone JS, Lutkenhoff ES, Vespa PM, Monti MM. A systematic investigation of the association between network dynamics in the human brain and the state of consciousness. Neurosci Conscious 2020; 2020:niaa008. [PMID: 32551138 PMCID: PMC7293819 DOI: 10.1093/nc/niaa008] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 02/17/2020] [Accepted: 03/09/2020] [Indexed: 12/29/2022] Open
Abstract
An increasing amount of studies suggest that brain dynamics measured with resting-state functional magnetic resonance imaging (fMRI) are related to the state of consciousness. However, the challenge of investigating neuronal correlates of consciousness is the confounding interference between (recovery of) consciousness and behavioral responsiveness. To address this issue, and validate the interpretation of prior work linking brain dynamics and consciousness, we performed a longitudinal fMRI study in patients recovering from coma. Patients were assessed twice, 6 months apart, and assigned to one of two groups. One group included patients who were unconscious at the first assessment but regained consciousness and improved behavioral responsiveness by the second assessment. The other group included patients who were already conscious and improved only behavioral responsiveness. While the two groups were matched in terms of the average increase in behavioral responsiveness, only one group experienced a categorical change in their state of consciousness allowing us to partially dissociate consciousness and behavioral responsiveness. We find the variance in network metrics to be systematically different across states of consciousness, both within and across groups. Specifically, at the first assessment, conscious patients exhibited significantly greater variance in network metrics than unconscious patients, a difference that disappeared once all patients had recovered consciousness. Furthermore, we find a significant increase in dynamics for patients who regained consciousness over time, but not for patients who only improved responsiveness. These findings suggest that changes in brain dynamics are indeed linked to the state of consciousness and not just to a general level of behavioral responsiveness.
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Affiliation(s)
- Julia S Crone
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Paul M Vespa
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA.,Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA 90095, USA
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29
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TbCAPs: A toolbox for co-activation pattern analysis. Neuroimage 2020; 211:116621. [DOI: 10.1016/j.neuroimage.2020.116621] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/16/2020] [Accepted: 02/06/2020] [Indexed: 01/03/2023] Open
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30
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Freitas LGA, Bolton TAW, Krikler BE, Jochaut D, Giraud AL, Hüppi PS, Van De Ville D. Time-resolved effective connectivity in task fMRI: Psychophysiological interactions of Co-Activation patterns. Neuroimage 2020; 212:116635. [PMID: 32105884 DOI: 10.1016/j.neuroimage.2020.116635] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/10/2020] [Accepted: 02/12/2020] [Indexed: 12/12/2022] Open
Abstract
Investigating context-dependent modulations of Functional Connectivity (FC) with functional magnetic resonance imaging is crucial to reveal the neurological underpinnings of cognitive processing. Most current analysis methods hypothesise sustained FC within the duration of a task, but this assumption has been shown too limiting by recent imaging studies. While several methods have been proposed to study functional dynamics during rest, task-based studies are yet to fully disentangle network modulations. Here, we propose a seed-based method to probe task-dependent modulations of brain activity by revealing Psychophysiological Interactions of Co-activation Patterns (PPI-CAPs). This point process-based approach temporally decomposes task-modulated connectivity into dynamic building blocks which cannot be captured by current methods, such as PPI or Dynamic Causal Modelling. Additionally, it identifies the occurrence of co-activation patterns at single frame resolution as opposed to window-based methods. In a naturalistic setting where participants watched a TV program, we retrieved several patterns of co-activation with a posterior cingulate cortex seed whose occurrence rates and polarity varied depending on the context; on the seed activity; or on an interaction between the two. Moreover, our method exposed the consistency in effective connectivity patterns across subjects and time, allowing us to uncover links between PPI-CAPs and specific stimuli contained in the video. Our study reveals that explicitly tracking connectivity pattern transients is paramount to advance our understanding of how different brain areas dynamically communicate when presented with a set of cues.
