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Kumar VA, Lee J, Liu HL, Allen JW, Filippi CG, Holodny AI, Hsu K, Jain R, McAndrews MP, Peck KK, Shah G, Shimony JS, Singh S, Zeineh M, Tanabe J, Vachha B, Vossough A, Welker K, Whitlow C, Wintermark M, Zaharchuk G, Sair HI. Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy. AJNR Am J Neuroradiol 2024; 45:139-148. [PMID: 38164572 DOI: 10.3174/ajnr.a8067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 10/12/2023] [Indexed: 01/03/2024]
Abstract
Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.
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Affiliation(s)
- V A Kumar
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J Lee
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - H-L Liu
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J W Allen
- Emory University (J.W.A.), Atlanta, Georgia
| | - C G Filippi
- Tufts University (C.G.F.), Boston, Massachusetts
| | - A I Holodny
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - K Hsu
- New York University (K.H., R.J.), New York, New York
| | - R Jain
- New York University (K.H., R.J.), New York, New York
| | - M P McAndrews
- University of Toronto (M.P.M.), Toronto, Ontario, Canada
| | - K K Peck
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - G Shah
- University of Michigan (G.S.), Ann Arbor, Michigan
| | - J S Shimony
- Washington University School of Medicine (J.S.S.), St. Louis, Missouri
| | - S Singh
- University of Texas Southwestern Medical Center (S.S.), Dallas, Texas
| | - M Zeineh
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - J Tanabe
- University of Colorado (J.T.), Aurora, Colorado
| | - B Vachha
- University of Massachusetts (B.V.), Worcester, Massachusetts
| | - A Vossough
- Children's Hospital of Philadelphia, University of Pennsylvania (A.V.), Philadelphia, Pennsylvania
| | - K Welker
- Mayo Clinic (K.W.), Rochester, Minnesota
| | - C Whitlow
- Wake Forest University (C.W.), Winston-Salem, North Carolina
| | - M Wintermark
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - G Zaharchuk
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - H I Sair
- Johns Hopkins University (H.I.S.), Baltimore, Maryland
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de Jong JJA, Jansen JFA, Vergoossen LWM, Schram MT, Stehouwer CDA, Wildberger JE, Linden DEJ, Backes WH. Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study. Brain Sci 2024; 14:62. [PMID: 38248277 PMCID: PMC10813868 DOI: 10.3390/brainsci14010062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/27/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.
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Affiliation(s)
- Joost J. A. de Jong
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Laura W. M. Vergoossen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Miranda T. Schram
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Coen D. A. Stehouwer
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - David E. J. Linden
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
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Hu P, Wang P, Zhao R, Yang H, Biswal BB. Characterizing the spatiotemporal features of functional connectivity across the white matter and gray matter during the naturalistic condition. Front Neurosci 2023; 17:1248610. [PMID: 38027509 PMCID: PMC10665512 DOI: 10.3389/fnins.2023.1248610] [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] [Received: 06/27/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The naturalistic stimuli due to its ease of operability has attracted many researchers in recent years. However, the influence of the naturalistic stimuli for whole-brain functions compared with the resting state is still unclear. Methods In this study, we clustered gray matter (GM) and white matter (WM) masks both at the ROI- and network-levels. Functional connectivity (FC) and inter-subject functional connectivity (ISFC) were calculated in GM, WM, and between GM and WM under the movie-watching and the resting-state conditions. Furthermore, intra-class correlation coefficients (ICC) of FC and ISFC were estimated on different runs of fMRI data to denote the reliability of them during the two conditions. In addition, static and dynamic connectivity indices were calculated with Pearson correlation coefficient to demonstrate the associations between the movie-watching and the resting-state. Results As the results, we found that the movie-watching significantly affected FC in whole-brain compared with the resting-state, but ISFC did not show significant connectivity induced by the naturalistic condition. ICC of FC and ISFC was generally higher during movie-watching compared with the resting-state, demonstrating that naturalistic stimuli could promote the reliability of connectivity. The associations between static and dynamic ISFC were weakly negative correlations in the naturalistic stimuli while there is no correlation between them under resting-state condition. Discussion Our findings confirmed that compared to resting-state condition, the connectivity indices under the naturalistic stimuli were more reliable and stable to investigate the normal functional activities of the human brain, and might promote the applications of FC in the cerebral dysfunction in various mental disorders.
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Affiliation(s)
- Peng Hu
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Rong Zhao
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Yang
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Institute for Brain Research, Beijing, China
| | - Bharat B. Biswal
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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Davies-Owen JA, Stancak A, Giesbrecht T, Thomas A, Kirkham TC, Roberts CA. Neural correlates of naturalistic single-trial appetitive conditioning. Physiol Behav 2023; 271:114350. [PMID: 37714323 DOI: 10.1016/j.physbeh.2023.114350] [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: 03/30/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND Prior research suggests naturalistic single-trial appetitive conditioning may be a potent phenomenon in humans, capable of modulating both motivation and attention. In this study, we aimed to characterise the neural correlates of this phenomenon using functional Magnetic Resonance Imaging (fMRI) paradigms METHODS: Twenty-three healthy adults (12 males) underwent conditioning during which they ate a novel 3D object made from white chocolate (CS+) and handled a similar object made from plastic (CS-). Brain activity was recorded before and after conditioning during a passive viewing paradigm RESULTS: A naturalistic CS+ was rated as more highly craved, better-liked and elicited greater expectancies for chocolate than the CS- after conditioning. An exploration of the interaction between time (pre- and post-conditioning) and CS type (CS+, CS-) during the passive viewing task suggested enhanced activation from pre- to post-conditioning in the right superior frontal gyrus (R.SFG) in response to the CS-. CONCLUSION Results reveal neural correlates of single-trial appetitive conditioning and highlight a possible role of response inhibition during learning about non-rewards, perhaps optimizing motivated behaviour. These findings contribute to our understanding of the neural mechanisms underpinning rapid reward and non-reward learning, and may inform development of behavioural interventions for reward-driven overeating.
