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Bao Q, Liu X, Xu J, Xia L, Otikovs M, Xie H, Liu K, Zhang Z, Zhou X, Liu C. Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding. Magn Reson Med 2024; 91:1368-1383. [PMID: 38073072 DOI: 10.1002/mrm.29925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To design an unsupervised deep learning (DL) model for correcting Nyquist ghosts of single-shot spatiotemporal encoding (SPEN) and evaluate the model for real MRI applications. METHODS The proposed method consists of three main components: (1) an unsupervised network that combines Residual Encoder and Restricted Subspace Mapping (RERSM-net) and is trained to generate a phase-difference map based on the even and odd SPEN images; (2) a spin physical forward model to obtain the corrected image with the learned phase difference map; and (3) cycle-consistency loss that is explored for training the RERSM-net. RESULTS The proposed RERSM-net could effectively generate smooth phase difference maps and correct Nyquist ghosts of single-shot SPEN. Both simulation and real in vivo MRI experiments demonstrated that our method outperforms the state-of-the-art SPEN Nyquist ghost correction method. Furthermore, the ablation experiments of generating phase-difference maps show the advantages of the proposed unsupervised model. CONCLUSION The proposed method can effectively correct Nyquist ghosts for the single-shot SPEN sequence.
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Affiliation(s)
- Qingjia Bao
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China
| | - Xinjie Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingyun Xu
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | - Liyang Xia
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | | | - Han Xie
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China
| | - Kewen Liu
- School of Information Engineering, Wuhan University of Technology, Wuhan, China
| | - Zhi Zhang
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China
| | - Xin Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
- Optics Valley Laboratory, Wuhan, China
| | - Chaoyang Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
- Optics Valley Laboratory, Wuhan, China
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2
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Schmidt T, Vannesjo SJ, Sommer S, Nagy Z. fMRI with whole-brain coverage, 75-ms temporal resolution and high SNR by combining HiHi reshuffling and multiband imaging. Magn Reson Imaging 2023; 103:48-53. [PMID: 37385353 DOI: 10.1016/j.mri.2023.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023]
Abstract
Increasing the temporal resolution of the blood‑oxygen level-dependent (BOLD) response is usually accompanied by a decrease in repetition time and therefore also a reduction of the magnetic resonance (MR) signal due to incomplete T1 relaxation and thus a loss of signal-to-noise ratio (SNR). A previous data reordering method can achieve higher temporal sampling rate without the loss of SNR but at the cost of increased scan time. In this proof-of-principle work, we show that combining HiHi reshuffling with multiband acceleration allows us to measure the in vivo BOLD response with a 75-ms sampling rate that is decoupled from the acquisition repetition time (here 1.5 s and hence higher SNR) while covering the entire forebrain with 60 2-mm slices in a ~ 35-min scan. We provide single-voxel time-courses of the BOLD responses in the primary visual and primary motor cortices in three fMRI experiments on a 7 T scanner - 1 male (scanned twice on different days for test-retest reproducibility) and 1 female participant.
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Affiliation(s)
- Tim Schmidt
- Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stefan Sommer
- Siemens Healthineers International AG, Zurich, Switzerland; Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland; Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Lausanne, Switzerland
| | - Zoltán Nagy
- Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, Switzerland
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3
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Levitt J, Yang Z, Williams SD, Lütschg Espinosa SE, Garcia-Casal A, Lewis LD. EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI. Neuroimage 2023; 273:120092. [PMID: 37028736 PMCID: PMC10202030 DOI: 10.1016/j.neuroimage.2023.120092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/02/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.
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Affiliation(s)
- Joshua Levitt
- Department of Biomedical Engineering, Boston University, USA
| | - Zinong Yang
- Department of Biomedical Engineering, Boston University, USA; Graduate Program of Neuroscience, Boston University, USA
| | | | | | | | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, USA; Institute for Medical Engineering and Sciences, Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA.