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Affiliation(s)
- Lorena G A Freitas
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland.
| | - Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Delphine Jochaut
- Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
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31
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Bolton TAW, Wotruba D, Buechler R, Theodoridou A, Michels L, Kollias S, Rössler W, Heekeren K, Van De Ville D. Triple Network Model Dynamically Revisited: Lower Salience Network State Switching in Pre-psychosis. Front Physiol 2020; 11:66. [PMID: 32116776 PMCID: PMC7027374 DOI: 10.3389/fphys.2020.00066] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 01/21/2020] [Indexed: 11/13/2022] Open
Abstract
Emerging evidence has attributed altered network coordination between the default mode, central executive, and salience networks (DMN/CEN/SAL) to disturbances seen in schizophrenia, but little is known for at-risk psychosis stages. Moreover, pinpointing impairments in specific network-to-network interactions, although essential to resolve possibly distinct harbingers of conversion to clinically diagnosed schizophrenia, remains particularly challenging. We addressed this by a dynamic approach to functional connectivity, where right anterior insula brain interactions were examined through co-activation pattern (CAP) analysis. We utilized resting-state fMRI in 19 subjects suffering from subthreshold delusions and hallucinations (UHR), 28 at-risk for psychosis with basic symptoms describing only self-experienced subclinical disturbances (BS), and 29 healthy controls (CTR) matched for age, gender, handedness, and intelligence. We extracted the most recurring CAPs, compared their relative occurrence and average dwell time to probe their temporal expression, and quantified occurrence balance to assess the putative loss of competing relationships. Our findings substantiate the pivotal role of the right anterior insula in governing CEN-to-DMN transitions, which appear dysfunctional prior to the onset of psychosis, especially when first attenuated psychotic symptoms occur. In UHR subjects, it is longer active in concert with the DMN and there is a loss of competition between a SAL/DMN state, and a state with insula/CEN activation paralleled by DMN deactivation. These features suggest that abnormal network switching disrupts one's capacity to distinguish between the internal world and external environment, which is accompanied by inflexibility and an excessive awareness to internal processes reflected by prolonged expression of the right anterior insula-default mode co-activation pattern.
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Affiliation(s)
- Thomas A W Bolton
- Institute of Bioengineering, École Polytechique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, Université de Genève, Geneva, Switzerland
| | - Diana Wotruba
- Collegium Helveticum, ETH Zürich, Zurich, Switzerland.,The Zürich Program for Sustainable Development of Mental Health Services, Psychiatry University Hospital Zürich, Zurich, Switzerland
| | - Roman Buechler
- The Zürich Program for Sustainable Development of Mental Health Services, Psychiatry University Hospital Zürich, Zurich, Switzerland.,Department of Neuroradiology, University Hospital Zürich, Zurich, Switzerland
| | - Anastasia Theodoridou
- The Zürich Program for Sustainable Development of Mental Health Services, Psychiatry University Hospital Zürich, Zurich, Switzerland.,Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, University Hospital Zürich, Zurich, Switzerland
| | - Spyros Kollias
- Department of Neuroradiology, University Hospital Zürich, Zurich, Switzerland
| | - Wulf Rössler
- Collegium Helveticum, ETH Zürich, Zurich, Switzerland.,The Zürich Program for Sustainable Development of Mental Health Services, Psychiatry University Hospital Zürich, Zurich, Switzerland.,Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland.,Institute of Psychiatry, University of São Paulo, São Paulo, Brazil
| | - Karsten Heekeren
- The Zürich Program for Sustainable Development of Mental Health Services, Psychiatry University Hospital Zürich, Zurich, Switzerland.,Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zurich, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, Université de Genève, Geneva, Switzerland
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32
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Zhang X, Pan WJ, Keilholz SD. The relationship between BOLD and neural activity arises from temporally sparse events. Neuroimage 2019; 207:116390. [PMID: 31785420 DOI: 10.1016/j.neuroimage.2019.116390] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 10/25/2019] [Accepted: 11/21/2019] [Indexed: 01/12/2023] Open
Abstract
Resting state functional magnetic resonance (rs-fMRI) imaging offers insights into how different brain regions are connected into functional networks. It was recently shown that networks that are almost identical to the ones created from conventional correlation analysis can be obtained from a subset of high-amplitude data, suggesting that the functional networks may be driven by instantaneous co-activations of multiple brain regions rather than ongoing oscillatory processes. The rs-fMRI studies, however, rely on the blood oxygen level dependent (BOLD) signal, which is only indirectly sensitive to neural activity through neurovascular coupling. To provide more direct evidence that the neuronal co-activation events produce the time-varying network patterns seen in rs-fMRI studies, we examined the simultaneous rs-fMRI and local field potential (LFP) recordings in rats performed in our lab over the past several years. We developed complementary analysis methods that focus on either the temporal or spatial domain, and found evidence that the interaction between LFP and BOLD may be driven by instantaneous co-activation events as well. BOLD maps triggered on high-amplitude LFP events resemble co-activation patterns created from rs-fMRI data alone, though the co-activation time points are defined differently in the two cases. Moreover, only LFP events that fall into the highest or lowest thirds of the amplitude distribution result in a BOLD signal that can be distinguished from noise. These findings provide evidence of an electrophysiological basis for the time-varying co-activation patterns observed in previous studies.