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Affiliation(s)
- Jennifer A Davies-Owen
- Department of Psychology, Institute of Population Health, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, England, UK
| | - Andrej Stancak
- Department of Psychology, Institute of Population Health, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, England, UK
| | - Timo Giesbrecht
- Unilever Research and Development, Port Sunlight, Quarry Road East, Bebington, CH63 3JW, England, UK
| | - Anna Thomas
- Unilever Research and Development, Port Sunlight, Quarry Road East, Bebington, CH63 3JW, England, UK
| | - Tim C Kirkham
- Department of Psychology, Institute of Population Health, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, England, UK
| | - Carl A Roberts
- Department of Psychology, Institute of Population Health, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, England, UK.
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Gao J, Zhao L, Zhong T, Li C, He Z, Wei Y, Zhang S, Guo L, Liu T, Han J, Jiang X, Zhang T. Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet. PSYCHORADIOLOGY 2023; 3:kkad011. [PMID: 38666131 PMCID: PMC10939348 DOI: 10.1093/psyrad/kkad011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/14/2023] [Accepted: 07/06/2023] [Indexed: 04/28/2024]
Abstract
Background Brain functional connectivity under the naturalistic paradigm has been shown to be better at predicting individual behaviors than other brain states, such as rest and doing tasks. Nevertheless, the state-of-the-art methods have found it difficult to achieve desirable results from movie-watching paradigm functional magnetic resonance imaging (mfMRI) -induced brain functional connectivity, especially when there are fewer datasets. Incorporating other physical measurements into the prediction method may enhance accuracy. Eye tracking, becoming popular due to its portability and lower expense, can provide abundant behavioral features related to the output of human's cognition, and thus might supplement the mfMRI in observing participants' subconscious behaviors. However, there are very few studies on how to effectively integrate the multimodal information to strengthen the performance by a unified framework. Objective A fusion approach with mfMRI and eye tracking, based on convolution with edge-node switching in graph neural networks (CensNet), is proposed in this article. Methods In this graph model, participants are designated as nodes, mfMRI derived functional connectivity as node features, and different eye-tracking features are used to compute similarity between participants to construct heterogeneous graph edges. By taking multiple graphs as different channels, we introduce squeeze-and-excitation attention module to CensNet (A-CensNet) to integrate graph embeddings from multiple channels into one. Results The proposed model outperforms those using a single modality and single channel, and state-of-the-art methods. Conclusions The results indicate that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.
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Affiliation(s)
- Jiaxing Gao
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lin Zhao
- Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Tianyang Zhong
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Changhe Li
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yaonei Wei
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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6
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Pollak C, Kügler D, Breteler MMB, Reuter M. Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts. Neuroimage 2023; 275:120176. [PMID: 37209757 DOI: 10.1016/j.neuroimage.2023.120176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023] Open
Abstract
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
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Affiliation(s)
- Clemens Pollak
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - David Kügler
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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7
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Lugtmeijer S, Geerligs L, Tsvetanov KA, Mitchell DJ, Cam-Can, Campbell KL. Lifespan differences in visual short-term memory load-modulated functional connectivity. Neuroimage 2023; 270:119982. [PMID: 36848967 DOI: 10.1016/j.neuroimage.2023.119982] [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: 11/29/2022] [Revised: 02/19/2023] [Accepted: 02/23/2023] [Indexed: 02/27/2023] Open
Abstract
Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge center for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N = 101, age 23-86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated functional connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention and visual networks during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Whole-brain analyses for the relation between connectivity and behavior were non-significant. Our results give additional support to the sensory recruitment model of working memory. We also demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their neural resources at the lowest load and therefore less able to further increase connectivity with increasing task demands.
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Affiliation(s)
- Selma Lugtmeijer
- Department of Psychology, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, Canada.
| | - Linda Geerligs
- Radboud University, Thomas van Aquinostraat 4, 6525 GD, Nijmegen, the Netherlands.
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, United Kingdom; Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom.
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
| | - Karen L Campbell
- Department of Psychology, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, Canada.
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8
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Eikenes L, Visser E, Vangberg T, Håberg AK. Both brain size and biological sex contribute to variation in white matter microstructure in middle-aged healthy adults. Hum Brain Mapp 2022; 44:691-709. [PMID: 36189786 PMCID: PMC9842919 DOI: 10.1002/hbm.26093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 01/25/2023] Open
Abstract
Whether head size and/or biological sex influence proxies of white matter (WM) microstructure such as fractional anisotropy (FA) and mean diffusivity (MD) remains controversial. Diffusion tensor imaging (DTI) indices are also associated with age, but there are large discrepancies in the spatial distribution and timeline of age-related differences reported. The aim of this study was to evaluate the associations between intracranial volume (ICV), sex, and age and DTI indices from WM in a population-based study of healthy individuals (n = 812) aged 50-66 in the Nord-Trøndelag health survey. Semiautomated tractography and tract-based spatial statistics (TBSS) analyses were performed on the entire sample and in an ICV-matched sample of men and women. The tractography results showed a similar positive association between ICV and FA in all major WM tracts in men and women. Associations between ICV and MD, radial diffusivity and axial diffusivity were also found, but to a lesser extent than FA. The TBSS results showed that both men and women had areas of higher and lower FA when controlling for age, but after controlling for age and ICV only women had areas with higher FA. The ICV matched analysis also demonstrated that only women had areas of higher FA. Age was negatively associated with FA across the entire WM skeleton in the TBSS analysis, independent of both sex and ICV. Combined, these findings demonstrated that both ICV and sex contributed to variation in DTI indices and emphasized the importance of considering ICV as a covariate in DTI analysis.