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4
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Sun K, Zhong Z, Dan G, Wang K, Karaman MM, Luo Q, Zhou XJ. Simultaneous multi-segment (SMSeg) EPI over multiple focal regions. Phys Med Biol 2023; 68:10.1088/1361-6560/acb2a9. [PMID: 36634366 PMCID: PMC9994176 DOI: 10.1088/1361-6560/acb2a9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023]
Abstract
Objective.This study aimed at developing a simultaneous multi-segment (SMSeg) imaging technique using a two-dimensional (2D) RF pulse in conjunction with echo planar imaging (EPI) to image multiple focal regions.Approach.The SMSeg technique leveraged periodic replicates of the excitation profile of a 2D RF pulse to simultaneously excite multiple focal regions at different locations. These locations were controlled by rotating and scaling transmit k-space trajectories. The resulting multiple isolated focal regions were projected into a composite 'slice' for display. GRAPPA-based parallel imaging was incorporated into SMSeg by taking advantage of coil sensitivity variations in both the phase-encoded and slice-selection directions. The SMSeg technique was implemented at 3 T in a single-shot gradient-echo EPI sequence and demonstrated in a phantom and human brains for both anatomic imaging and functional imaging.Main results.In both the phantom and the human brain, SMSeg images from three focal regions were simultaneously acquired. SMSeg imaging enabled up to a six-fold acceleration in parallel imaging without causing appreciable residual aliasing artifacts when compared with a conventional gradient-echo EPI sequence with the same acceleration factor. In the functional imaging experiment, BOLD activations associated with a visuomotor task were simultaneously detected in two non-coplanar segments (each with a size of 240 × 30 mm2), corresponding to visual and motor cortices, respectively.Significance.Our study has demonstrated that SMSeg imaging can be a viable method for studying multiple focal regions simultaneously.
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Affiliation(s)
- Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Kezhou Wang
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.,VasSol, Inc., River Forest, IL, United States of America
| | - M Muge Karaman
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Qingfei Luo
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America.,Departments of Radiology and Neurosurgery, University of Illinois College of Medicine at Chicago, Chicago, IL, United States of America
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5
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Peng X, Liu Q, Hubbard CS, Wang D, Zhu W, Fox MD, Liu H. Robust dynamic brain coactivation states estimated in individuals. Sci Adv 2023; 9:eabq8566. [PMID: 36652524 PMCID: PMC9848428 DOI: 10.1126/sciadv.abq8566] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 12/14/2022] [Indexed: 06/01/2023]
Abstract
A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.
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Affiliation(s)
- Xiaolong Peng
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Liu
- Changping Laboratory, Beijing, China
| | - Catherine S. Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
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6
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Zhang X. Magnetic resonance imaging of the monkey fetal brain in utero. Investig Magn Reson Imaging 2022; 26:177-190. [PMID: 36937817 PMCID: PMC10019598 DOI: 10.13104/imri.2022.26.4.177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Non-human primates (NHPs) are the closest living relatives of the human and play a critical role in investigating the effects of maternal viral infection and consumption of medicines, drugs, and alcohol on fetal development. With the advance of contemporary fast MRI techniques with parallel imaging, fetal MRI is becoming a robust tool increasingly used in clinical practice and preclinical studies to examine congenital abnormalities including placental dysfunction, congenital heart disease (CHD), and brain abnormalities non-invasively. Because NHPs are usually scanned under anesthesia, the motion artifact is reduced substantially, allowing multi-parameter MRI techniques to be used intensively to examine the fetal development in a single scanning session or longitudinal studies. In this paper, the MRI techniques for scanning monkey fetal brains in utero in biomedical research are summarized. Also, a fast imaging protocol including T2-weighted imaging, diffusion MRI, resting-state functional MRI (rsfMRI) to examine rhesus monkey fetal brains in utero on a clinical 3T scanner is introduced.
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Affiliation(s)
- Xiaodong Zhang
- EPC Imaging Center and Division of Neuropharmacology and Neurologic Diseases, Emory National Primate Research Center, Emory University, Atlanta, Georgia, 30329, USA
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7
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Ebrahiminia F, Cichy RM, Khaligh-Razavi SM. A multivariate comparison of electroencephalogram and functional magnetic resonance imaging to electrocorticogram using visual object representations in humans. Front Neurosci 2022; 16:983602. [DOI: 10.3389/fnins.2022.983602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Today, most neurocognitive studies in humans employ the non-invasive neuroimaging techniques functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). However, how the data provided by fMRI and EEG relate exactly to the underlying neural activity remains incompletely understood. Here, we aimed to understand the relation between EEG and fMRI data at the level of neural population codes using multivariate pattern analysis. In particular, we assessed whether this relation is affected when we change stimuli or introduce identity-preserving variations to them. For this, we recorded EEG and fMRI data separately from 21 healthy participants while participants viewed everyday objects in different viewing conditions, and then related the data to electrocorticogram (ECoG) data recorded for the same stimulus set from epileptic patients. The comparison of EEG and ECoG data showed that object category signals emerge swiftly in the visual system and can be detected by both EEG and ECoG at similar temporal delays after stimulus onset. The correlation between EEG and ECoG was reduced when object representations tolerant to changes in scale and orientation were considered. The comparison of fMRI and ECoG overall revealed a tighter relationship in occipital than in temporal regions, related to differences in fMRI signal-to-noise ratio. Together, our results reveal a complex relationship between fMRI, EEG, and ECoG signals at the level of population codes that critically depends on the time point after stimulus onset, the region investigated, and the visual contents used.