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Affiliation(s)
- Xiaodi Zhang
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, SuiteW200, Atlanta, GA, 30322, USA.
| | - Wen-Ju Pan
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, SuiteW200, Atlanta, GA, 30322, USA.
| | - Shella Dawn Keilholz
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Health Sciences Research Building, 1760 Haygood Drive, SuiteW200, Atlanta, GA, 30322, USA.
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33
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Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI. Neuroimage 2019; 206:116316. [PMID: 31672663 DOI: 10.1016/j.neuroimage.2019.116316] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 09/09/2019] [Accepted: 10/26/2019] [Indexed: 01/22/2023] Open
Abstract
Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.
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34
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Luppi AI, Craig MM, Pappas I, Finoia P, Williams GB, Allanson J, Pickard JD, Owen AM, Naci L, Menon DK, Stamatakis EA. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun 2019; 10:4616. [PMID: 31601811 PMCID: PMC6787094 DOI: 10.1038/s41467-019-12658-9] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 09/16/2019] [Indexed: 12/26/2022] Open
Abstract
Prominent theories of consciousness emphasise different aspects of neurobiology, such as the integration and diversity of information processing within the brain. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from awake volunteers, propofol-anaesthetised volunteers, and patients with disorders of consciousness, in order to identify consciousness-specific patterns of brain function. We demonstrate that cortical networks are especially affected by loss of consciousness during temporal states of high integration, exhibiting reduced functional diversity and compromised informational capacity, whereas thalamo-cortical functional disconnections emerge during states of higher segregation. Spatially, posterior regions of the brain’s default mode network exhibit reductions in both functional diversity and integration with the rest of the brain during unconsciousness. These results show that human consciousness relies on spatio-temporal interactions between brain integration and functional diversity, whose breakdown may represent a generalisable biomarker of loss of consciousness, with potential relevance for clinical practice. How do diversity (entropy) and integration of activity across brain regions interact to support consciousness? Here the authors show that anaesthetised individuals and patients with disorders of consciousness exhibit overlapping reductions in both diversity and integration in the brain’s default mode network.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - Ioannis Pappas
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Helen Wills Neuroscience Institute, 210 Barker Hall, University of California - Berkeley, 94720, Berkeley, CA, USA
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), CB2 0QQ, Cambridge, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), CB2 0QQ, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity College Dublin, Dublin, Dublin 2, Ireland
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), CB2 0QQ, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK. .,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.
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35
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Modulation of the spontaneous hemodynamic response function across levels of consciousness. Neuroimage 2019; 200:450-459. [DOI: 10.1016/j.neuroimage.2019.07.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 07/03/2019] [Accepted: 07/04/2019] [Indexed: 01/06/2023] Open
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36
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Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma. Front Neurosci 2019; 13:803. [PMID: 31507353 PMCID: PMC6716456 DOI: 10.3389/fnins.2019.00803] [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] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 07/17/2019] [Indexed: 01/08/2023] Open
Abstract
Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R 2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.