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Affiliation(s)
- Live Eikenes
- Department of Circulation and Medical ImagingNorwegian University of Science and TechnologyTrondheimNorway
| | - Eelke Visser
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK,Donders InstituteRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
| | - Torgil Vangberg
- Department of Clinical MedicineUiT The Arctic University of NorwayTromsøNorway,PET CenterUniversity Hospital North NorwayTromsøNorway
| | - Asta K. Håberg
- Department of NeuroscienceNorwegian University of Science and TechnologyTrondheimNorway,Department of Diagnostic Imaging, MR‐CenterSt. Olav's University HospitalTrondheimNorway
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9
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Finn ES. Is it time to put rest to rest? Trends Cogn Sci 2021; 25:1021-1032. [PMID: 34625348 PMCID: PMC8585722 DOI: 10.1016/j.tics.2021.09.005] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 12/29/2022]
Abstract
The so-called resting state, in which participants lie quietly with no particular inputs or outputs, represented a paradigm shift from conventional task-based studies in human neuroimaging. Our foray into rest was fruitful from both a scientific and methodological perspective, but at this point, how much more can we learn from rest on its own? While rest still dominates in many subfields, data from tasks have empirically demonstrated benefits, as well as the potential to provide insights about the mind in addition to the brain. I argue that we can accelerate progress in human neuroscience by de-emphasizing rest in favor of more grounded experiments, including promising integrated designs that respect the prominence of self-generated activity while offering enhanced control and interpretability.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
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10
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Borbás R, Fehlbaum LV, Rudin U, Stadler C, Raschle NM. Neural correlates of theory of mind in children and adults using CAToon: Introducing an open-source child-friendly neuroimaging task. Dev Cogn Neurosci 2021; 49:100959. [PMID: 33989857 PMCID: PMC8134957 DOI: 10.1016/j.dcn.2021.100959] [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] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 04/27/2021] [Accepted: 05/01/2021] [Indexed: 01/05/2023] Open
Abstract
Theory of Mind (ToM) or mentalizing is a basic social skill which is characterized by our ability of perspective-taking and the understanding of cognitive and emotional states of others. ToM development is essential to successfully navigate in various social contexts. The neural basis of mentalizing is well-studied in adults, however, less evidence exists in children. Potential reasons are methodological challenges, including a lack of age-appropriate fMRI paradigms. We introduce a novel child-friendly and open-source ToM fMRI task, for which accuracy and performance were evaluated behaviorally in 60 children ages three to nine (32♂). Furthermore, 27 healthy young adults (14♂; mean = 25.41 years) and 33 children ages seven to thirteen (17♂; mean = 9.06 years) completed the Cognitive and Affective Theory of Mind Cartoon task (CAToon;www.jacobscenter.uzh.ch/en/research/developmental_neuroscience/downloads/catoon.html) during a fMRI session. Behavioral results indicate that children of all ages can solve the CAToon task above chance level, though reliable performance is reached around five years. Neurally, activation increases were observed for adults and children in brain regions previously associated with mentalizing, including bilateral temporoparietal junction, temporal gyri, precuneus and medial prefrontal/orbitofrontal cortices. We conclude that CAToon is suitable for developmental neuroimaging studies within an fMRI environment starting around preschool and up.
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Affiliation(s)
- Réka Borbás
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland; Department of Child and Adolescent Psychiatry, University of Basel, University Psychiatric Clinics Basel, Basel, Switzerland
| | - Lynn V Fehlbaum
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland; Department of Child and Adolescent Psychiatry, University of Basel, University Psychiatric Clinics Basel, Basel, Switzerland
| | - Ursula Rudin
- Department of Child and Adolescent Psychiatry, University of Basel, University Psychiatric Clinics Basel, Basel, Switzerland
| | - Christina Stadler
- Department of Child and Adolescent Psychiatry, University of Basel, University Psychiatric Clinics Basel, Basel, Switzerland
| | - Nora M Raschle
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland; Department of Child and Adolescent Psychiatry, University of Basel, University Psychiatric Clinics Basel, Basel, Switzerland.
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11
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Finn ES, Bandettini PA. Movie-watching outperforms rest for functional connectivity-based prediction of behavior. Neuroimage 2021; 235:117963. [PMID: 33813007 PMCID: PMC8204673 DOI: 10.1016/j.neuroimage.2021.117963] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/23/2021] [Accepted: 03/08/2021] [Indexed: 01/31/2023] Open
Abstract
A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing—i.e., movie watching—yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.
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Affiliation(s)
- Emily S Finn
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States
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12
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Mascali D, Moraschi M, DiNuzzo M, Tommasin S, Fratini M, Gili T, Wise RG, Mangia S, Macaluso E, Giove F. Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks. Hum Brain Mapp 2021; 42:1805-1828. [PMID: 33528884 PMCID: PMC7978116 DOI: 10.1002/hbm.25332] [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: 04/15/2020] [Revised: 12/04/2020] [Accepted: 12/17/2020] [Indexed: 11/17/2022] Open
Abstract
In‐scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in‐scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition‐dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion‐related artifacts between resting‐state and task conditions. Denoising pipelines—including realignment/tissue‐based regression, PCA/ICA‐based methods (aCompCor and ICA‐AROMA, respectively), global signal regression, and censoring of motion‐contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance‐dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance‐dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task‐based functional connectivity data, and more generally for resting‐state data, are discussed.
