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Ahmadi Ghomroudi P, Siugzdaite R, Messina I, Grecucci A. Decoding acceptance and reappraisal strategies from resting state macro networks. Sci Rep 2024; 14:19232. [PMID: 39164353 PMCID: PMC11336109 DOI: 10.1038/s41598-024-68490-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 07/24/2024] [Indexed: 08/22/2024] Open
Abstract
Acceptance and reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task-based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks' contributions to these abilities, especially regarding acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the affective network and increased activity inside the executive and sensorimotor networks to be predictive of acceptance. We also expect that these networks may be associated at least in part with reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females; mean age: 30.09 ± 12.87 years, mean education: 12.62 ± 1.41 years). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting-state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased affective, and executive, and increased sensorimotor networks, while reappraisal was predicted by an increase in the sensorimotor network only. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal strategies, with the sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, illustrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.
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Affiliation(s)
- Parisa Ahmadi Ghomroudi
- DiPSCo-Department of Psychology and Cognitive Sciences, University of Trento, Corso Bettini, 84, 38068, Rovereto, Italy.
| | - Roma Siugzdaite
- Department of Experimental Psychology, Faculty of Psychology and Pedagogical Sciences, Ghent University, Ghent, Belgium
| | | | - Alessandro Grecucci
- DiPSCo-Department of Psychology and Cognitive Sciences, University of Trento, Corso Bettini, 84, 38068, Rovereto, Italy
- CISMed-Center for Medical Sciences, University of Trento, Trento, Italy
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Kambli H, Santamaria-Pang A, Tarapov I, Beheshtian E, Luna LP, Sair H, Jones C. Atlas-Based Labeling of Resting-State fMRI. Brain Connect 2024; 14:319-326. [PMID: 38814830 DOI: 10.1089/brain.2023.0080] [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] [Indexed: 06/01/2024] Open
Abstract
Background: Functional magnetic resonance imaging (fMRI) has the potential to provide noninvasive functional mapping of the brain with high spatial and temporal resolution. However, fMRI independent components (ICs) must be manually inspected, selected, and interpreted, requiring time and expertise. We propose a novel approach for automated labeling of fMRI ICs by establishing their characteristic spatio-functional relationship. Methods: The approach identifies 9 resting-state networks and 45 ICs and generates a functional activation feature map that quantifies the spatial distribution, relative to an anatomical labeled atlas, of the z-scores of each IC across a cohort of 176 subjects. The cosine-similarity metric was used to classify unlabeled ICs based on the similarity to the spatial distribution of activation with the pregenerated feature map. The approach was tested on three fMRI datasets from the 1000 functional connectome projects, consisting of 280 subjects, that were not included in feature map generation. Results: The results demonstrate the effectiveness of the approach in classifying ICs based on their spatial features with an accuracy of better than 95%. Conclusions: The approach significantly reduces expert time and computation time required for labeling ICs, while improving reliability and accuracy. The spatio-functional relationship also provides an explainable relationship between the functional activation and the anatomically defined regions.
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Affiliation(s)
- Hrishikesh Kambli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | | | | | | | - Licia P Luna
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Haris Sair
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA
| | - Craig Jones
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
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Tsai CJ, Lin HY, Gau SSF. Correlation of altered intrinsic functional connectivity with impaired self-regulation in children and adolescents with ADHD. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01787-y. [PMID: 38906983 DOI: 10.1007/s00406-024-01787-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/16/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Attention-deficit hyperactivity disorder (ADHD) has a high prevalence of co-occurring impaired self-regulation (dysregulation), exacerbating adverse outcomes. Neural correlates underlying impaired self-regulation in ADHD remain inconclusive. We aimed to investigate the impact of dysregulation on intrinsic functional connectivity (iFC) in children with ADHD and the correlation of iFC with dysregulation among children with ADHD relative to typically developing controls (TDC). METHODS Resting-state functional MRI data of 71 children with ADHD (11.38 ± 2.44 years) and 117 age-matched TDC were used in the final analysis. We restricted our analyses to resting-state networks (RSNs) of interest derived from independent component analysis. Impaired self-regulation was estimated based on the Child Behavioral Checklist-Dysregulation Profile. RESULTS Children with ADHD showed stronger iFC than TDC in the left frontoparietal network, somatomotor network (SMN), visual network (VIS), default-mode network (DMN), and dorsal attention network (DAN) (FWE-corrected alpha < 0.05). After adding dysregulation levels as an extra regressor, the ADHD group only showed stronger iFC in the VIS and SMN. ADHD children with high dysregulation had higher precuneus iFC within DMN than ADHD children with low dysregulation. Angular gyrus iFC within DMN was positively correlated with dysregulation in the ADHD group but negatively correlated with dysregulation in the TDC group. Functional network connectivity showed ADHD had a greater DMN-DAN connection than TDC, regardless of the dysregulation level. CONCLUSIONS Our findings suggest that DMN connectivity may contribute to impaired self-regulation in ADHD. Impaired self-regulation should be considered categorical and dimensional moderators for the neural correlates of altered iFC in ADHD.
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Affiliation(s)
- Chia-Jui Tsai
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsiang-Yuan Lin
- Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Susan Shur-Fen Gau
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan.
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan.
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Weidler C, Gramegna C, Müller D, Schrickel M, Habel U. Resting-state functional connectivity and structural differences between smokers and healthy non-smokers. Sci Rep 2024; 14:6878. [PMID: 38519565 PMCID: PMC10960011 DOI: 10.1038/s41598-024-57510-3] [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: 12/19/2023] [Accepted: 03/19/2024] [Indexed: 03/25/2024] Open
Abstract
Previous studies have shown an association between cigarette use and altered resting-state functional connectivity (rsFC) in many large-scale networks, sometimes complemented by measures of cortical atrophy. In this study, we aimed to further explore the neural differences between smokers and healthy non-smokers through the integration of functional and structural analyses. Imaging data of fifty-two smokers and forty-five non-smokers were analyzed through an independent component analysis for group differences in rsFC. Smokers showed lower rsFC within the dorsal attention network (DAN) in the left superior and middle frontal gyrus and left superior division of the lateral occipital cortex compared to non-smokers; moreover, cigarette use was found to be associated with reduced grey matter volume in the left superior and middle frontal gyrus and right orbitofrontal cortex, partly overlapping with functional findings. Within smokers, daily cigarette consumption was positively associated with increased rsFC within the cerebellar network and the default mode network and decreased rsFC within the visual network and the salience network, while carbon monoxide level showed a positive association with increased rsFC within the sensorimotor network. Our results suggest that smoking negatively impacts rsFC within the DAN and that changes within this network might serve as a circuit-based biomarker for structural deficits.
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Affiliation(s)
- Carmen Weidler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Chiara Gramegna
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
- PhD Program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
- Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy.
| | - Dario Müller
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Maike Schrickel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
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Zheng Y, Hou Z, Ma S, Huang Z, Peng J, Huang S, Guo R, Huang J, Lin Z, Zhuang Z, Yin J, Xie L. Altered dynamic functional network connectivity in rheumatoid arthritis associated with peripheral inflammation and neuropsychiatric disorders. RMD Open 2024; 10:e003684. [PMID: 38428977 PMCID: PMC10910624 DOI: 10.1136/rmdopen-2023-003684] [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: 09/06/2023] [Accepted: 02/12/2024] [Indexed: 03/03/2024] Open
Abstract
OBJECTIVE This study explored the dynamic functional connective (DFC) alterations in patients with rheumatoid arthritis (RA) and investigated the correlation between the neuropsychiatric symptoms, peripheral inflammation and DFC alterations. METHOD Using resting-state functional MRI, we investigated the DFC based on spatial independent component analysis and sliding window method for 30 patients with RA and 30 healthy controls (HCs). The Spearman correlation was calculated between aberrant DFC alterations, Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HAD), C reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Diagnostic efficacy of indicators was assessed using receiver operating characteristic analysis (ROC). RESULTS Three dynamic functional states were identified. Compared with HC, patients with RA showed reduced FC variabilities between sensorimotor network (SMN) and insula, SMN and orbitofrontal cortex, which were the crucial regions of sensory processing network. The above FC variabilities were correlated with the MoCA, HAD, CRP and ESR in patients with RA. Additionally, the CRP and ESR were negatively correlated to MoCA and positively related to HAD in patients with RA. The ROC analysis results showed that MoCA, HAD and FC variabilities of the sensory processing network could distinguish patients with RA from HC and also identify patients with RA with high ESR. CONCLUSION Our findings demonstrated that abnormal DFC patterns in sensory processing networks in patients with RA were closely associated with peripheral inflammation and neuropsychiatric symptoms. This indicates that the dynamic temporal characteristics of the brain functional network may be potential neuroimaging biomarkers for revealing the pathological mechanism of RA.