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8
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Parsons N, Steward T, Clohesy R, Almgren H, Duehlmeyer L. A systematic review of resting-state functional connectivity in obesity: Refining current neurobiological frameworks and methodological considerations moving forward. Rev Endocr Metab Disord 2022; 23:861-879. [PMID: 34159504 DOI: 10.1007/s11154-021-09665-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 02/07/2023]
Abstract
Obesity is the second most common cause of preventable morbidity worldwide. Resting-state functional magnetic resonance imaging (fMRI) has been used extensively to characterise altered communication between brain regions in individuals with obesity, though findings from this research have not yet been systematically evaluated within the context of prominent neurobiological frameworks. This systematic review aggregated resting-state fMRI findings in individuals with obesity and evaluated the contribution of these findings to current neurobiological models. Findings were considered in relation to a triadic model of problematic eating, outlining disrupted communication between reward, inhibitory, and homeostatic systems. We identified a pattern of consistently increased orbitofrontal and decreased insula cortex resting-state functional connectivity in individuals with obesity in comparison to healthy weight controls. BOLD signal amplitude was also increased in people with obesity across studies, predominantly confined to subcortical regions, including the hippocampus, amygdala, and putamen. We posit that altered orbitofrontal cortex connectivity may be indicative of a shift in the valuation of food-based rewards and that dysfunctional insula connectivity likely contributes to altered homeostatic signal processing. Homeostatic violation signals in obesity may be maintained despite satiety, thereby 'hijacking' the executive system and promoting further food intake. Moving forward, we provide a roadmap for more reliable resting-state and task-based functional connectivity experiments, which must be reconciled within a common framework if we are to uncover the interplay between psychological and biological factors within current theoretical frameworks.
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Affiliation(s)
- Nicholas Parsons
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne Burwood Campus, VIC, Australia
| | - Trevor Steward
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Rebecca Clohesy
- School of Psychology, Deakin University, Melbourne Burwood Campus, VIC, Australia
| | - Hannes Almgren
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
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Tobe RH, MacKay-Brandt A, Lim R, Kramer M, Breland MM, Tu L, Tian Y, Trautman KD, Hu C, Sangoi R, Alexander L, Gabbay V, Castellanos FX, Leventhal BL, Craddock RC, Colcombe SJ, Franco AR, Milham MP. A longitudinal resource for studying connectome development and its psychiatric associations during childhood. Sci Data 2022; 9:300. [PMID: 35701428 DOI: 10.1038/s41597-022-01329-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 04/20/2022] [Indexed: 12/14/2022] Open
Abstract
Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.
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10
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Kaplan S, Meyer D, Miranda-Dominguez O, Perrone A, Earl E, Alexopoulos D, Barch DM, Day TK, Dust J, Eggebrecht AT, Feczko E, Kardan O, Kenley JK, Rogers CE, Wheelock MD, Yacoub E, Rosenberg M, Elison JT, Fair DA, Smyser CD. Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations. Neuroimage 2022; 247:118838. [PMID: 34942363 PMCID: PMC8803544 DOI: 10.1016/j.neuroimage.2021.118838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/30/2021] [Accepted: 12/18/2021] [Indexed: 11/24/2022] Open
Abstract
The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.
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Affiliation(s)
- Sydney Kaplan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Dominique Meyer
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Oscar Miranda-Dominguez
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anders Perrone
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Eric Earl
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA,Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M. Barch
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA,Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Trevor K.M. Day
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Joseph Dust
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adam T. Eggebrecht
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Omid Kardan
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Jeanette K. Kenley
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E. Rogers
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Muriah D. Wheelock
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Monica Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Jed T. Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA,Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA,Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Christopher D. Smyser
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
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11
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Suarez A, Valdés-Hernández PA, Bernal B, Dunoyer C, Khoo HM, Bosch-Bayard J, Riera JJ. Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models. Front Neurol 2021; 12:659081. [PMID: 34690906 PMCID: PMC8531269 DOI: 10.3389/fneur.2021.659081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 09/03/2021] [Indexed: 11/16/2022] Open
Abstract
Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.