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Affiliation(s)
- Daniele Mascali
- MARBILab, CREF - Centro Ricerche Enrico Fermi, Roma, 00184, Italy.,Fondazione Santa Lucia IRCCS, Roma, Italy
| | - Marta Moraschi
- MARBILab, CREF - Centro Ricerche Enrico Fermi, Roma, 00184, Italy.,Fondazione Santa Lucia IRCCS, Roma, Italy
| | - Mauro DiNuzzo
- MARBILab, CREF - Centro Ricerche Enrico Fermi, Roma, 00184, Italy.,Fondazione Santa Lucia IRCCS, Roma, Italy
| | - Silvia Tommasin
- Dipartimento di Neuroscienze umane, Sapienza Università di Roma, Roma, Italy
| | - Michela Fratini
- Fondazione Santa Lucia IRCCS, Roma, Italy.,Istituto di Nanotecnologia, Consiglio Nazionale delle Ricerche, Roma, Italy
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Richard G Wise
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK.,Institute for Advanced Biomedical Technologies, "G. D'Annunzio University" of Chieti-Pescara, Chieti, Italy.,Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti, Italy
| | - Silvia Mangia
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Federico Giove
- MARBILab, CREF - Centro Ricerche Enrico Fermi, Roma, 00184, Italy.,Fondazione Santa Lucia IRCCS, Roma, Italy
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13
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Boenniger MM, Diers K, Herholz SC, Shahid M, Stöcker T, Breteler MMB, Huijbers W. A Functional MRI Paradigm for Efficient Mapping of Memory Encoding Across Sensory Conditions. Front Hum Neurosci 2021; 14:591721. [PMID: 33551773 PMCID: PMC7859438 DOI: 10.3389/fnhum.2020.591721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 12/02/2020] [Indexed: 11/13/2022] Open
Abstract
We introduce a new and time-efficient memory-encoding paradigm for functional magnetic resonance imaging (fMRI). This paradigm is optimized for mapping multiple contrasts using a mixed design, using auditory (environmental/vocal) and visual (scene/face) stimuli. We demonstrate that the paradigm evokes robust neuronal activity in typical sensory and memory networks. We were able to detect auditory and visual sensory-specific encoding activities in auditory and visual cortices. Also, we detected stimulus-selective activation in environmental-, voice-, scene-, and face-selective brain regions (parahippocampal place and fusiform face area). A subsequent recognition task allowed the detection of sensory-specific encoding success activity (ESA) in both auditory and visual cortices, as well as sensory-unspecific positive ESA in the hippocampus. Further, sensory-unspecific negative ESA was observed in the precuneus. Among others, the parallel mixed design enabled sustained and transient activity comparison in contrast to rest blocks. Sustained and transient activations showed great overlap in most sensory brain regions, whereas several regions, typically associated with the default-mode network, showed transient rather than sustained deactivation. We also show that the use of a parallel mixed model had relatively little influence on positive or negative ESA. Together, these results demonstrate a feasible, versatile, and brief memory-encoding task, which includes multiple sensory stimuli to guarantee a comprehensive measurement. This task is especially suitable for large-scale clinical or population studies, which aim to test task-evoked sensory-specific and sensory-unspecific memory-encoding performance as well as broad sensory activity across the life span within a very limited time frame.
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Affiliation(s)
- Meta M. Boenniger
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Kersten Diers
- Image Analysis Group, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Sibylle C. Herholz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Mohammad Shahid
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Tony Stöcker
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Monique M. B. Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Willem Huijbers
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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14
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Jolly E, Sadhukha S, Chang LJ. Response to Lynch et al: On measuring head motion and effects of head molds during fMRI. Neuroimage 2021; 225:117484. [PMID: 33160085 PMCID: PMC7953432 DOI: 10.1016/j.neuroimage.2020.117484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 11/24/2022] Open
Abstract
We recently presented evidence indicating limited efficacy of custom-molded headcases in reducing head motion in two naturalistic experimental contexts - passive movie watching, and speaking in the scanner (Jolly et al., 2020). In a commentary on this work, Lynch et al (2020) present additional data that support the original findings of (Power et al., 2019) and raise several potential issues with our recent work. We appreciate the opportunity to address these criticisms and raise additional points that should be considered when interpreting these conflicting findings. We do not believe that their criticisms diminish the value of our work, but instead, along with this reply, help better elucidate the key factors researchers should consider to make the most informed choice about their own research protocols.
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Affiliation(s)
- E Jolly
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, United States.
| | - S Sadhukha
- Department of Psychology, New York University, United States
| | - L J Chang
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, United States
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15
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Vandewouw MM, Dunkley BT, Lerch JP, Anagnostou E, Taylor MJ. Characterizing Inscapes and resting-state in MEG: Effects in typical and atypical development. Neuroimage 2020; 225:117524. [PMID: 33147510 DOI: 10.1016/j.neuroimage.2020.117524] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Examining the brain at rest is a powerful approach used to understand the intrinsic properties of typical and disordered human brain function, yet task-free paradigms are associated with greater head motion, particularly in young and/or clinical populations such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Inscapes, a non-social and non-verbal movie paradigm, has been introduced to increase attention, thus mitigating head motion, while reducing the task-induced activations found during typical movie watching. Inscapes has not yet been validated for use in magnetoencephalography (MEG), and it has yet to be shown whether its effects are stable in clinical populations. Across typically developing (N = 32) children and adolescents and those with ASD (N = 46) and ADHD (N = 42), we demonstrate that head motion is reduced during Inscapes. Due to the task state evoked by movie paradigms, we also expectedly observed concomitant modulations in local neural activity (oscillatory power) and functional connectivity (phase and envelope coupling) in intrinsic resting-state networks and across the frequency spectra compared to a fixation cross resting-state. Increases in local activity were accompanied by decreases in low-frequency connectivity within and between resting-state networks, primarily the visual network, suggesting that task-state evoked by Inscapes moderates ongoing and spontaneous cortical inhibition that forms the idling intrinsic networks found during a fixation cross resting-state. Importantly, these effects were similar in ASD and ADHD, making Inscapes a well-suited advancement for investigations of resting brain function in young and clinical populations.
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Affiliation(s)
- Marlee M Vandewouw
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
| | - Benjamin T Dunkley
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Evdokia Anagnostou
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
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16
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Aliko S, Huang J, Gheorghiu F, Meliss S, Skipper JI. A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Sci Data 2020; 7:347. [PMID: 33051448 PMCID: PMC7555491 DOI: 10.1038/s41597-020-00680-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/16/2020] [Indexed: 12/22/2022] Open
Abstract
Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of 'resting-state' data for which the functions of networks cannot be verifiably labelled. We make a 'Naturalistic Neuroimaging Database' (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.
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Affiliation(s)
- Sarah Aliko
- London Interdisciplinary Biosciences Consortium, University College London, London, UK.
