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Lee J, Lee J. Discovering individual fingerprints in resting-state functional connectivity using deep neural networks. Hum Brain Mapp 2024; 45:e26561. [PMID: 38096866 PMCID: PMC10789221 DOI: 10.1002/hbm.26561] [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: 06/06/2023] [Revised: 11/13/2023] [Accepted: 11/28/2023] [Indexed: 01/16/2024] Open
Abstract
Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.
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Affiliation(s)
- Juhyeon Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
- Interdisciplinary Program in Precision Public HealthKorea UniversitySeoulSouth Korea
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyBostonMassachusettsUSA
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2
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Cao H, Barber AD, Rubio JM, Argyelan M, Gallego JA, Lencz T, Malhotra AK. Effects of phase encoding direction on test-retest reliability of human functional connectome. Neuroimage 2023; 277:120238. [PMID: 37364743 PMCID: PMC10529794 DOI: 10.1016/j.neuroimage.2023.120238] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.
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Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.
| | - Anita D Barber
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Jose M Rubio
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Miklos Argyelan
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Juan A Gallego
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Todd Lencz
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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3
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Fernandez-Alvarez M, Atienza M, Cantero JL. Effects of non-modifiable risk factors of Alzheimer's disease on intracortical myelin content. Alzheimers Res Ther 2022; 14:202. [PMID: 36587227 PMCID: PMC9805254 DOI: 10.1186/s13195-022-01152-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/25/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND Non-modifiable risk factors of Alzheimer's disease (AD) have lifelong effects on cortical integrity that could be mitigated if identified at early stages. However, it remains unknown whether cortical microstructure is affected in older individuals with non-modifiable AD risk factors and whether altered cortical tissue integrity produces abnormalities in brain functional networks in this AD-risk population. METHODS Using relative T1w/T2w (rT1w/T2w) ratio maps, we have compared tissue integrity of normal-appearing cortical GM between controls and cognitively normal older adults with either APOE4 (N = 50), with a first-degree family history (FH) of AD (N = 52), or with the co-occurrence of both AD risk factors (APOE4+FH) (N = 35). Additionally, individuals with only one risk factor (APOE4 or FH) were combined into one group (N = 102) and compared with controls. The same number of controls matched in age, sex, and years of education was employed for each of these comparisons. Group differences in resting state functional connectivity (rs-FC) patterns were also investigated, using as FC seeds those cortical regions showing significant changes in rT1w/T2w ratios. RESULTS Overall, individuals with non-modifiable AD risk factors exhibited significant variations in rT1w/T2w ratios compared to controls, being APOE4 and APOE4+FH at opposite ends of a continuum. The co-occurrence of APOE4 and FH was further accompanied by altered patterns of rs-FC. CONCLUSIONS These findings may have practical implications for early detection of cortical abnormalities in older populations with APOE4 and/or FH of AD and open new avenues to monitor changes in cortical tissue integrity associated with non-modifiable AD risk factors.
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Affiliation(s)
- Marina Fernandez-Alvarez
- grid.15449.3d0000 0001 2200 2355Laboratory of Functional Neuroscience, Pablo de Olavide University, Ctra. de Utrera Km 1, 41013 Seville, Spain ,grid.418264.d0000 0004 1762 4012CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Mercedes Atienza
- grid.15449.3d0000 0001 2200 2355Laboratory of Functional Neuroscience, Pablo de Olavide University, Ctra. de Utrera Km 1, 41013 Seville, Spain ,grid.418264.d0000 0004 1762 4012CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
| | - Jose L. Cantero
- grid.15449.3d0000 0001 2200 2355Laboratory of Functional Neuroscience, Pablo de Olavide University, Ctra. de Utrera Km 1, 41013 Seville, Spain ,grid.418264.d0000 0004 1762 4012CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Madrid, Spain
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Chen AA, Srinivasan D, Pomponio R, Fan Y, Nasrallah IM, Resnick SM, Beason-Held LL, Davatzikos C, Satterthwaite TD, Bassett DS, Shinohara RT, Shou H. Harmonizing functional connectivity reduces scanner effects in community detection. Neuroimage 2022; 256:119198. [PMID: 35421567 PMCID: PMC9202339 DOI: 10.1016/j.neuroimage.2022.119198] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/12/2022] Open
Abstract
Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.
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Affiliation(s)
- Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raymond Pomponio
- Department of Biostatistics, Colorado School of Public Health, Aurora, CO 80045, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Nuerology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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Weber S, Heim S, Richiardi J, Van De Ville D, Serranová T, Jech R, Marapin RS, Tijssen MAJ, Aybek S. Multi-centre classification of functional neurological disorders based on resting-state functional connectivity. Neuroimage Clin 2022; 35:103090. [PMID: 35752061 PMCID: PMC9240866 DOI: 10.1016/j.nicl.2022.103090] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/28/2022] [Accepted: 06/16/2022] [Indexed: 11/28/2022]
Abstract
Using machine learning on multi-centre data, FND patients were successfully classified with an accuracy of 72%. The angular- and supramarginal gyri, cingular- and insular cortex, and the hippocampus were the most discriminant regions. To provide diagnostic utility, future studies must include patients with similar symptoms but different diagnoses.
Background Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a “rule-in” procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting. Methods This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation). Results FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%). Conclusions The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.