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Affiliation(s)
- Yanmin Zheng
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zhiduo Hou
- Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shuhua Ma
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zikai Huang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Jianhua Peng
- Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shuxin Huang
- Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Ruiwei Guo
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Jinzhuang Huang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zhirong Lin
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zelin Zhuang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Jingjing Yin
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Lei Xie
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
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van der Horn HJ, Ling JM, Wick TV, Dodd AB, Robertson-Benta CR, McQuaid JR, Zotev V, Vakhtin AA, Ryman SG, Cabral J, Phillips JP, Campbell RA, Sapien RE, Mayer AR. Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury. Neuroimage 2024; 285:120470. [PMID: 38016527 PMCID: PMC10815936 DOI: 10.1016/j.neuroimage.2023.120470] [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: 09/13/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023] Open
Abstract
Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.
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Affiliation(s)
| | - Josef M Ling
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | - Tracey V Wick
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | - Andrew B Dodd
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | | | | | - Vadim Zotev
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | | | | | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Braga, Portugal
| | | | - Richard A Campbell
- Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131
| | - Robert E Sapien
- Department of Emergency Medicine, University of New Mexico, Albuquerque, NM 87131
| | - Andrew R Mayer
- The Mind Research Network/LBERI, Albuquerque, NM 87106; Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131; Department of Psychology, University of New Mexico, Albuquerque, NM 87131; Department of Neurology, University of New Mexico, Albuquerque, NM 87131
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Araújo RP, Figueiredo P, Pinto J, Vilela P, Martins IP, Gil-Gouveia R. Altered functional connectivity in a sensorimotor-insular network during spontaneous migraine attacks: A resting-state FMRI study. Brain Res 2023; 1818:148513. [PMID: 37499729 DOI: 10.1016/j.brainres.2023.148513] [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: 04/06/2023] [Revised: 07/01/2023] [Accepted: 07/24/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND Previous functional magnetic resonance imaging studies have identified brain-connectivity alterations across multiple regions in people with migraine when compared to healthy controls. Few studies have focused on such changes throughout the different phases of the migraine cycle. We aimed to investigate functional connectivity during spontaneous occurring episodic migraine attacks, in comparison to interictal periods. METHODS Eleven women with episodic migraine without aura underwent two sessions of resting-state fMRI, during and outside of a spontaneous migraine attack. Functional connectivity changes were assessed across canonical resting-state networks, identified by independent component analysis. Significantly altered connectivity was correlated with migraine attack symptoms. RESULTS Decreased functional connectivity between subregions of the sensorimotor network (specifically, the primary somatosensory and motor cortices) and the posterior insula, bilaterally, was found during attacks. In both sessions, the functional connectivity between these regions was lower in patients who usually suffered longer attacks. DISCUSSION The sensorimotor and insular regions are involved in nociceptive, autonomic, and somatosensory processing so the finding of reduced connectivity between these structures within a migraine attack is likely associated to the perception of pain and the heighten sensitivity to stimuli experienced in this disorder.
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Affiliation(s)
- Raquel Pestana Araújo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Joana Pinto
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford
| | - Pedro Vilela
- Neuroradiology Department, Hospital da Luz, Lisbon, Portugal
| | - Isabel Pavão Martins
- Centro de Estudos Egas Moniz, Department of Clinical Neurosciences, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Raquel Gil-Gouveia
- Headache Center, Neurology Department, Hospital da Luz, Lisboa, Portugal; Universidade Católica Portuguesa, Institute of Health Sciences, Center for Interdisciplinary Research in Health, Lisbon, Portugal.
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Niroumand Sarvandani M, Sheikhi Koohsar J, Rafaiee R, Saeedi M, Seyedhosseini Tamijani SM, Ghazvini H, Sheibani H. COVID-19 and the Brain: A Psychological and Resting-state Functional Magnetic Resonance Imagin (fMRI) Study of the Whole-brain Functional Connectivity. Basic Clin Neurosci 2023; 14:753-771. [PMID: 39070192 PMCID: PMC11273205 DOI: 10.32598/bcn.2021.1425.4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 06/27/2023] [Accepted: 07/31/2023] [Indexed: 07/30/2024] Open
Abstract
Introduction Coronavirus-2019 (COVID-19) spreads rapidly worldwide and causes severe acute respiratory syndrome. The current study aims to evaluate the relationship between the whole-brain functional connections in a resting state and cognitive impairments in patients with COVID-19 compared to the healthy control group. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) and Montreal cognitive assessment (MoCA) data were obtained from 29 patients of the acute stage of COVID-19 on the third day of admission and 20 healthy controls. Cross-correlation of the mean resting-state signals was determined in the voxels of 23 independent components (IC) of brain neural circuits. To assess cognitive function and neuropsychological status, MoCA was performed on all participants. The relationship between rs-fMRI information, neuropsychological status, and paraclinical data was analyzed. Results The COVID-19 group got a lower mean MoCA score and showed a significant reduction in the functional connectivity of the IC14 (P<0.001) and IC38 (P<0.001) regions compared to the controls. The increase in functional connectivity was observed in the COVID-19 group compared to the controls at baseline in the default mode network (DMN) IC00 (P<0.001) and dorsal attention network (DAN) IC08 (P<0.001) regions. Furthermore, the alternation of functional connectivity in the mentioned ICs was significantly correlated with the mean MoCA scores and inflammatory parameters, i.e. erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Conclusion Functional connectivity abnormalities in four brain neural circuits are associated with cognitive impairment and increased inflammatory markers in patients with COVID-19. Highlights The patients with coronavirus-2019 (COVID-19) got a lower mean Montreal cognitive assessment (MoCA) score.The patients with COVID-19 showed significant reduction in the functional connectivity of the IC14 and IC38 regions.The patients with COVID-19 showed significant increase of functional connectivity in the default mode network (DMN) IC00 and dorsal attention network (DAN) IC08 regions.Alternation of functional connectivity was significantly correlated with the mean MoCA scores and ESR and CRP. Plain Language Summary The researcher aimed at assessing cognitive impairments and investigating the whole-brain functional connectivity using resting state fMRI in patients with COVID-19 compared with healthy control group. The result showed That COVID-19 group got a lower mean cognitive score and showed a significant reduction in the functional connectivity of the IC14 and IC38 regions of brain compared with controls. Also, the increase of functional connectivity was observed in the COVID-19 group compared with controls at baseline in the default mode network (DMN) and dorsal attention network (DAN) regions of brain. Moreover, Functional connectivity abnormalities in four brain neural circuits associated with cognitive impairment and increased inflammatory markers in patients with COVID-19.
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Affiliation(s)
- Mohammad Niroumand Sarvandani
- Department of Addiction Studies, School of Medicine, Student Research Committee, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Javad Sheikhi Koohsar
- Health Related Social and Behavioral Sciences Research Center, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Raheleh Rafaiee
- Department of Neuroscience, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Maryam Saeedi
- Department of Neurology, School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran
| | | | - Hamed Ghazvini
- Department of Neuroscience, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Hossein Sheibani
- Unit of Clinical Research Development, Imam Hossein Hospital, Shahroud University of Medical Sciences, Shahroud, Iran
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D'Andrea CB, Laumann TO, Newbold DJ, Nelson SM, Nielsen AN, Chauvin R, Marek S, Greene DJ, Dosenbach NUF, Gordon EM. Substructure of the brain's Cingulo-Opercular network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561772. [PMID: 37873065 PMCID: PMC10592749 DOI: 10.1101/2023.10.10.561772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The Cingulo-Opercular network (CON) is an executive network of the human brain that regulates actions. CON is composed of many widely distributed cortical regions that are involved in top-down control over both lower-level (i.e., motor) and higher-level (i.e., cognitive) functions, as well as in processing of painful stimuli. Given the topographical and functional heterogeneity of the CON, we investigated whether subnetworks within the CON support separable aspects of action control. Using precision functional mapping (PFM) in 15 participants with > 5 hours of resting state functional connectivity (RSFC) and task data, we identified three anatomically and functionally distinct CON subnetworks within each individual. These three distinct subnetworks were linked to Decisions, Actions, and Feedback (including pain processing), respectively, in convergence with a meta-analytic task database. These Decision, Action and Feedback subnetworks represent pathways by which the brain establishes top-down goals, transforms those goals into actions, implemented as movements, and processes critical action feedback such as pain.