- Experimental Psychology, University College London, London, UK.
| | - Jiawen Huang
- Experimental Psychology, University College London, London, UK
| | | | - Stefanie Meliss
- Experimental Psychology, University College London, London, UK
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
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17
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Snytte J, Elshiekh A, Subramaniapillai S, Manning L, Pasvanis S, Devenyi GA, Olsen RK, Rajah MN. The ratio of posterior–anterior medial temporal lobe volumes predicts source memory performance in healthy young adults. Hippocampus 2020; 30:1209-1227. [DOI: 10.1002/hipo.23251] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 07/02/2020] [Accepted: 07/10/2020] [Indexed: 02/01/2023]
Affiliation(s)
- Jamie Snytte
- Integrated Program in Neuroscience, Faculty of Medicine McGill University Montreal Quebec Canada
| | - Abdelhalim Elshiekh
- Integrated Program in Neuroscience, Faculty of Medicine McGill University Montreal Quebec Canada
| | | | - Lyssa Manning
- Massachusetts General Hospital Boston Massachusetts USA
| | - Stamatoula Pasvanis
- Cerebral Imaging Centre Douglas Mental Health University Institute Montreal Quebec Canada
| | - Gabriel A. Devenyi
- Cerebral Imaging Centre Douglas Mental Health University Institute Montreal Quebec Canada
- Department of Psychiatry McGill University Montreal Quebec Canada
| | - Rosanna K. Olsen
- Department of Psychology University of Toronto Toronto Ontario Canada
- Rotman Research Institute Baycrest Health Sciences Toronto Ontario Canada
| | - Maria Natasha Rajah
- Cerebral Imaging Centre Douglas Mental Health University Institute Montreal Quebec Canada
- Department of Psychiatry McGill University Montreal Quebec Canada
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18
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Finn ES, Glerean E, Khojandi AY, Nielson D, Molfese PJ, Handwerker DA, Bandettini PA. Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging. Neuroimage 2020; 215:116828. [PMID: 32276065 PMCID: PMC7298885 DOI: 10.1016/j.neuroimage.2020.116828] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 04/02/2020] [Accepted: 04/02/2020] [Indexed: 01/07/2023] Open
Abstract
Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or "idiosynchrony". Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.
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Affiliation(s)
- Emily S Finn
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Arman Y Khojandi
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Dylan Nielson
- Mood Brain & Development Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Peter J Molfese
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
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19
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Power JD, Lynch CJ, Adeyemo B, Petersen SE. A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies. Cereb Cortex 2020; 30:5544-5559. [PMID: 32494823 DOI: 10.1093/cercor/bhaa139] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 12/12/2022] Open
Abstract
This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like "Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties". Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.
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Affiliation(s)
- Jonathan D Power
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Charles J Lynch
- Brain and Mind Research Institute, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Babatunde Adeyemo
- Departments of Neurology and Psychology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Steven E Petersen
- Departments of Neurology and Psychology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
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20
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Meissner TW, Walbrin J, Nordt M, Koldewyn K, Weigelt S. Head motion during fMRI tasks is reduced in children and adults if participants take breaks. Dev Cogn Neurosci 2020; 44:100803. [PMID: 32716852 PMCID: PMC7284013 DOI: 10.1016/j.dcn.2020.100803] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 04/20/2020] [Accepted: 05/27/2020] [Indexed: 12/30/2022] Open
Abstract
In children, fMRI data acquisition split into multiple sessions reduces head motion. In adults, fMRI data acquisition split by inside-scanner breaks reduces head motion. In both children and adults, motion increases over the duration of a study. In both children and adults, motion increases over the duration of a run.
Head motion remains a challenging confound in functional magnetic resonance imaging (fMRI) studies of both children and adults. Most pediatric neuroimaging labs have developed experience-based, child-friendly standards concerning e.g. the maximum length of a session or the time between mock scanner training and actual scanning. However, it is unclear which factors of child-friendly neuroimaging approaches are effective in reducing head motion. Here, we investigate three main factors including (i) time lag of mock scanner training to the actual scan, (ii) prior scan time, and (iii) task engagement in a dataset of 77 children (aged 6–13) and 64 adults (aged 18–35) using a multilevel modeling approach. In children, distributing fMRI data acquisition across multiple same-day sessions reduces head motion. In adults, motion is reduced after inside-scanner breaks. Despite these positive effects of splitting up data acquisition, motion increases over the course of a study as well as over the course of a run in both children and adults. Our results suggest that splitting up fMRI data acquisition is an effective tool to reduce head motion in general. At the same time, different ways of splitting up data acquisition benefit children and adults.
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Affiliation(s)
- Tobias W Meissner
- TU Dortmund University, Faculty of Rehabilitation Sciences, Department of Vision, Visual Impairments & Blindness, Emil-Figge-Str. 50, 44227, Dortmund, Germany; Ruhr University Bochum, Faculty of Psychology, Universitätsstr. 150, 44801, Bochum, Germany.
| | - Jon Walbrin
- Bangor University, School of Psychology, Developmental Social Vision Lab, Penrallt Road, Bangor, LL57 2AS, Wales, United Kingdom; University of Coimbra, Faculty of Psychology and Education Sciences, Proaction Lab, Rua Colégio Novo, 3000-115, Coimbra, Portugal.
| | - Marisa Nordt
- Ruhr University Bochum, Faculty of Psychology, Universitätsstr. 150, 44801, Bochum, Germany; Stanford University, Psychology Department, 450 Serra Mall, Stanford, CA, 94305, USA.
| | - Kami Koldewyn
- Bangor University, School of Psychology, Developmental Social Vision Lab, Penrallt Road, Bangor, LL57 2AS, Wales, United Kingdom.
| | - Sarah Weigelt
- TU Dortmund University, Faculty of Rehabilitation Sciences, Department of Vision, Visual Impairments & Blindness, Emil-Figge-Str. 50, 44227, Dortmund, Germany.