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Affiliation(s)
- D Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, United States
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States.,U.S. Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McChord, WA, United States.,Department of Psychology, Auburn University, Auburn, AL, United States
| | - Jeffrey S Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Thomas S Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn, AL, United States.,Center for Neuroscience, Auburn University, Auburn, AL, United States.,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States.,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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37
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Onofrj M, Espay AJ, Bonanni L, Delli Pizzi S, Sensi SL. Hallucinations, somatic-functional disorders of PD-DLB as expressions of thalamic dysfunction. Mov Disord 2019; 34:1100-1111. [PMID: 31307115 DOI: 10.1002/mds.27781] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 04/30/2019] [Accepted: 05/24/2019] [Indexed: 12/12/2022] Open
Abstract
Hallucinations, delusions, and functional neurological manifestations (conversion and somatic symptom disorders) of Parkinson's disease (PD) and dementia with Lewy bodies increase in frequency with disease progression, predict the onset of cognitive decline, and eventually blend with and are concealed by dementia. These symptoms share the absence of reality constraints and can be considered comparable elements of the PD-dementia with Lewy bodies psychosis. We propose that PD-dementia with Lewy bodies psychotic disorders depend on thalamic dysfunction promoting a theta burst mode and subsequent thalamocortical dysrhythmia with focal cortical coherence to theta electroencephalogram rhythms. This theta electroencephalogram activity, also called fast-theta or pre-alpha, has been shown to predict cognitive decline and fluctuations in Parkinson's disease with dementia and dementia with Lewy bodies. These electroencephalogram alterations are now considered a predictive marker for progression to dementia. The resulting thalamocortical dysrhythmia inhibits the frontal attentional network and favors the decoupling of the default mode network. As the default mode network is involved in integration of self-referential information into conscious perception, unconstrained default mode network activity, as revealed by recent imaging studies, leads to random formation of connections that link strong autobiographical correlates to trivial stimuli, thereby producing hallucinations, delusions, and functional neurological disorders. The thalamocortical dysrhythmia default mode network decoupling hypothesis provides the rationale for the design and testing of novel therapeutic pharmacological and nonpharmacological interventions in the context of PD, PD with dementia, and dementia with Lewy bodies. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Alberto J Espay
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA
| | - Laura Bonanni
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Departments of Neurology and Pharmacology, Institute for Mind Impairments and Neurological Disorders, University of California - Irvine, Irvine, California, USA
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38
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Kung YC, Li CW, Chen S, Chen SCJ, Lo CYZ, Lane TJ, Biswal B, Wu CW, Lin CP. Instability of brain connectivity during nonrapid eye movement sleep reflects altered properties of information integration. Hum Brain Mapp 2019; 40:3192-3202. [PMID: 30941797 DOI: 10.1002/hbm.24590] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 03/05/2019] [Accepted: 03/11/2019] [Indexed: 02/01/2023] Open
Abstract
Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time-varying information propagation during sleep. Accordingly, we conducted simultaneous EEG-fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding-window approach. We divided dFC into two aspects: mean dFC (dFCmean ) and variance dFC (dFCvar ). A high dFCmean indicates stable brain network integrity, whereas a high dFCvar indicates instability of information transfer within and between functional networks. For the network-based dFC, the dFCvar were negatively correlated with the dFCmean across the waking and three NREM sleep stages. As sleep deepened, the dFCmean decreased (N0~N1 > N2 > N3), whereas the dFCvar peaked during the N2 stage (N0~N1 < N3 < N2). The highest dFCvar during the N2 stage indicated the unstable synchronizations across the entire brain. In the N3 stage, the overall disrupted network integration was observed through the lowest dFCmean and elevated dFCvar, compared with N0 and N1. Conclusively, when the network specificity (dFCmean ) breaks down, the consciousness dissipates with increasing variability of information exchange (dFCvar ).
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Affiliation(s)
- Yi-Chia Kung
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Shuo Chen
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Sharon Chia-Ju Chen
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Yi Z Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Timothy J Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
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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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Fernandez Guerrero A, Achermann P. Intracortical Causal Information Flow of Oscillatory Activity (Effective Connectivity) at the Sleep Onset Transition. Front Neurosci 2018; 12:912. [PMID: 30564093 PMCID: PMC6288604 DOI: 10.3389/fnins.2018.00912] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 11/20/2018] [Indexed: 12/03/2022] Open
Abstract
We investigated the sleep onset transition in humans from an effective connectivity perspective in a baseline condition (approx. 16 h of wakefulness) and after sleep deprivation (40 h of sustained wakefulness). Using EEG recordings (27 derivations), source localization (LORETA) allowed us to reconstruct the underlying patterns of neuronal activity in various brain regions, e.g., the default mode network (DMN), dorsolateral prefrontal cortex and hippocampus, which were defined as regions of interest (ROI). We applied isolated effective coherence (iCOH) to assess effective connectivity patterns at the sleep onset transition [2 min prior to and 10 min after sleep onset (first occurrence of stage 2)]. ICOH reveals directionality aspects and resolves the spectral characteristics of information flow in a given network of ROIs. We observed an anterior-posterior decoupling of the DMN, and moreover, a prominent role of the posterior cingulate cortex guiding the process of the sleep onset transition, particularly, by transmitting information in the low frequency range (delta and theta bands) to other nodes of DMN (including the hippocampus). In addition, the midcingulate cortex appeared as a major cortical relay station for spindle synchronization (originating from the thalamus; sigma activity). The inclusion of hippocampus indicated that this region might be functionally involved in sigma synchronization observed in the cortex after sleep onset. Furthermore, under conditions of increased homeostatic pressure, we hypothesize that an anterior-posterior decoupling of the DMN occurred at a faster rate compared to baseline overall indicating weakened connectivity strength within the DMN. Finally, we also demonstrated that cortico-cortical spindle synchronization was less effective after sleep deprivation than in baseline, thus, reflecting the reduction of spindles under increased sleep pressure.