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Affiliation(s)
- Carolina Badke D'Andrea
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO 63310, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Dillan J Newbold
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota 55455, USA
| | - Ashley N Nielsen
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Roselyne Chauvin
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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Allen CH, Maurer JM, Gullapalli AR, Edwards BG, Aharoni E, Harenski CL, Anderson NE, Harenski KA, Calhoun VD, Kiehl KA. Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls. FRONTIERS IN NEUROIMAGING 2023; 2:1216494. [PMID: 37554634 PMCID: PMC10406221 DOI: 10.3389/fnimg.2023.1216494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/19/2023] [Indexed: 08/10/2023]
Abstract
Previous work in incarcerated boys and adult men and women suggest that individuals scoring high on psychopathic traits show altered resting-state limbic/paralimbic, and default mode functional network properties. However, it is unclear whether similar results extend to high-risk adolescent girls with elevated psychopathic traits. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist: Youth Version (PCL:YV)] were associated with altered inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of low-frequency fluctuations (ALFFs) across resting-state networks among high-risk incarcerated adolescent girls (n = 40). Resting-state networks were identified by applying group independent component analysis (ICA) to resting-state fMRI scans, and a priori regions of interest included limbic, paralimbic, and default mode network components. We tested the association of psychopathic traits (PCL:YV Factor 1 measuring affective/interpersonal traits and PCL:YV Factor 2 assessing antisocial/lifestyle traits) to these three resting-state measures. PCL:YV Factor 1 scores were associated with increased low-frequency and decreased high-frequency fluctuations in components corresponding to the default mode network, as well as increased intra-network FNC in components corresponding to cognitive control networks. PCL:YV Factor 2 scores were associated with increased low-frequency fluctuations in sensorimotor networks and decreased high-frequency fluctuations in default mode, sensorimotor, and visual networks. Consistent with previous analyses in incarcerated adult women, our results suggest that psychopathic traits among incarcerated adolescent girls are associated with altered intra-network ALFFs-primarily that of increased low-frequency and decreased high-frequency fluctuations-and connectivity across multiple networks including paralimbic regions. These results suggest stable neurobiological correlates of psychopathic traits among women across development.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, Albuquerque, NM, United States
| | | | | | | | - Eyal Aharoni
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | | | | | | | - Vince D. Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
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11
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Du Y, Kong Y, He X. IABC: A Toolbox for Intelligent Analysis of Brain Connectivity. Neuroinformatics 2023; 21:303-321. [PMID: 36609668 DOI: 10.1007/s12021-022-09617-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2022] [Indexed: 01/09/2023]
Abstract
Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject neuroimaging measures for further analysis. After user inputs functional magnetic resonance imaging (fMRI) data of multiple subjects that are regularly organized (e.g., in Brain Imaging Data Structure (BIDS)) and clicks a few buttons to set parameters, IABC automatically outputs brain functional networks, their related time courses, and functional network connectivity of each subject. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Yanshu Kong
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
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12
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Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study. Neuroinformatics 2023; 21:35-43. [PMID: 36018533 PMCID: PMC9931783 DOI: 10.1007/s12021-022-09603-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2022] [Indexed: 10/15/2022]
Abstract
Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.
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13
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Allen CH, Maurer JM, Edwards BG, Gullapalli AR, Harenski CL, Harenski KA, Calhoun VD, Kiehl KA. Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits. FRONTIERS IN NEUROIMAGING 2022; 1:971201. [PMID: 37555166 PMCID: PMC10406317 DOI: 10.3389/fnimg.2022.971201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/20/2022] [Indexed: 08/10/2023]
Abstract
Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, Albuquerque, NM, United States
| | | | - Bethany G. Edwards
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | | | | | | | - Vince D. Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
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14
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Zanella F, Monachesi B, Grecucci A. Resting-state BOLD temporal variability in sensorimotor and salience networks underlies trait emotional intelligence and explains differences in emotion regulation strategies. Sci Rep 2022; 12:15163. [PMID: 36071093 PMCID: PMC9452559 DOI: 10.1038/s41598-022-19477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/30/2022] [Indexed: 11/09/2022] Open
Abstract
A converging body of behavioural findings supports the hypothesis that the dispositional use of emotion regulation (ER) strategies depends on trait emotional intelligence (trait EI) levels. Unfortunately, neuroscientific investigations of such relationship are missing. To fill this gap, we analysed trait measures and resting state data from 79 healthy participants to investigate whether trait EI and ER processes are associated to similar neural circuits. An unsupervised machine learning approach (independent component analysis) was used to decompose resting-sate functional networks and to assess whether they predict trait EI and specific ER strategies. Individual differences results showed that high trait EI significantly predicts and negatively correlates with the frequency of use of typical dysfunctional ER strategies. Crucially, we observed that an increased BOLD temporal variability within sensorimotor and salience networks was associated with both high trait EI and the frequency of use of cognitive reappraisal. By contrast, a decreased variability in salience network was associated with the use of suppression. These findings support the tight connection between trait EI and individual tendency to use functional ER strategies, and provide the first evidence that modulations of BOLD temporal variability in specific brain networks may be pivotal in explaining this relationship.
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Affiliation(s)
- Federico Zanella
- Clinical and Affective Neuroscience Lab - Cli.A.N Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Bianca Monachesi
- Clinical and Affective Neuroscience Lab - Cli.A.N Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy.
| | - Alessandro Grecucci
- Clinical and Affective Neuroscience Lab - Cli.A.N Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
- Centre for Medical Sciences, CISMed, University of Trento, Trento, Italy
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15
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Bremer B, Wu Q, Mora Álvarez MG, Hölzel BK, Wilhelm M, Hell E, Tavacioglu EE, Torske A, Koch K. Mindfulness meditation increases default mode, salience, and central executive network connectivity. Sci Rep 2022; 12:13219. [PMID: 35918449 PMCID: PMC9346127 DOI: 10.1038/s41598-022-17325-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 07/25/2022] [Indexed: 11/09/2022] Open
Abstract
Recent research has begun to identify the neural mechanisms underlying the beneficial impact of mindfulness meditation training (MMT) on health and cognition. However, little is known about the effects of MMT on the global interplay of large-scale networks (LSNs) in the brain. In the present study, healthy, meditation-naïve adults (N = 46) underwent resting state fMRI prior to and upon completing 31 days of MMT or an active control intervention. Independent component analysis, sliding time window, and seed-based correlation analyses were performed to assess training-related changes in functional connectivity (FC) within and between networks with relevance to mindfulness meditation. Across sliding time window analyses and seed-based correlation analyses, we found increased FC between nodes of the default mode network (DMN) and nodes of the salience network (SN) in participants of the MMT. Seed-based correlation analyses revealed further connectivity increases between the SN and key regions of the central executive network (CEN). These results indicate, that, among multiple LSNs, one month of mindfulness meditation effectively increases interconnectivity between networks of the triple network model (DMN, SN, CEN), hereby introducing a potential mechanistic concept underlying the beneficial impact of MMT. Clinical trial registration: This study is listed as a clinical trial on the ISRCTN registry with trial ID ISRCTN95197731 (date of first registration: 15/02/2022).
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Affiliation(s)
- Benno Bremer
- Department of Neuroradiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany. .,TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.
| | - Qiong Wu
- Department of Neuroradiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.,TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.,Institute of Medical Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - María Guadalupe Mora Álvarez
- Department of Neuroradiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.,TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany
| | - Britta Karen Hölzel
- Department of Neuroradiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.,TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany
| | - Maximilian Wilhelm
- TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.,Heidelberg University Hospital, Heidelberg, Germany
| | - Elena Hell
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Ebru Ecem Tavacioglu
- TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.,Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Alyssa Torske
- Department of Neuroradiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.,TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.,Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Martinsried, Germany
| | - Kathrin Koch
- Department of Neuroradiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.,TUM-Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.,Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Martinsried, Germany
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16
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Grami F, de Marco G, Bodranghien F, Manto M, Habas C. Cerebellar Transcranial Direct Current Stimulation Reconfigures Brain Networks Involved in Motor Execution and Mental Imagery. CEREBELLUM (LONDON, ENGLAND) 2022; 21:665-680. [PMID: 34453688 DOI: 10.1007/s12311-021-01322-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
Transcranial direct current stimulation (tDCS) is growingly applied to the cerebellum to modulate the activity of cerebellar circuitry, affecting both motor and cognitive performances in a polarity-specific manner. The remote effects of tDCS are mediated in particular via the dentato-thalamo-cortical pathway. We showed recently that tDCS of the cerebellum exerts dynamic effects on resting state networks. We tested the neural hypothesis that tDCS reconfigurates brain networks involved in motor execution (ME) and motor mental imagery (MMI). We combined tDCS applied over the right cerebellum and fMRI to investigate tDCS-induced reconfiguration of ME- and MMI-related networks using a randomized, sham-controlled design in 21 right-handed healthy volunteers. Subjects were instructed to draw circles at comfortable speed and to imagine drawing circles with their right hand. fMRI data were recorded after real anodal stimulation (1.5 mA, 20 min) or sham tDCS. Real tDCS compared with SHAM specifically reconfigurated the functional links between the main intrinsic connected networks, especially the central executive network, in relation with lobule VII, and the salience network. The right cerebellum mainly influenced prefrontal and anterior cingulate areas in both tasks, and improved the overt motor performance. During MMI, the cerebellum also modulated the default-mode network and associative visual areas. These results demonstrate that tDCS of the cerebellum represents a novel tool to modulate cognitive brain networks controlling motor execution and mental imagery, tuning the activity of remote cortical regions. This approach opens novel doors for the non-invasive neuromodulation of disorders involving cerebello-thalamo-cortical paths.