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21
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Charbonnier L, Raemaekers MAH, Cornelisse PA, Verwoert M, Braun KPJ, Ramsey NF, Vansteensel MJ. A Functional Magnetic Resonance Imaging Approach for Language Laterality Assessment in Young Children. Front Pediatr 2020; 8:587593. [PMID: 33313027 PMCID: PMC7707083 DOI: 10.3389/fped.2020.587593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 09/03/2020] [Indexed: 11/23/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is a usable technique to determine hemispheric dominance of language function, but high-quality fMRI images are difficult to acquire in young children. Here we aimed to develop and validate an fMRI approach to reliably determine hemispheric language dominance in young children. We designed two new tasks (story, SR; Letter picture matching, LPM) that aimed to match the interests and the levels of cognitive development of young children. We studied 32 healthy children (6-10 years old, median age 8.7 years) and seven children with epilepsy (7-11 years old, median age 8.6 years) and compared the lateralization index of the new tasks with those of a well-validated task (verb generation, VG) and with clinical measures of hemispheric language dominance. A conclusive assessment of hemispheric dominance (lateralization index ≤-0.2 or ≥0.2) was obtained for 94% of the healthy participants who performed both new tasks. At least one new task provided conclusive language laterality assessment in six out of seven participants with epilepsy. The new tasks may contribute to assessing language laterality in young and preliterate children and may benefit children who are scheduled for surgical treatment of disorders such as epilepsy.
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Affiliation(s)
- Lisette Charbonnier
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mathijs A H Raemaekers
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Philippe A Cornelisse
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Maxime Verwoert
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Kees P J Braun
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
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22
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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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23
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Mori A, Klöbl M, Okada G, Reed MB, Takamura M, Michenthaler P, Takagaki K, Handschuh PA, Yokoyama S, Murgas M, Ichikawa N, Gryglewski G, Shibasaki C, Spies M, Yoshino A, Hahn A, Okamoto Y, Lanzenberger R, Yamawaki S, Kasper S. Predicting Ventral Striatal Activation During Reward Anticipation From Functional Connectivity at Rest. Front Hum Neurosci 2019; 13:289. [PMID: 31507394 PMCID: PMC6718467 DOI: 10.3389/fnhum.2019.00289] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 08/12/2019] [Indexed: 11/30/2022] Open
Abstract
Reward anticipation is essential for directing behavior toward positively valenced stimuli, creating motivational salience. Task-related activation of the ventral striatum (VS) has long been used as a target for understanding reward function. However, some subjects may not be able to perform the respective tasks because of their complexity or subjects' physical or mental disabilities. Moreover, task implementations may differ, which results in limited comparability. Hence, developing a task-free method for evaluating neural gain circuits is essential. Research has shown that fluctuations in neuronal activity at rest denoted individual differences in the brain functional networks. Here, we proposed novel models to predict the activation of the VS during gain anticipation, using the functional magnetic resonance imaging data of 45 healthy subjects acquired during a monetary incentive delay task and under rest. In-sample validation and held-out data were used to estimate the generalizability of the models. It was possible to predict three measures of reward activation (sensitivity, average, maximum) from resting-state functional connectivity (Pearson's r = 0.38-0.54 in validation data). Especially high contributions to the models were observed from the default mode network. These findings highlight the potential of using functional connectivity at rest as a task-free alternative for predicting activation in the VS, offering a possibility to estimate reward response in the broader sampling of subject populations.
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Affiliation(s)
- Asako Mori
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Koki Takagaki
- Health Service Center, Hiroshima University, Hiroshima, Japan
| | | | - Satoshi Yokoyama
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Matej Murgas
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Chiyo Shibasaki
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Marie Spies
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Atsuo Yoshino
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Shigeto Yamawaki
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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24
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Maknojia S, Churchill NW, Schweizer TA, Graham SJ. Resting State fMRI: Going Through the Motions. Front Neurosci 2019; 13:825. [PMID: 31456656 PMCID: PMC6700228 DOI: 10.3389/fnins.2019.00825] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 07/23/2019] [Indexed: 11/19/2022] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.
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Affiliation(s)
- Sanam Maknojia
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Nathan W Churchill
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Tom A Schweizer
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.,Division of Neurosurgery, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - S J Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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25
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Achterberg M, van der Meulen M. Genetic and environmental influences on MRI scan quantity and quality. Dev Cogn Neurosci 2019; 38:100667. [PMID: 31170550 PMCID: PMC6969338 DOI: 10.1016/j.dcn.2019.100667] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 05/14/2019] [Accepted: 05/20/2019] [Indexed: 01/24/2023] Open
Abstract
The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample (N = 512, aged 7–9), and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high (88% of participants completed all runs), while scan quality decreased with increasing session length. Scanner related distress was negatively associated with scan quantity (i.e., completed runs), but not with scan quality (i.e., included runs). In line with previous studies, behavioral genetic analyses showed that genetics explained part of the variation in head motion, with heritability estimates of 29% for framewise displacement and 65% for absolute displacement. Additionally, our results revealed that subtle head motion (after exclusion of excessive head motion) showed lower heritability estimates (0–14%), indicating that findings of motion-corrected and quality-controlled MRI data may be less confounded by genetic factors. These findings provide insights in factors contributing to scan quality in children, an issue that is highly relevant for the field of developmental neuroscience.
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Affiliation(s)
- Michelle Achterberg
- Leiden Consortium on Individual Development, Leiden University, the Netherlands; Institute of Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, the Netherlands.
| | - Mara van der Meulen
- Leiden Consortium on Individual Development, Leiden University, the Netherlands; Institute of Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, the Netherlands
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26
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Large-scale intrinsic connectivity is consistent across varying task demands. PLoS One 2019; 14:e0213861. [PMID: 30970031 PMCID: PMC6457563 DOI: 10.1371/journal.pone.0213861] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 03/02/2019] [Indexed: 01/02/2023] Open
Abstract
Measuring whole-brain functional connectivity patterns based on task-free (‘resting-state’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisition is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns (commonly termed ‘resting state networks’). We employed a ‘steady-states’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing us to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis) using standard resting-state functional connectivity (FC) analysis, we show that the whole-brain network architecture characteristic of the resting-state is comparable across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Changes in functional connectivity were detected locally, within the active networks. But to identify these local changes, the contributions of different FC networks to the global intrinsic connectivity pattern had to be isolated. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.