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Affiliation(s)
- Antonio Fernandez Guerrero
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Peter Achermann
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- The KEY Institute for Brain-Mind Research, Department of Psychiatry, Psychotherapy and Sychosomatics, University Hospital of Psychiatry, Zurich, Switzerland
- Zurich Center for Interdisciplinary Sleep Research, University of Zurich, Zurich, Switzerland
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41
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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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Estimated hemodynamic response function parameters obtained from resting state BOLD fMRI signals in subjects with autism spectrum disorder and matched healthy subjects. Data Brief 2018; 19:1305-1309. [PMID: 30225289 PMCID: PMC6139368 DOI: 10.1016/j.dib.2018.04.126] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 04/27/2018] [Accepted: 04/30/2018] [Indexed: 11/20/2022] Open
Abstract
In Functional magnetic resonance imaging (fMRI), the blood oxygen level dependent (BOLD) signal is modeled as a convolution of the hemodynamic response function (HRF) and the unmeasured latent neural signal. Although most cortical and subcortical brain regions share the canonical shape of the HRF, the temporal structure of HRFs are variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. The variability between subjects can be examined by three parameters that characterize the HRF: response height (RH), time-to-peak (TTP) and full-width at half-max (FWHM). This data provides three HRF parameters at every voxel, obtained from Autism Spectrum Disorder (ASD) patients (N = 531), and matched healthy controls (N = 571). Since ongoing studies suggest that non-standard populations have important differences in their HRFs when compared with healthy control, this data set is valuable in studying variability of HRF in ASD group and inferring the underlying pathology that also affects the HRF. It also has implications for fMRI analyses like resting-sate connectivity analysis.
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43
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn Reson Med 2018; 80:1697-1713. [DOI: 10.1002/mrm.27146] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 01/26/2023]
Affiliation(s)
- D. Rangaprakash
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychiatry and Biobehavioral Sciences; University of California Los Angeles; Los Angeles California
| | - Guo-Rong Wu
- Department of Data Analysis; University of Ghent; Ghent Belgium
- Key Laboratory of Cognition and Personality, Southwest University; Chongqing China
| | | | - Xiaoping Hu
- Department of Bioengineering; University of California Riverside; Riverside California
| | - Gopikrishna Deshpande
- Auburn University MRI Research Center, Department of Electrical and Computer Engineering; Auburn University; Auburn Alabama
- Department of Psychology; Auburn University; Auburn Alabama
- Center for Health Ecology and Equity Research, Auburn University; Auburn Alabama
- Alabama Advanced Imaging Consortium, Auburn University, University of South Alabama and University of Alabama at Tuscaloosa and Birmingham; Alabama
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44
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner. Data Brief 2018; 17:1175-1179. [PMID: 29876476 PMCID: PMC5988211 DOI: 10.1016/j.dib.2018.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 12/22/2017] [Accepted: 01/02/2018] [Indexed: 01/10/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.
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Affiliation(s)
- D. Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Guo-Rong Wu
- Department of Data Analysis, University of Ghent, Ghent, Belgium
- Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | | | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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45
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Zhuang X, Walsh RR, Sreenivasan K, Yang Z, Mishra V, Cordes D. Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease. Neuroimage 2018; 172:64-84. [PMID: 29355770 DOI: 10.1016/j.neuroimage.2018.01.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/23/2017] [Accepted: 01/10/2018] [Indexed: 12/28/2022] Open
Abstract
The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Ryan R Walsh
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | | | - Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Virendra Mishra
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA.