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Affiliation(s)
- F Grami
- Laboratoire LINP2 Laboratoire Interdisciplinaire de Neurosciences, Physiologie Et Psychologie : Activité Physique, Santé Et Apprentissages, UPL, Université Paris Nanterre, Nanterre, France
| | - G de Marco
- Laboratoire LINP2 Laboratoire Interdisciplinaire de Neurosciences, Physiologie Et Psychologie : Activité Physique, Santé Et Apprentissages, UPL, Université Paris Nanterre, Nanterre, France
| | - F Bodranghien
- Unité D'Etude du Mouvement GRIM, FNRS, ULB-Erasme, Route de Lennik, Bruxelles, Belgium
| | - M Manto
- Services de Neurosciences, UMons, 7000, Mons, Belgium
- Unité Des Ataxies Cérébelleuses, Service de Neurologie, CHU-Charleroi, 6000, Charleroi, Belgium
| | - C Habas
- Service de Neuroimagerie, Centre Hospitalier National D'Ophtalmologie Des Quinze-Vingts, Université Versailles Saint-Quentin, Paris, France.
- Service de NeuroImagerie, CHNO des 15-20, 28, rue de Charenton, 75012, Paris, France.
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17
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Mancuso L, Cavuoti-Cabanillas S, Liloia D, Manuello J, Buzi G, Cauda F, Costa T. Tasks activating the default mode network map multiple functional systems. Brain Struct Funct 2022; 227:1711-1734. [PMID: 35179638 PMCID: PMC9098625 DOI: 10.1007/s00429-022-02467-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/31/2022] [Indexed: 12/30/2022]
Abstract
Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic-extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.
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Affiliation(s)
- Lorenzo Mancuso
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | | | - Donato Liloia
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Giulia Buzi
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | - Franco Cauda
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy.
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
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18
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Oyarzabal EA, Hsu LM, Das M, Chao THH, Zhou J, Song S, Zhang W, Smith KG, Sciolino NR, Evsyukova IY, Yuan H, Lee SH, Cui G, Jensen P, Shih YYI. Chemogenetic stimulation of tonic locus coeruleus activity strengthens the default mode network. SCIENCE ADVANCES 2022; 8:eabm9898. [PMID: 35486721 PMCID: PMC9054017 DOI: 10.1126/sciadv.abm9898] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/15/2022] [Indexed: 05/31/2023]
Abstract
The default mode network (DMN) of the brain is functionally associated with a wide range of behaviors. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the mouse DMN. Chemogenetic-induced tonic LC activity decreased cerebral blood volume (CBV) and glucose uptake and increased synchronous low-frequency fMRI activity within the frontal cortices of the DMN. Fiber photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.
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Affiliation(s)
- Esteban A. Oyarzabal
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC, USA
| | - Li-Ming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Manasmita Das
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Jingheng Zhou
- In Vivo Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Sheng Song
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Weiting Zhang
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Kathleen G. Smith
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Natale R. Sciolino
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Irina Y. Evsyukova
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Hong Yuan
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Guohong Cui
- In Vivo Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Patricia Jensen
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
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19
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Zhutovsky P, Zantvoord JB, Ensink JBM, Op den Kelder R, Lindauer RJL, van Wingen GA. Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity. Neuroimage Clin 2022; 32:102898. [PMID: 34911201 PMCID: PMC8645516 DOI: 10.1016/j.nicl.2021.102898] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/15/2021] [Accepted: 11/23/2021] [Indexed: 01/23/2023]
Abstract
ML and rs-fMRI have shown promise in predicting treatment-response in adults with PTSD. Currently, no biomarkers for treatment-response are available in youth with PTSD. FC between the FPN and SMN was stronger in treatment non-responders on the group-level. A network within the bilateral STG predicted response for individual youth with 76% accuracy. Future studies should test generalizability of these findings and test if larger cohorts increase accuracy.
Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD). However, response varies considerably among individuals. Currently, no biomarkers are available to assist clinicians in identifying youth who are most likely to benefit from treatment. In this study, we investigated whether resting-state functional magnetic resonance imaging (rs-fMRI) could distinguish between responders and non-responders on the group- and individual patient level. Pre-treatment rs-fMRI was recorded in 40 youth (ages 8–17 years) with (partial) PTSD before trauma-focused psychotherapy. Change in symptom severity from pre- to post-treatment was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders (≥30% symptom reduction) and non-responders. Functional networks were identified using meta-independent component analysis. Group-differences within- and between-network connectivity between responders and non-responders were tested using permutation testing. Individual predictions were made using multivariate, cross-validated support vector machine classification. A network centered on the bilateral superior temporal gyrus predicted treatment response for individual patients with 76% accuracy (pFWE = 0.02, 87% sensitivity, 65% specificity, area-under-receiver-operator-curve of 0.82). Functional connectivity between the frontoparietal and sensorimotor network was significantly stronger in non-responders (t = 5.35, pFWE = 0.01) on the group-level. Within-network connectivity was not significantly different between groups. This study provides proof-of-concept evidence for the feasibility to predict trauma-focused psychotherapy response in youth with PTSD at an individual-level. Future studies are required to test if larger cohorts could increase accuracy and to test further generalizability of the prediction models.
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Affiliation(s)
- Paul Zhutovsky
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Jasper B Zantvoord
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Department of Child and Adolescent Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Judith B M Ensink
- Amsterdam UMC, University of Amsterdam, Department of Child and Adolescent Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands; Levvel, Academic Centre for Child and Adolescent Psychiatry, Amsterdam, The Netherlands.
| | - Rosanne Op den Kelder
- Levvel, Academic Centre for Child and Adolescent Psychiatry, Amsterdam, The Netherlands; Research Institute of Child Development and Education, University of Amsterdam, Amsterdam, The Netherlands.
| | - Ramon J L Lindauer
- Amsterdam UMC, University of Amsterdam, Department of Child and Adolescent Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands; Levvel, Academic Centre for Child and Adolescent Psychiatry, Amsterdam, The Netherlands.
| | - Guido A van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands.
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20
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Jarrahi B, McEwen SC, Holschneider DP, Schiehser DM, Petkus AJ, Gomez ME, Van Horn JD, Filoteo V, Jakowec MW, Petzinger GM. The Effects of Cardiorespiratory and Motor Skill Fitness on Intrinsic Functional Connectivity of Neural Networks in Individuals with Parkinson's Disease. Brain Plast 2021; 7:77-95. [PMID: 34868875 PMCID: PMC8609487 DOI: 10.3233/bpl-200115] [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] [Accepted: 01/05/2021] [Indexed: 11/26/2022] Open
Abstract
Background: Studies in aging older adults have shown the positive association between cognition and exercise related fitness, particularly cardiorespiratory fitness. These reports have also demonstrated the association of high cardiorespiratory fitness, as well as other types of fitness, on the reversal of age-related decline in neural network connectivity, highlighting the potential role of fitness on age- and disease-related brain changes. While the clinical benefits of exercise are well-documented in Parkinson’s disease (PD), the extent to which cardiorespiratory fitness (assessed by estimated VO2max testing) or motor skill fitness (assessed by the Physical Performance Test (PPT)) affects neural network connectivity in PD remains to be investigated. The purpose of this study was to explore the hypothesis that higher fitness level is associated with an increase in the intrinsic network connectivity of cognitive networks commonly affected in PD. Methods: In this cross-sectional resting state fMRI, we used a multivariate statistical approach based on high-dimensional independent component analysis (ICA) to investigate the association between two independent fitness metrics (estimated VO2max and PPT) and resting state network connectivity. Results: We found that increased estimated VO2max was associated with increased within network connectivity in cognitive networks known to be impaired in PD, including those sub-serving memory and executive function. There was a similar trend for high levels of PPT to be associated with increased within network connectivity in distinct resting state networks. The between functional network connectivity analysis revealed that cardiorespiratory fitness was associated with increased functional connectivity between somatosensory motor network and several cognitive networks sub-serving memory, attention, and executive function. Conclusion: This study provides important empirical data supporting the potential association between two forms of fitness and multiple resting state networks impacting PD cognition. Linking fitness to circuit specific modulation of resting state network connectivity will help establish a neural basis for the positive effects of fitness and specific exercise modalities and provide a foundation to identify underlying mechanisms to promote repair.