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27
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de Vries CF, Staff RT, Waiter GD, Sokunbi MO, Sandu AL, Murray AD. Motion During Acquisition is Associated With fMRI Brain Entropy. IEEE J Biomed Health Inform 2019; 24:586-593. [PMID: 30946681 DOI: 10.1109/jbhi.2019.2907189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Measures of fMRI brain entropy have been used to investigate age and disease related neural changes. However, it is unclear if movement in the scanner is associated with brain entropy after geometric correction for movement. As age and disease can affect motor control, quantifying and correcting for the influence of movement will avoid false findings. This paper examines the influence of head motion on fMRI brain entropy. Resting-state and task-based fMRI data from 281 individuals born in Aberdeen between 1950 and 1956 were analyzed. The images were realigned, followed by nuisance regression of the head motion parameters. The images were either high-pass filtered (0.008 Hz) or band-pass (0.008-0.1 Hz) filtered in order to compare the two methods; fuzzy approximate entropy and fuzzy sample entropy were calculated for every voxel. Motion was quantified as the mean displacement and mean rotation in three dimensions. Greater mean motion was correlated with decreased entropy for all four methods of calculating entropy. Different movement characteristics produce different patterns of associations, which appear to be artefact. However, across all motion metrics, entropy calculation methods, and scan conditions, a number of regions consistently show a significant negative association: the right cerebellar crus, left precentral gyrus (primary motor cortex), the left postcentral gyrus (primary somatosensory cortex), and the opercular part of the left inferior frontal gyrus. The robustness of our findings at these locations suggests that decreased entropy in specific brain regions may be a marker for decreased motor control.
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28
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Reiter MA, Mash LE, Linke AC, Fong CH, Fishman I, Müller RA. Distinct Patterns of Atypical Functional Connectivity in Lower-Functioning Autism. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:251-259. [PMID: 30343132 PMCID: PMC7202917 DOI: 10.1016/j.bpsc.2018.08.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/03/2018] [Accepted: 08/10/2018] [Indexed: 12/13/2022]
Abstract
BACKGROUND Functional magnetic resonance imaging research on autism spectrum disorders (ASDs) has been largely limited to individuals with near-average intelligence. Although cognitive impairment is common in ASDs, functional network connectivity in this population remains poorly understood. Specifically, it remains unknown whether lower-functioning individuals exhibit exacerbated connectivity abnormalities similar to those previously detected in higher-functioning samples or specific divergent patterns of connectivity. METHODS Resting-state functional magnetic resonance imaging data from 88 children (44 ASD, 44 typically developing; average age: 11 years) were included. Based on IQ, individuals with ASDs were assigned to either a lower-functioning group (mean IQ = 77 ± 6) or a higher-functioning group (mean IQ = 123 ± 8). Two typically developing comparison groups were matched to these groups on head motion, handedness, and age. Seeds in the medial prefrontal cortex, posterior cingulate cortex, posterior superior temporal sulcus, insula, and amygdala were used to contrast whole-brain functional connectivity across groups. RESULTS Lower-functioning ASD participants (compared with higher-functioning ASD participants) showed significant underconnectivity within the default mode network and the ventral visual stream. Higher-functioning ASD participants (compared with matched typically developing participants) showed significantly decreased anticorrelations among default mode, salience, and task-positive regions. Effect sizes of detected differences were large (Cohen's d > 1.46). CONCLUSIONS Lower- and higher-functioning individuals with ASDs demonstrated distinct patterns of atypical connectivity. Findings suggest a gross pattern of predominantly reduced network integration in lower-functioning ASDs (affecting default mode and visual networks) and predominantly reduced network segregation in higher-functioning ASDs. Results indicate the need for stratification by general functional level in studies of functional connectivity in ASDs.
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Affiliation(s)
- Maya A Reiter
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Lisa E Mash
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Christopher H Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Inna Fishman
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
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29
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Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI. Sci Rep 2019; 9:419. [PMID: 30674933 PMCID: PMC6344520 DOI: 10.1038/s41598-018-36699-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/22/2018] [Indexed: 01/02/2023] Open
Abstract
Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. The aim of this investigation was to determine whether psychiatric symptoms and other characteristics of the individual predict HM during rsfMRI. A sample of n = 464 participants (174 male) from the Tulsa1000, a naturalistic longitudinal study recruiting subjects with different levels of severity in mood/anxiety/substance use disorders based on the dimensional NIMH Research Domain Criteria framework was used for this study. Based on a machine learning (ML) pipeline with nested cross-validation to avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demographic (age and sex) variables identifies BMI and weight as the most important variables and explained 10.9 percent of the HM variance (95% CI: 9.9–11.8). In comparison ML models with 105 self-report measures for state and trait psychological characteristics identified nicotine and alcohol use variables as well as impulsivity inhibitory control variables but explain only 5 percent of HM variance (95% CI: 3.5–6.4). A combined ML model using all 120 variables did not perform significantly better than the model using only 15 physical variables (combined model 95% confidence interval: 10.2–12.4). Taken together, after considering physical variables, state or trait psychological characteristics do not provide additional power to predict motion during rsfMRI.
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30
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Drew PJ, Winder AT, Zhang Q. Twitches, Blinks, and Fidgets: Important Generators of Ongoing Neural Activity. Neuroscientist 2018; 25:298-313. [PMID: 30311838 DOI: 10.1177/1073858418805427] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Animals and humans continuously engage in small, spontaneous motor actions, such as blinking, whisking, and postural adjustments ("fidgeting"). These movements are accompanied by changes in neural activity in sensory and motor regions of the brain. The frequency of these motions varies in time, is affected by sensory stimuli, arousal levels, and pathology. These fidgeting behaviors can be entrained by sensory stimuli. Fidgeting behaviors will cause distributed, bilateral functional activation in the 0.01 to 0.1 Hz frequency range that will show up in functional magnetic resonance imaging and wide-field calcium neuroimaging studies, and will contribute to the observed functional connectivity among brain regions. However, despite the large potential of these behaviors to drive brain-wide activity, these fidget-like behaviors are rarely monitored. We argue that studies of spontaneous and evoked brain dynamics in awake animals and humans should closely monitor these fidgeting behaviors. Differences in these fidgeting behaviors due to arousal or pathology will "contaminate" ongoing neural activity, and lead to apparent differences in functional connectivity. Monitoring and accounting for the brain-wide activations by these behaviors is essential during experiments to differentiate fidget-driven activity from internally driven neural dynamics.