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46
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Huang H, Tanner J, Parvataneni H, Rice M, Horgas A, Ding M, Price C. Impact of Total Knee Arthroplasty with General Anesthesia on Brain Networks: Cognitive Efficiency and Ventricular Volume Predict Functional Connectivity Decline in Older Adults. J Alzheimers Dis 2018; 62:319-333. [PMID: 29439328 PMCID: PMC5827939 DOI: 10.3233/jad-170496] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Using resting state functional magnetic resonance imaging (RS-fMRI), we explored: 1) pre- to post-operative changes in functional connectivity in default mode, salience, and central executive networks after total knee arthroplasty (TKA) with general anesthesia, and 2) the contribution of cognitive/brain reserve metrics these resting state functional declines. Individuals age 60 and older electing unilateral total knee arthroplasty (TKA; n = 48) and non-surgery peers with osteoarthritis (n = 45) completed baseline cognitive testing and baseline and post-surgery (post-baseline, 48-h post-surgery) brain MRI. We acquired cognitive and brain estimates for premorbid (vocabulary, reading, education, intracranial volume) and current (working memory, processing speed, declarative memory, ventricular volume) reserve. Functional network analyses corrected for pain severity and pain medication. The surgery group declined in every functional network of interest (p < 0.001). Relative to non-surgery peers, 23% of surgery participants declined in at least one network and 15% of the total TKA sample declined across all networks. Larger preoperative ventricular volume and lower scores on preoperative metrics of processing speed and working memory predicted default mode network connectivity decline. Premorbid cognitive and premorbid brain reserve did not predict decline. Within 48 hours after surgery, at least one fourth of the older adult sample showed significant functional network decline. Metrics of current brain status (ventricular volume), working memory, and processing speed predicted the severity of default mode network connectivity decline. These findings demonstrate the relevance of preoperative cognition and brain integrity on acute postoperative functional network change.
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Affiliation(s)
- Haiqing Huang
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Jared Tanner
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Hari Parvataneni
- Department of Orthopedic Surgery, University of Florida, Gainesville, FL, USA
| | - Mark Rice
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
| | - Ann Horgas
- College of Nursing, University of Florida, Gainesville, FL, USA
| | - Mingzhou Ding
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Catherine Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
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47
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Abrol A, Rashid B, Rachakonda S, Damaraju E, Calhoun VD. Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity. Front Neurosci 2017; 11:624. [PMID: 29163021 PMCID: PMC5682010 DOI: 10.3389/fnins.2017.00624] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 10/26/2017] [Indexed: 12/18/2022] Open
Abstract
Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets.
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Affiliation(s)
- Anees Abrol
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Barnaly Rashid
- The Mind Research Network, Albuquerque, NM, United States
| | | | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, 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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48
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Chen JE, Rubinov M, Chang C. Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data. Neuroimaging Clin N Am 2017; 27:547-560. [PMID: 28985928 PMCID: PMC5679015 DOI: 10.1016/j.nic.2017.06.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Functional MR imaging (fMR imaging) studies have recently begun to examine spontaneous changes in interregional interactions (functional connectivity) over seconds to minutes, and their relation to natural shifts in cognitive and physiologic states. This practice opens the potential for uncovering structured, transient configurations of coordinated brain activity whose features may provide novel cognitive and clinical biomarkers. However, analysis of these time-varying phenomena requires careful differentiation between neural and nonneural contributions to the fMR imaging signal and thorough validation and statistical testing. In this article, the authors present an overview of methodological and interpretational considerations in this emerging field.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Mikail Rubinov
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Catie Chang
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
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49
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Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma. Hum Brain Mapp 2017; 39:264-287. [PMID: 29058357 DOI: 10.1002/hbm.23841] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 08/29/2017] [Accepted: 10/01/2017] [Indexed: 12/15/2022] Open
Abstract
Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.,Human Dimension Division, HQ TRADOC, Fort Eustis, Virgina
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.,Department of Psychology, Auburn University, Auburn, Alabama.,Alabama Advanced Imaging Consortium, USA
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50
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Di Perri C, Amico E, Heine L, Annen J, Martial C, Larroque SK, Soddu A, Marinazzo D, Laureys S. Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns. Hum Brain Mapp 2017; 39:89-103. [PMID: 29024197 DOI: 10.1002/hbm.23826] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 08/11/2017] [Accepted: 09/18/2017] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness. METHODS Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames. RESULTS Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Carol Di Perri
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium.,Centre for Clinical Brain Sciences, Centre for Dementia Prevention, UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Enrico Amico
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium.,Department of Data-analysis, University of Ghent, Ghent, B9000, Belgium.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana
| | - Lizette Heine
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
| | | | - Andrea Soddu
- Brain and Mind Institute, Physics & Astronomy Department, Western University, London, Ontario, Canada
| | - Daniele Marinazzo
- Department of Data-analysis, University of Ghent, Ghent, B9000, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium
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