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Affiliation(s)
- Behnaz Jarrahi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, California, USA
| | - Sarah C McEwen
- Pacific Brain Health Center, Pacific Neuroscience Institute, Santa Monica, CA, USA.,Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA, USA
| | - Daniel P Holschneider
- Department of Neurology, University of Southern California, Los Angeles, CA, USA.,Department of Psychiatry and the Behavioral Sciences, University of Southern California, San Pablo St., Los Angeles, CA, USA
| | - Dawn M Schiehser
- Research & Psychology Services, VA San Diego Healthcare System (VASDHS), San Diego, CA, USA.,Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew J Petkus
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Megan E Gomez
- Department of Psychology, Tibor Rubin Veterans Administration Medical Center, Long Beach, CA, USA
| | - Jack D Van Horn
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Vincent Filoteo
- Research & Psychology Services, VA San Diego Healthcare System (VASDHS), San Diego, CA, USA.,Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Michael W Jakowec
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Giselle M Petzinger
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
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21
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Ghasemi M, Foroutannia A, Babajani‐Feremi A. Characterizing resting-state networks in Parkinson's disease: A multi-aspect functional connectivity study. Brain Behav 2021; 11:e02101. [PMID: 33784022 PMCID: PMC8119826 DOI: 10.1002/brb3.2101] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 01/03/2021] [Accepted: 02/21/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Resting-state functional magnetic resonance imaging (Rs-fMRI) can be used to investigate the alteration of resting-state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs. METHODS This study used Rs-fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject-specific score based on group RSNs and a dual-regression approach (namely RSN scores); (2) voxel-wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups. RESULTS The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients. CONCLUSION We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD.
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Affiliation(s)
- Mahdieh Ghasemi
- Neural Engineering LaboratoryDepartment of Biomedical EngineeringUniversity of NeyshaburNeyshaburIran
| | - Ali Foroutannia
- Neural Engineering LaboratoryDepartment of Biomedical EngineeringUniversity of NeyshaburNeyshaburIran
| | - Abbas Babajani‐Feremi
- Department of NeurologyDell Medical SchoolThe University of Texas at AustinAustinTXUSA
- Magnetoencephalography LabDell Children's Medical CenterAustinTXUSA
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22
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Ma Y, MacDonald A. "Impact of ICA Dimensionality on the Test-Retest Reliability of Resting-State Functional Connectivity. Brain Connect 2021; 11:875-886. [PMID: 33926215 DOI: 10.1089/brain.2020.0970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
As resting-state functional connectivity (rsFC) research moves toward the study of individual differences, test-retest reliability is increasingly important to understand. Previous literature supports the test-retest reliability of rsFC derived with independent component analysis (ICA) and dual regression, yet the impact of dimensionality (i.e., the number of components to extract from group-ICA) remained obscure in the current context of large-scale datasets. To provide principled guidelines on this issue, ICA at dimensionalities varying from 25 to 350 was applied to the cortical surface with resting-state functional magnetic resonance imaging data from 1003 participants in the Human Connectome Project. The reliability of two rsFC measures: (within-component) coherence and (between-component) connectivity was estimated. Reliability and its change with dimensionality varied by network: the cognitive (frontoparietal, cingulo-opercular, dorsal attention, and default) networks were measured with the highest reliability which improved with increased dimensionality until at least 150; the visual and somatomotor networks were measured with lower reliability which benefited mildly from increased dimensionality; the temporal pole/orbitofrontal cortex (TP/OFC) network was measured with the lowest reliability. Overall, ICA reliability was optimized at dimensionalities of 150 or above. Compared with two popular binary, non-overlapping cortical atlases, ICA and dual regression resulted in higher reliability for the cognitive networks, lower reliability for the somatomotor network, and similar reliability for the visual and TP/OFC networks. These findings highlight analytical decisions that maximize the reliability of rsFC measures and how they depend on one's networks of interest.
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Affiliation(s)
- Yizhou Ma
- University of Minnesota Twin Cities, 5635, Psychology, Minneapolis, Minnesota, United States;
| | - Angus MacDonald
- University of Minnesota Twin Cities, 5635, Psychology, N219 Elliot Hall 75 E. River Rd., Minneapolis, Minnesota, United States, 55455.,N219 Elliot Hall 75 E. River Rd.Minneapolis, Minnesota, United States, 55455;
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23
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Patil AU, Madathil D, Huang CM. Healthy Aging Alters the Functional Connectivity of Creative Cognition in the Default Mode Network and Cerebellar Network. Front Aging Neurosci 2021; 13:607988. [PMID: 33679372 PMCID: PMC7929978 DOI: 10.3389/fnagi.2021.607988] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 01/19/2021] [Indexed: 02/06/2023] Open
Abstract
Creativity is a higher-order neurocognitive process that produces unusual and unique thoughts. Behavioral and neuroimaging studies of younger adults have revealed that creative performance is the product of dynamic and spontaneous processes involving multiple cognitive functions and interactions between large-scale brain networks, including the default mode network (DMN), fronto-parietal executive control network (ECN), and salience network (SN). In this resting-state functional magnetic resonance imaging (rs-fMRI) study, group independent component analysis (group-ICA) and resting state functional connectivity (RSFC) measures were applied to examine whether and how various functional connected networks of the creative brain, particularly the default-executive and cerebro-cerebellar networks, are altered with advancing age. The group-ICA approach identified 11 major brain networks across age groups that reflected age-invariant resting-state networks. Compared with older adults, younger adults exhibited more specific and widespread dorsal network and sensorimotor network connectivity within and between the DMN, fronto-parietal ECN, and visual, auditory, and cerebellar networks associated with creativity. This outcome suggests age-specific changes in the functional connected network, particularly in the default-executive and cerebro-cerebellar networks. Our connectivity data further elucidate the critical roles of the cerebellum and cerebro-cerebellar connectivity in creativity in older adults. Furthermore, our findings provide evidence supporting the default-executive coupling hypothesis of aging and novel insights into the interactions of cerebro-cerebellar networks with creative cognition in older adults, which suggest alterations in the cognitive processes of the creative aging brain.
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Affiliation(s)
- Abhishek Uday Patil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.,Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Deepa Madathil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.,Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan
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24
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Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis. Neuroinformatics 2021; 19:619-637. [PMID: 33543442 DOI: 10.1007/s12021-021-09514-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2021] [Indexed: 12/12/2022]
Abstract
Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.
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25
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Doucet GE, Labache L, Thompson PM, Joliot M, Frangou S. Atlas55+: Brain Functional Atlas of Resting-State Networks for Late Adulthood. Cereb Cortex 2021; 31:1719-1731. [PMID: 33188411 PMCID: PMC7869083 DOI: 10.1093/cercor/bhaa321] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 09/21/2020] [Accepted: 10/09/2020] [Indexed: 11/14/2022] Open
Abstract
Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional magnetic resonance imaging data from three cohorts of healthy older adults (total N = 563; age = 55-95 years) and a younger-adult cohort (N = 128; age = 18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (P = 2.3 × 10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.
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Affiliation(s)
- Gaelle E Doucet
- Boys Town National Research Hospital, Omaha, NE 68131, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Loic Labache
- GIN, UMR5293, CEA, CNRS, Bordeaux University, Bordeaux 33000, France
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90033, USA
| | - Marc Joliot
- GIN, UMR5293, CEA, CNRS, Bordeaux University, Bordeaux 33000, France
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Centre for Brain Health, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
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26
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Barber AD, Hegarty CE, Lindquist M, Karlsgodt KH. Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks. Cereb Cortex 2021; 31:2834-2844. [PMID: 33429433 DOI: 10.1093/cercor/bhaa391] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 01/26/2023] Open
Abstract
Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.