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Affiliation(s)
- Samantha Weber
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Salome Heim
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, Geneva University Hospitals, Geneva, Switzerland
| | - Tereza Serranová
- Centre for Interventional Therapy of Movement Disorders, Department of Neurology, Charles University, 1(st) Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Robert Jech
- Centre for Interventional Therapy of Movement Disorders, Department of Neurology, Charles University, 1(st) Faculty of Medicine and General University Hospital in Prague, Czech Republic; Department of Neurology, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Ramesh S Marapin
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; UMCG Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Marina A J Tijssen
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; UMCG Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Selma Aybek
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
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Wang Y, Chen X, Liu R, Zhang Z, Zhou J, Feng Y, Jiang C, Zuo XN, Zhou Y, Wang G. Effect of Phase-Encoding Direction on Gender Differences: A Resting-State Functional Magnetic Resonance Imaging Study. Front Neurosci 2022; 15:748080. [PMID: 35145372 PMCID: PMC8824585 DOI: 10.3389/fnins.2021.748080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
AimNeuroimaging studies have highlighted gender differences in brain functions, but conclusions are not well established. Few studies paid attention to the influence of phase-encoding (PE) direction in echo-planar imaging on gender differences, which is a commonly used technique in functional magnetic resonance imaging (fMRI). A disadvantage of echo-planar images is the geometrical distortion and signal loss due to large susceptibility effects along the PE direction. The present research aimed to clarify how PE direction can affect the outcome of a specific research on gender differences.MethodsWe collected resting-state fMRI using anterior to posterior (AP) and posterior to anterior (PA) directions from 113 healthy participants. We calculated several commonly used indices for spontaneous brain activity including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC) of posterior cingulate cortex for each session, and performed three group comparisons: (i) AP versus PA; (ii) male versus female; (iii) interaction between gender and PE direction.ResultsThe estimated indices differed substantially between the two PE directions, and the regions that exhibited differences were roughly similar for all the indices. In addition, we found that multiple brain regions showed gender differences in these estimated indices. Further, we observed an interaction effect between gender and PE direction in the bilateral middle frontal gyrus, right precentral gyrus, right postcentral gyrus, right lingual gyrus, and bilateral cerebellum posterior lobe.ConclusionThese apparent findings revealed that PE direction can partially influence gender differences in spontaneous brain activity of resting-state fMRI. Therefore, future studies should document the adopted PE direction and appropriate selection of PE direction will be important in future resting-state fMRI studies.
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Affiliation(s)
- Yun Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
| | - Xiongying Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
| | - Rui Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
| | - Zhifang Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
| | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
| | - Yuan Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chao Jiang
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Yuan Zhou,
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- *Correspondence: Gang Wang,
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McNabb CB, Lindner M, Shen S, Burgess LG, Murayama K, Johnstone T. Inter-slice leakage and intra-slice aliasing in simultaneous multi-slice echo-planar images. Brain Struct Funct 2020; 225:1153-1158. [PMID: 32140847 PMCID: PMC7166208 DOI: 10.1007/s00429-020-02053-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 02/21/2020] [Indexed: 11/26/2022]
Abstract
Simultaneous multi-slice (SMS) imaging is a popular technique for increasing acquisition speed in echo-planar imaging (EPI) fMRI. However, SMS data are prone to motion sensitivity and slice leakage artefacts, which spread signal between simultaneously acquired slices. Relevant to motion sensitivity, artefacts from moving anatomic structures propagate along the phase-encoding (PE) direction. This is particularly relevant for eye movement. As signal from the eye is acquired along with signal from simultaneously excited slices during SMS, there is potential for signal to spread in-plane and between spatially remote slices. After identifying an artefact temporally coinciding with signal fluctuations in the eye and spatially distributed in correspondence with multiband slice acceleration and parallel imaging factors, we conducted a series of small experiments to investigate eye movement artefacts in SMS data and the contribution of PE direction to the invasiveness of these artefacts. Five healthy adult volunteers were scanned during a blinking task using a standard SMS-EPI protocol with posterior-to-anterior (P ≫ A), anterior-to-posterior (A ≫ P) or right-to-left (R ≫ L) PE direction. The intensity of signal fluctuations (artefact severity) was measured at expected artefact positions and control positions. We demonstrated a direct relationship between eye movements and artefact severity across expected artefact regions. Within-brain artefacts were apparent in P ≫ A- and A ≫ P-acquired data but not in R ≫ L data due to the shift in artefact positions. Further research into eye motion artefacts in SMS data is warranted but researchers should exercise caution with SMS protocols. We recommend rigorous piloting of SMS protocols and switching to R ≫ L/L ≫ R PE where feasible.
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Affiliation(s)
- Carolyn Beth McNabb
- School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading, RG6 7BE, UK.
| | - Michael Lindner
- School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading, RG6 7BE, UK.,Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Earley Gate, Reading, RG6 7BE, UK
| | - Shan Shen
- Technical Support, University of Reading, Reading, UK
| | - Laura Grace Burgess
- School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading, RG6 7BE, UK
| | - Kou Murayama
- School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading, RG6 7BE, UK.,Research Institute, Kochi University of Technology, Kami, Kochi, Japan
| | - Tom Johnstone
- School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Reading, RG6 7BE, UK.,School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia
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