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Affiliation(s)
- Patrick J Drew
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.,Department of Neurosurgery and Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Aaron T Winder
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Qingguang Zhang
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
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31
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Functional magnetic resonance imaging: Basic principles and application in the neurosciences. RADIOLOGIA 2018. [DOI: 10.1016/j.rxeng.2018.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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32
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Madan CR. Age differences in head motion and estimates of cortical morphology. PeerJ 2018; 6:e5176. [PMID: 30065858 PMCID: PMC6065477 DOI: 10.7717/peerj.5176] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 06/16/2018] [Indexed: 01/20/2023] Open
Abstract
Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.
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33
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Tommasin S, Mascali D, Moraschi M, Gili T, Hassan IE, Fratini M, DiNuzzo M, Wise RG, Mangia S, Macaluso E, Giove F. Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation. Neuroimage 2018; 179:570-581. [PMID: 29908935 DOI: 10.1016/j.neuroimage.2018.06.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 06/04/2018] [Indexed: 01/09/2023] Open
Abstract
Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation.
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Affiliation(s)
- Silvia Tommasin
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Dipartimento di Neuroscienze umane, Sapienza Università di Roma, Roma, Italy
| | - Daniele Mascali
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy
| | - Marta Moraschi
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy
| | - Tommaso Gili
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Fondazione Santa Lucia IRCCS, Roma, Italy
| | | | - Michela Fratini
- Fondazione Santa Lucia IRCCS, Roma, Italy; Istituto di Nanotecnologia, Consiglio Nazionale delle Ricerche, Roma, Italy
| | - Mauro DiNuzzo
- Center for Basic and Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Richard G Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Silvia Mangia
- Center for Magnetic Resonance Research, Dept. of Radiology, University of Minnesota, Minneapolis, USA
| | | | - Federico Giove
- Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Fondazione Santa Lucia IRCCS, Roma, Italy.
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34
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Labbé Atenas T, Ciampi Díaz E, Cruz Quiroga JP, Uribe Arancibia S, Cárcamo Rodríguez C. Functional magnetic resonance imaging: basic principles and application in the neurosciences. RADIOLOGIA 2018; 60:368-377. [PMID: 29544987 DOI: 10.1016/j.rx.2017.12.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 12/25/2017] [Accepted: 12/26/2017] [Indexed: 11/25/2022]
Abstract
Functional magnetic resonance imaging (fMRI) is an advanced tool for the study of brain functions in healthy subjects and in neuropsychiatric patients. This tool makes it possible to identify and locate specific phenomena related to neuronal metabolism and activity. Starting with the detection of changes in the blood supply to a region that participates in a function, more complex approaches have been developed to study the dynamics of neuronal networks. Studies examining the brain at rest or involved in different tasks have provided evidence related to the onset, development, and/or response to treatment in various diseases. The diversity of the possible artifacts associated with image registration as well as the complexity of the analytical experimental designs has generated abundant debate about the technique behind fMRI. This article aims to introduce readers to the fundamentals underlying fMRI, to explain how fMRI studies are interpreted, and to discuss fMRI's contributions to the study of the mechanisms underlying diverse diseases of the nervous system.
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Affiliation(s)
- T Labbé Atenas
- Centro Interdisciplinario de Neurociencias, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - E Ciampi Díaz
- Departamento de Neurología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - J P Cruz Quiroga
- Departamento de Radiología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - S Uribe Arancibia
- Departamento de Radiología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Centro de Imágenes Biomédicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - C Cárcamo Rodríguez
- Centro Interdisciplinario de Neurociencias, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Departamento de Neurología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Ide JS, Li CSR. Time scale properties of task and resting-state functional connectivity: Detrended partial cross-correlation analysis. Neuroimage 2018; 173:240-248. [PMID: 29454934 DOI: 10.1016/j.neuroimage.2018.02.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 01/09/2018] [Accepted: 02/14/2018] [Indexed: 12/19/2022] Open
Abstract
Functional connectivity analysis is an essential tool for understanding brain function. Previous studies showed that brain regions are functionally connected through low-frequency signals both within the default mode network (DMN) and task networks. However, no studies have directly compared the time scale (frequency) properties of network connectivity during task versus rest, or examined how they relate to task performance. Here, using fMRI data collected from sixty-eight subjects at rest and during a stop signal task, we addressed this issue with a novel functional connectivity measure based on detrended partial cross-correlation analysis (DPCCA). DPCCA has the advantage of quantifying correlations between two variables in different time scales while controlling for the influence of other variables. The results showed that the time scales of within-network connectivity of the DMN and task networks are modulated in opposite directions across rest and task, with the time scale increased during rest vs. task in the DMN and vice versa in task networks. In regions of interest analysis, the within-network connectivity time scale of the pre-supplementary motor area - a medial prefrontal cortical structure of the task network and critical to proactive inhibitory control - correlated inversely with Barratt impulsivity and stop signal reaction time. Together, these findings demonstrate that time scale properties of brain networks may vary across mental states and provide evidence in support of a role of low frequency fluctuations of BOLD signals in behavioral control.
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Affiliation(s)
- Jaime S Ide
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06519, USA.
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06519, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, 06520, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA
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Silva MA, See AP, Essayed WI, Golby AJ, Tie Y. Challenges and techniques for presurgical brain mapping with functional MRI. NEUROIMAGE-CLINICAL 2017; 17:794-803. [PMID: 29270359 PMCID: PMC5735325 DOI: 10.1016/j.nicl.2017.12.008] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 11/10/2017] [Accepted: 12/05/2017] [Indexed: 01/22/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients. fMRI is increasingly used for presurgical brain mapping for surgical planning. Understanding of the limitations of fMRI is critical for its clinical use. Clinical fMRI's challenges and potential solutions are discussed. Intraoperative use and validation of fMRI are discussed.
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Affiliation(s)
- Michael A Silva
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Alfred P See
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Walid I Essayed
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra J Golby
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yanmei Tie
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA.
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