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Affiliation(s)
- Anita D Barber
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York, 11004, USA.,Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, New York, 11030, USA.,Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | | | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, 21205, USA
| | - Katherine H Karlsgodt
- Department of Psychology, University of California, Los Angeles, 90095, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 90095, USA
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27
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Ng ASL, Wang J, Ng KK, Chong JSX, Qian X, Lim JKW, Tan YJ, Yong ACW, Chander RJ, Hameed S, Ting SKS, Kandiah N, Zhou JH. Distinct network topology in Alzheimer's disease and behavioral variant frontotemporal dementia. ALZHEIMERS RESEARCH & THERAPY 2021; 13:13. [PMID: 33407913 PMCID: PMC7786961 DOI: 10.1186/s13195-020-00752-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/15/2020] [Indexed: 11/18/2022]
Abstract
Background Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits. Methods In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled. Results Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes. Conclusions Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.
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Affiliation(s)
- Adeline Su Lyn Ng
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Juan Wang
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Joanna Su Xian Chong
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xing Qian
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Joseph Kai Wei Lim
- Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yi Jayne Tan
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Alisa Cui Wen Yong
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Russell Jude Chander
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Shahul Hameed
- Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore.,Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, Singapore
| | - Simon Kang Seng Ting
- Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore.,Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, Singapore
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Juan Helen Zhou
- Neuroscience and Behavioral Disorders Program, Duke-NUS Medical School, Singapore, Singapore. .,Centre for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. .,Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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28
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Dørum ES, Kaufmann T, Alnæs D, Richard G, Kolskår KK, Engvig A, Sanders AM, Ulrichsen K, Ihle-Hansen H, Nordvik JE, Westlye LT. Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking. Heliyon 2020; 6:e04854. [PMID: 32995596 PMCID: PMC7501434 DOI: 10.1016/j.heliyon.2020.e04854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 04/25/2020] [Accepted: 09/02/2020] [Indexed: 01/21/2023] Open
Abstract
A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes. We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load. MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.
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Affiliation(s)
- Erlend S. Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Knut K. Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Andreas Engvig
- Department of Internal Medicine, Oslo University Hospital, Norway
| | - Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Hege Ihle-Hansen
- Department of Geriatric Medicine, Oslo University Hospital, Norway
| | | | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway
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29
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Gordon EM, Laumann TO, Marek S, Raut RV, Gratton C, Newbold DJ, Greene DJ, Coalson RS, Snyder AZ, Schlaggar BL, Petersen SE, Dosenbach NUF, Nelson SM. Default-mode network streams for coupling to language and control systems. Proc Natl Acad Sci U S A 2020; 117:17308-17319. [PMID: 32632019 PMCID: PMC7382234 DOI: 10.1073/pnas.2005238117] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.
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Affiliation(s)
- Evan M Gordon
- Veterans Integrated Service Network 17 Center of Excellence for Research on Returning War Veterans, US Department of Veterans Affairs, Waco, TX 76711;
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235
- Department of Psychology and Neuroscience, Baylor University, Waco, TX 76789
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Scott Marek
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Ryan V Raut
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL 60208
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Deanna J Greene
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Rebecca S Coalson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychological & Brain Sciences, Washington University School of Medicine, St. Louis, MO 63110
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M Nelson
- Veterans Integrated Service Network 17 Center of Excellence for Research on Returning War Veterans, US Department of Veterans Affairs, Waco, TX 76711
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235
- Department of Psychology and Neuroscience, Baylor University, Waco, TX 76789
- Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, Bryan, TX 77807
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30
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Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. Neuroimage 2020; 221:117126. [PMID: 32673748 DOI: 10.1016/j.neuroimage.2020.117126] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/12/2020] [Accepted: 06/29/2020] [Indexed: 02/04/2023] Open
Abstract
Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.
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Affiliation(s)
- Kamalaker Dadi
- Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France.
| | - Gaël Varoquaux
- Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France
| | | | | | | | | | - Arthur Mensch
- Inria, CEA, Université Paris-Saclay, Palaiseau, 91120, France; ENS, DMA, 45 Rue D'Ulm, 75005, Paris, France
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31
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Multiple inflammatory profiles of microglia and altered neuroimages in APP/PS1 transgenic AD mice. Brain Res Bull 2020; 156:86-104. [DOI: 10.1016/j.brainresbull.2020.01.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 12/14/2019] [Accepted: 01/03/2020] [Indexed: 12/11/2022]
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32
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Kiviniemi V. Comment to: "Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates". Hum Brain Mapp 2019; 41:1112-1113. [PMID: 31833145 PMCID: PMC7268075 DOI: 10.1002/hbm.24823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/01/2019] [Accepted: 10/03/2019] [Indexed: 11/19/2022] Open
Affiliation(s)
- Vesa Kiviniemi
- Oulu Functional Neuroimaging, Oulu University Hospital, Oulu, Finland
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33
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Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data. Transl Psychiatry 2019; 9:326. [PMID: 31792202 PMCID: PMC6889413 DOI: 10.1038/s41398-019-0663-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 09/30/2019] [Accepted: 11/01/2019] [Indexed: 01/10/2023] Open
Abstract
Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.
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34
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Raitamaa L, Korhonen V, Huotari N, Raatikainen V, Hautaniemi T, Kananen J, Rasila A, Helakari H, Zienkiewicz A, Myllylä T, Borchardt V, Kiviniemi V. Breath hold effect on cardiovascular brain pulsations - A multimodal magnetic resonance encephalography study. J Cereb Blood Flow Metab 2019; 39:2471-2485. [PMID: 30204040 PMCID: PMC6893986 DOI: 10.1177/0271678x18798441] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Ultra-fast functional magnetic resonance encephalography (MREG) enables separate assessment of cardiovascular, respiratory, and vasomotor waves from brain pulsations without temporal aliasing. We examined effects of breath hold- (BH) related changes on cardiovascular brain pulsations using MREG to study the physiological nature of cerebrovascular reactivity. We used alternating 32 s BH and 88 s resting normoventilation (NV) to change brain pulsations during MREG combined with simultaneously measured respiration, continuous non-invasive blood pressure, and cortical near-infrared spectroscopy (NIRS) in healthy volunteers. Changes in classical resting-state network BOLD-like signal and cortical blood oxygenation were reproduced based on MREG and NIRS signals. Cardiovascular pulsation amplitudes of MREG signal from anterior cerebral artery, oxygenated hemoglobin concentration in frontal cortex, and blood pressure decreased after BH. MREG cardiovascular pulse amplitudes in cortical areas and sagittal sinus increased, while cerebrospinal fluid and white matter remained unchanged. Respiratory centers in the brainstem - hypothalamus - thalamus - amygdala network showed strongest increases in cardiovascular pulsation amplitude. The spatial propagation of averaged cardiovascular impulses altered as a function of successive BH runs. The spread of cardiovascular pulse cycles exhibited a decreasing spatial similarity over time. MREG portrayed spatiotemporally accurate respiratory network activity and cardiovascular pulsation dynamics related to BH challenges at an unpreceded high temporal resolution.
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Affiliation(s)
- Lauri Raitamaa
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Niko Huotari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Ville Raatikainen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Taneli Hautaniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Aleksi Rasila
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Heta Helakari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Aleksandra Zienkiewicz
- Biomedical Sensors and Measurement Systems Group, Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
| | - Teemu Myllylä
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland.,Biomedical Sensors and Measurement Systems Group, Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
| | - Viola Borchardt
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
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35
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Parsons N, Bowden SC, Vogrin S, D'Souza WJ. Single-subject manual independent component analysis and resting state fMRI connectivity outcomes in patients with juvenile absence epilepsy. Magn Reson Imaging 2019; 66:42-49. [PMID: 31734272 DOI: 10.1016/j.mri.2019.11.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/17/2019] [Accepted: 11/09/2019] [Indexed: 12/30/2022]
Abstract
The quality of fMRI data impacts functional connectivity measures and consequently, the decisions that clinicians and researchers make regarding functional connectivity interpretation. The present study used resting state fMRI to investigate resting state network connectivity in a sample of patients with Juvenile Absence Epilepsy. Single-subject manual independent component analysis was used in two levels, whereby all noise components were removed, and cerebrospinal fluid pulsation components only were isolated and removed. Improved temporal signal to noise ratios and functional connectivity metrics were observed in each of the cleaning levels for both epilepsy and control cohorts. Results showed full, single-subject manual independent component analysis reduced the number of functional connectivity correlations and increased the strength of these correlations. Similar effects were also observed for the cerebrospinal fluid pulsation only cleaned data relative to the uncleaned, and fully cleaned data. Single-subject manual independent component analysis coupled with short TR multiband acquisition can significantly improve the validity of findings derived from fMRI data sets.