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Affiliation(s)
- Alejandro Suarez
- Neuronal Mass Dynamics Laboratory, Florida International University, Miami, FL, United States
| | | | - Byron Bernal
- Nicklaus Children Hospital, Miami, FL, United States
| | | | - Hui Ming Khoo
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Neurosurgery, Osaka University, Suita, Japan
| | - Jorge Bosch-Bayard
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge J Riera
- Neuronal Mass Dynamics Laboratory, Florida International University, Miami, FL, United States
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12
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Raimondo L, Oliveira ĹAF, Heij J, Priovoulos N, Kundu P, Leoni RF, van der Zwaag W. Advances in resting state fMRI acquisitions for functional connectomics. Neuroimage 2021; 243:118503. [PMID: 34479041 DOI: 10.1016/j.neuroimage.2021.118503] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 01/21/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.
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Affiliation(s)
- Luisa Raimondo
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Ĺcaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Jurjen Heij
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | | | - Prantik Kundu
- Hyperfine Research Inc, Guilford, CT, United States; Icahn School of Medicine at Mt. Sinai, New York, United States
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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13
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Versteeg E, van der Velden TA, van Leeuwen CC, Borgo M, Huijing ER, Hendriks AD, Hendrikse J, Klomp DWJ, Siero JCW. A plug-and-play, lightweight, single-axis gradient insert design for increasing spatiotemporal resolution in echo planar imaging-based brain imaging. NMR Biomed 2021; 34:e4499. [PMID: 33619838 PMCID: PMC8244051 DOI: 10.1002/nbm.4499] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 05/25/2023]
Abstract
The goal of this study was to introduce and evaluate the performance of a lightweight, high-performance, single-axis (z-axis) gradient insert design primarily intended for high-resolution functional magnetic resonance imaging, and aimed at providing both ease of use and a boost in spatiotemporal resolution. The optimal winding positions of the coil were obtained using a genetic algorithm with a cost function that balanced gradient performance (minimum 0.30 mT/m/A) and field linearity (≥16 cm linear region). These parameters were verified using field distribution measurements by B0 -mapping. The correction of geometrical distortions was performed using theoretical field distribution of the coil. Simulations and measurements were performed to investigate the echo planar imaging echo-spacing reduction due to the improved gradient performance. The resulting coil featured a 16-cm linear region, a weight of 45 kg, an installation time of 15 min, and a maximum gradient strength and slew rate of 200 mT/m and 1300 T/m/s, respectively, when paired with a commercially available gradient amplifier (940 V/630 A). The field distribution measurements matched the theoretically expected field. By utilizing the theoretical field distribution, geometrical distortions were corrected to within 6% of the whole-body gradient reference image in the target region. Compared with a whole-body gradient set, a maximum reduction in echo-spacing of a factor of 2.3 was found, translating to a 344 μs echo-spacing, for a field of view of 192 mm, a receiver bandwidth of 920 kHz and a gradient amplitude of 112 mT/m. We present a lightweight, single-axis gradient insert design that can provide high gradient performance and an increase in spatiotemporal resolution with correctable geometrical distortions while also offering a short installation time of less than 15 min and minimal system modifications.
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Affiliation(s)
- Edwin Versteeg
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | | | | | | | - Erik R. Huijing
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Arjan D. Hendriks
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Jeroen Hendrikse
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Dennis W. J. Klomp
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Jeroen C. W. Siero
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
- Spinoza Center for NeuroimagingAmsterdamthe Netherlands
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14
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Wang X, Wang Q, Zhang P, Qian S, Liu S, Liu DQ. Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data. Neuroinformatics 2021; 19:23-38. [PMID: 32285299 DOI: 10.1007/s12021-020-09463-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.
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Affiliation(s)
- Xinbo Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Qing Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Peiwen Zhang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Shufang Qian
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Shiyu Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China.