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Affiliation(s)
- Nicholas Parsons
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, 41 Victoria Parade, Fitzroy, VIC 3065, Australia.
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; Department of Medicine, St Vincent's Hospital, The University of Melbourne, 41 Victoria Parade, Fitzroy, VIC 3065, Australia
| | - Simon Vogrin
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, 41 Victoria Parade, Fitzroy, VIC 3065, Australia
| | - Wendyl J D'Souza
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; Department of Medicine, St Vincent's Hospital, The University of Melbourne, 41 Victoria Parade, Fitzroy, VIC 3065, Australia
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36
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Raatikainen V, Korhonen V, Borchardt V, Huotari N, Helakari H, Kananen J, Raitamaa L, Joskitt L, Loukusa S, Hurtig T, Ebeling H, Uddin LQ, Kiviniemi V. Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder. Autism Res 2019; 13:244-258. [PMID: 31637863 PMCID: PMC7027814 DOI: 10.1002/aur.2218] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/28/2019] [Accepted: 09/16/2019] [Indexed: 02/06/2023]
Abstract
This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. Lay Summary Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.
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Affiliation(s)
- Ville Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Viola Borchardt
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Heta Helakari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Janne Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Leena Joskitt
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Tuula Hurtig
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Hanna Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
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37
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The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding. NEUROIMAGE-CLINICAL 2019; 24:101935. [PMID: 31352219 PMCID: PMC6664225 DOI: 10.1016/j.nicl.2019.101935] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 07/10/2019] [Accepted: 07/13/2019] [Indexed: 12/16/2022]
Abstract
Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R2 = 0.18; Replication: adj. R2 = 0.22; MSE = 0.06, Predictive R2 = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R2 = 0.21; Replication: adj. R2 = 0.18; MSE = 0.03, Predictive R2 = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.
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38
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Schumacher J, Peraza LR, Firbank M, Thomas AJ, Kaiser M, Gallagher P, O'Brien JT, Blamire AM, Taylor JP. Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease. Neuroimage Clin 2019; 22:101812. [PMID: 30991620 PMCID: PMC6462776 DOI: 10.1016/j.nicl.2019.101812] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 03/12/2019] [Accepted: 04/02/2019] [Indexed: 01/22/2023]
Abstract
We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.
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Affiliation(s)
- Julia Schumacher
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom.
| | - Luis R Peraza
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom; Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
| | - Michael Firbank
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
| | - Alan J Thomas
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom; Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Peter Gallagher
- Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge CB2 0SP, United Kingdom
| | - Andrew M Blamire
- Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom
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39
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Phase fMRI informs whole-brain function connectivity balance across lifespan with connection-specific aging effects during the resting state. Brain Struct Funct 2019; 224:1489-1503. [PMID: 30826929 DOI: 10.1007/s00429-019-01850-8] [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: 09/28/2018] [Accepted: 02/19/2019] [Indexed: 10/27/2022]
Abstract
A functional magnetic resonance imaging (fMRI) experiment produces complex-valued images consisting of pairwise magnitude and phase images. As different perspective on the same magnetic source, fMRI magnitude and phase data are complementary for brain function analysis. We collected 600-subject fMRI data during rest, decomposed via group-level independent component analysis (ICA) (mICA and pICA for magnitude and phase respectively), and calculated brain functional network connectivity matrices (mFC and pFC). The pFC matrix shows a fewer of significant connections balanced across positive and negative relationships. In comparison, the mFC matrix contains a positively-biased pattern with more significant connections. Our experiment data analyses also show that human brain maintains a whole-brain connection balance in resting state across an age span from 10 to 76 years, however, phase and magnitude data analyses reveal different connection-specific age effects on significant positive and negative subnetwork couplings.
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40
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Ghasemi M, Foroutannia A. Disruption of the Brain Resting State Networks in Parkinsonism. ACTA ACUST UNITED AC 2019. [DOI: 10.29252/shefa.7.1.23] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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41
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Wang J, Khosrowabadi R, Ng KK, Hong Z, Chong JSX, Wang Y, Chen CY, Hilal S, Venketasubramanian N, Wong TY, Chen CLH, Ikram MK, Zhou J. Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment. Front Aging Neurosci 2018; 10:404. [PMID: 30618711 PMCID: PMC6300727 DOI: 10.3389/fnagi.2018.00404] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 11/23/2018] [Indexed: 12/13/2022] Open
Abstract
According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases.
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Affiliation(s)
- Juan Wang
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Reza Khosrowabadi
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.,Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Kwun Kei Ng
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Zhaoping Hong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Joanna Su Xian Chong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yijun Wang
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Chun-Yin Chen
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore, Singapore
| | | | - Tien Yin Wong
- Memory Aging & Cognition Centre, National University Health System, Singapore, Singapore.,Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | | | - Mohammad Kamran Ikram
- Department of Pharmacology, National University of Singapore, Singapore, Singapore.,Memory Aging & Cognition Centre, National University Health System, Singapore, Singapore.,Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.,Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.,Clinical Imaging Research Centre, The Agency for Science, Technology and Research-National University of Singapore, Singapore, Singapore
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42
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Rudorf S, Baumgartner T, Markett S, Schmelz K, Wiest R, Fischbacher U, Knoch D. Intrinsic connectivity networks underlying individual differences in control-averse behavior. Hum Brain Mapp 2018; 39:4857-4869. [PMID: 30156744 PMCID: PMC6866545 DOI: 10.1002/hbm.24328] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 06/21/2018] [Accepted: 07/12/2018] [Indexed: 11/10/2022] Open
Abstract
When people sense that another person tries to control their decisions, some people will act against the control, whereas others will not. This individual tendency to control-averse behavior can have far-reaching consequences, such as engagement in illegal activities or noncompliance with medical treatments. Although individual differences in control-averse behavior have been well documented in behavioral studies, their neurological basis is less well understood. Here, we use a neural trait approach to examine whether individual differences in control-averse behavior might be linked to stable brain-based characteristics. To do so, we analyze the association between intrinsic connectivity networks as measured by resting state functional magnetic resonance imaging and control-averse behavior in an economic exchange game. In this game, subjects make choices that are either free or controlled by another person, with real consequences to both interaction partners. We find that the individual level of control-averse behavior can be positively predicted by intrinsic connectivity within the salience network, but not the central executive network or the default mode network. Specifically, subjects with a more prominent connectivity hub in the dorsal anterior cingulate cortex show greater levels of control-averse behavior. This finding provides the first evidence that the heterogeneity in control-averse behavior might originate in systematic differences of the stable functional brain organization.
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Affiliation(s)
- Sarah Rudorf
- Department of Social Psychology and Social NeuroscienceInstitute of Psychology, University of BernBernSwitzerland
- Center for Cognition, Learning and MemoryUniversity of BernBernSwitzerland
| | - Thomas Baumgartner
- Department of Social Psychology and Social NeuroscienceInstitute of Psychology, University of BernBernSwitzerland
- Center for Cognition, Learning and MemoryUniversity of BernBernSwitzerland
| | - Sebastian Markett
- Molecular Psychology, Department of PsychologyHumboldt University BerlinBerlinGermany
| | - Katrin Schmelz
- Department of EconomicsUniversity of KonstanzKonstanzGermany
- Thurgau Institute of EconomicsKreuzlingenSwitzerland
| | - Roland Wiest
- Department of NeuroradiologyInselspitalBernSwitzerland
| | - Urs Fischbacher
- Department of EconomicsUniversity of KonstanzKonstanzGermany
- Thurgau Institute of EconomicsKreuzlingenSwitzerland
| | - Daria Knoch
- Department of Social Psychology and Social NeuroscienceInstitute of Psychology, University of BernBernSwitzerland
- Center for Cognition, Learning and MemoryUniversity of BernBernSwitzerland
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43
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Sala-Llonch R, Smith SM, Woolrich M, Duff EP. Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination. Hum Brain Mapp 2018; 40:407-419. [PMID: 30259597 PMCID: PMC6492132 DOI: 10.1002/hbm.24381] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 08/21/2018] [Accepted: 08/24/2018] [Indexed: 01/03/2023] Open
Abstract
The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches, and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled data set of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation, and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p < .05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high‐dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA‐based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC.
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Affiliation(s)
- Roser Sala-Llonch
- Faculty of Medicine, Department of Biomedicine, University of Barcelona, Barcelona, Spain
| | - Stephen M Smith
- Nuffield Department of Clinical Neurosciences, Functional Magnetic Resonance Imaging of the Brain Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Mark Woolrich
- Department of Psychiatry, Oxford University Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Eugene P Duff
- Nuffield Department of Clinical Neurosciences, Functional Magnetic Resonance Imaging of the Brain Centre, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.,Department of Paediatrics, University of Oxford, Oxford, United Kingdom
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Functional Connectivity Alterations in Children with Spastic and Dyskinetic Cerebral Palsy. Neural Plast 2018; 2018:7058953. [PMID: 30186320 PMCID: PMC6114065 DOI: 10.1155/2018/7058953] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 06/01/2018] [Accepted: 06/06/2018] [Indexed: 01/28/2023] Open
Abstract
Cerebral palsy (CP) has long been investigated to be associated with a range of motor and cognitive dysfunction. As the two most common CP subtypes, spastic cerebral palsy (SCP) and dyskinetic cerebral palsy (DCP) may share common and distinct elements in their pathophysiology. However, the common and distinct dysfunctional characteristics between SCP and DCP on the brain network level are less known. This study aims to detect the alteration of brain functional connectivity in children with SCP and DCP based on resting-state functional MRI (fMRI). Resting-state networks (RSNs) were established based on the independent component analysis (ICA), and the functional network connectivity (FNC) was performed on the fMRI data from 16 DCP, 18 bilateral SCP, and 18 healthy children. Compared with healthy controls, altered functional connectivity within the cerebellum network, sensorimotor network (SMN), left frontoparietal network (LFPN), and salience network (SN) were found in DCP and SCP groups. Furthermore, the disconnections of the FNC consistently focused on the visual pathway; covariance of the default mode network (DMN) with other networks was observed both in DCP and SCP groups, while the DCP group had a distinct connectivity abnormality in motor pathway and self-referential processing-related connections. Correlations between the functional disconnection and the motor-related clinical measurement in children with CP were also found. These findings indicate functional connectivity impairment and altered integration widely exist in children with CP, suggesting that the abnormal functional connectivity is a pathophysiological mechanism of motor and cognitive dysfunction of CP.
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45
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Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia. J Neurosci Methods 2018; 304:24-38. [DOI: 10.1016/j.jneumeth.2018.02.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 02/05/2018] [Accepted: 02/20/2018] [Indexed: 01/05/2023]
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46
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Nikolaou F, Orphanidou C, Murphy K, Wise RG, Mitsis GD. Investigation Of Interaction Between Physiological Signals And fMRI Dynamic Functional Connectivity Using Independent Component Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1019-1023. [PMID: 30440564 DOI: 10.1109/embc.2018.8512465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The blood oxygen level dependent (BOLD) fMRI signal is influenced not only by neuronal activity but also by fluctuations in physiological signals, including respiration, arterial CO2 and heart rate/ heart rate variability (HR/HRV). Even spontaneous physiological signal fluctuations have been shown to influence the BOLD fMRI signal in a regionally specific manner. Consequently, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. In addition, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity - DFC), with the sources of this variation not fully elucidated. The effect of physiological factors on dynamic (time-varying) resting-state functional connectivity has not been studied extensively, to our knowledge. In our previous study, we investigated the effect of heart rate (HR) and end-tidal CO2 (PETCO2) on the time-varying network degree of three well-described RSNs (DMN, SMN and Visual Network) using mask-based and seed-based analysis, and we identified brain-heart interactions which were more pronounced in specific frequency bands. Here, we extend this work, by estimating DFC and its corresponding network degree for the RSNs, employing a data-driven approach to extract the RSNs (low-and high-dimensional Independent Component Analysis (ICA)), which we subsequently correlate with the characteristics of simultaneously collected physiological signals. The results confirm that physiological signals have a modulatory effect on resting-state, fMRI-based DFC.
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47
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Bi XA, Sun Q, Zhao J, Xu Q, Wang L. Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment. Front Neurosci 2018; 12:413. [PMID: 29970984 PMCID: PMC6018085 DOI: 10.3389/fnins.2018.00413] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 05/30/2018] [Indexed: 01/02/2023] Open
Abstract
Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.
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Affiliation(s)
- Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Junxia Zhao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Liqin Wang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
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Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 4:16-26. [PMID: 29980494 DOI: 10.1016/j.bpsc.2018.05.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 05/10/2018] [Accepted: 05/21/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND Depression is a complex disorder with large interindividual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains, such as anxiety. A dimensional and symptom-based approach may help refine the characterization of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals. METHODS We assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252). RESULTS We identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different total severity, symptom patterns, and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, respectively. These subgroups showed evidence of differential static brain-connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity, or global connectivity. CONCLUSIONS Adding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain.
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Keinänen T, Rytky S, Korhonen V, Huotari N, Nikkinen J, Tervonen O, Palva JM, Kiviniemi V. Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network. J Neurosci Res 2018; 96:1689-1698. [PMID: 29761531 DOI: 10.1002/jnr.24257] [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: 01/26/2018] [Revised: 04/20/2018] [Accepted: 04/23/2018] [Indexed: 01/14/2023]
Abstract
Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.
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Affiliation(s)
- Tuija Keinänen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.,Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Seppo Rytky
- Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Juha Nikkinen
- Department of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland
| | - Osmo Tervonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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50
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Schumacher J, Peraza LR, Firbank M, Thomas AJ, Kaiser M, Gallagher P, O'Brien JT, Blamire AM, Taylor J. Functional connectivity in dementia with Lewy bodies: A within- and between-network analysis. Hum Brain Mapp 2018; 39:1118-1129. [PMID: 29193464 PMCID: PMC5900719 DOI: 10.1002/hbm.23901] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 10/28/2017] [Accepted: 11/20/2017] [Indexed: 01/07/2023] Open
Abstract
Dementia with Lewy bodies (DLB) is a common form of dementia and is characterized by cognitive fluctuations, visual hallucinations, and Parkinsonism. The phenotypic expression of the disease may, in part, relate to alterations in functional connectivity within and between brain networks. This resting-state study sought to clarify this in DLB, how networks differed from Alzheimer's disease (AD), and whether they were related to clinical symptoms in DLB. Resting-state networks were estimated using independent component analysis. We investigated functional connectivity changes in 31 DLB patients compared to 31 healthy controls and a disease comparator group of 29 AD patients using dual regression and FSLNets. Within-network connectivity was generally decreased in DLB compared to controls, mainly in motor, temporal, and frontal networks. Between-network connectivity was mainly intact; only the connection between a frontal and a temporal network showed increased connectivity in DLB. Differences between AD and DLB were subtle and we did not find any significant correlations with the severity of clinical symptoms in DLB. This study emphasizes the importance of reduced connectivity within motor, frontal, and temporal networks in DLB with relative sparing of the default mode network. The lack of significant correlations between connectivity measures and clinical scores indicates that the observed reduced connectivity within these networks might be related to the presence, but not to the severity of motor and cognitive impairment in DLB patients. Furthermore, our results suggest that AD and DLB may show more similarities than differences in patients with mild disease.
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Affiliation(s)
- Julia Schumacher
- Institute of Neuroscience, Newcastle University, Campus for Ageing and VitalityNewcastle upon TyneNE4 5PLUnited Kingdom
| | - Luis R. Peraza
- Institute of Neuroscience, Newcastle University, Campus for Ageing and VitalityNewcastle upon TyneNE4 5PLUnited Kingdom
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle UniversityNewcastle upon TyneNE4 5TGUnited Kingdom
| | - Michael Firbank
- Institute of Neuroscience, Newcastle University, Campus for Ageing and VitalityNewcastle upon TyneNE4 5PLUnited Kingdom
| | - Alan J. Thomas
- Institute of Neuroscience, Newcastle University, Campus for Ageing and VitalityNewcastle upon TyneNE4 5PLUnited Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle UniversityNewcastle upon TyneNE4 5TGUnited Kingdom
- Institute of Neuroscience, Newcastle University, The Henry Wellcome BuildingNewcastle upon TyneNE2 4HHUnited Kingdom
| | - Peter Gallagher
- Institute of Neuroscience, Newcastle University, The Henry Wellcome BuildingNewcastle upon TyneNE2 4HHUnited Kingdom
| | - John T. O'Brien
- Department of PsychiatryUniversity of Cambridge School of MedicineCambridgeCB2 0SPUnited Kingdom
| | - Andrew M. Blamire
- Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Campus for Ageing and VitalityNewcastle upon TyneNE4 5PLUnited Kingdom
| | - John‐Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and VitalityNewcastle upon TyneNE4 5PLUnited Kingdom
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