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15
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Lin L, Hu P, Tong X, Na S, Cao R, Yuan X, Garrett DC, Shi J, Maslov K, Wang LV. High-speed three-dimensional photoacoustic computed tomography for preclinical research and clinical translation. Nat Commun 2021; 12:882. [PMID: 33563996 PMCID: PMC7873071 DOI: 10.1038/s41467-021-21232-1] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 01/15/2021] [Indexed: 01/30/2023] Open
Abstract
Photoacoustic computed tomography (PACT) has generated increasing interest for uses in preclinical research and clinical translation. However, the imaging depth, speed, and quality of existing PACT systems have previously limited the potential applications of this technology. To overcome these issues, we developed a three-dimensional photoacoustic computed tomography (3D-PACT) system that features large imaging depth, scalable field of view with isotropic spatial resolution, high imaging speed, and superior image quality. 3D-PACT allows for multipurpose imaging to reveal detailed angiographic information in biological tissues ranging from the rodent brain to the human breast. In the rat brain, we visualize whole brain vasculatures and hemodynamics. In the human breast, an in vivo imaging depth of 4 cm is achieved by scanning the breast within a single breath hold of 10 s. Here, we introduce the 3D-PACT system to provide a unique tool for preclinical research and an appealing prototype for clinical translation.
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Affiliation(s)
- Li Lin
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Peng Hu
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Xin Tong
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Shuai Na
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Rui Cao
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Xiaoyun Yuan
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA ,grid.12527.330000 0001 0662 3178Present Address: Department of Electronic Engineering, Tsinghua University, Haidian District, Beijing, China
| | - David C. Garrett
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Junhui Shi
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA ,Present Address: Zhejiang Lab, China Artificial Intelligence Town, Hangzhou Zhejiang, China
| | - Konstantin Maslov
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
| | - Lihong V. Wang
- grid.20861.3d0000000107068890Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA USA
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16
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Abstract
Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Helen C. Barron
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Jason P. Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, CanadaM5G 1L7
| | - Cassandra Sampaio-Baptista
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
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17
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Zaboski BA, Stern EF, Skosnik PD, Pittenger C. Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review. Front Psychiatry 2021; 12:703398. [PMID: 34408681 PMCID: PMC8365146 DOI: 10.3389/fpsyt.2021.703398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/21/2021] [Indexed: 12/28/2022] Open
Abstract
Electroencephalography (EEG) measures the brain's electrical activity with high temporal resolution. In comparison to neuroimaging modalities such as MRI or PET, EEG is relatively cheap, non-invasive, portable, and simple to administer, making it an attractive tool for clinical deployment. Despite this, studies utilizing EEG to investigate obsessive-compulsive disorder (OCD) are relatively sparse. This contrasts with a robust literature using other brain imaging methodologies. The present review examines studies that have used EEG to examine predictors and correlates of response in OCD and draws tentative conclusions that may guide much needed future work. Key findings include a limited literature base; few studies have attempted to predict clinical change from EEG signals, and they are confounded by the effects of both pharmacotherapy and psychotherapy. The most robust literature, consisting of several studies, has examined event-related potentials, including the P300, which several studies have reported to be abnormal at baseline in OCD and to normalize with treatment; but even here the literature is quite heterogeneous, and more work is needed. With more robust research, we suggest that the relatively low cost and convenience of EEG, especially in comparison to fMRI and PET, make it well-suited to the development of feasible personalized treatment algorithms.
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Affiliation(s)
- Brian A Zaboski
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Elisa F Stern
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Patrick D Skosnik
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Christopher Pittenger
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
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18
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Abstract
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.
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Affiliation(s)
- Jackie Yang
- NYU Grossman School of Medicine, 550 1(st) Avenue, New York, NY 10016, USA
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
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19
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Bhandari R, Kirilina E, Caan M, Suttrup J, De Sanctis T, De Angelis L, Keysers C, Gazzola V. Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T. Neuroimage 2020; 213:116731. [PMID: 32173409 PMCID: PMC7181191 DOI: 10.1016/j.neuroimage.2020.116731] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/27/2020] [Accepted: 03/09/2020] [Indexed: 02/06/2023] Open
Abstract
Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.
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Affiliation(s)
- Ritu Bhandari
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands.
| | - Evgeniya Kirilina
- Center for Cognitive Neuroscience, Free University, Berlin, Germany; Max Plank Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Matthan Caan
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Biomedical Engineering & Physics, Amsterdam, the Netherlands
| | - Judith Suttrup
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Teresa De Sanctis
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Lorenzo De Angelis
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Christian Keysers
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands
| | - Valeria Gazzola
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands.
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20
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Fair DA, Miranda-Dominguez O, Snyder AZ, Perrone A, Earl EA, Van AN, Koller JM, Feczko E, Tisdall MD, van der Kouwe A, Klein RL, Mirro AE, Hampton JM, Adeyemo B, Laumann TO, Gratton C, Greene DJ, Schlaggar BL, Hagler DJ, Watts R, Garavan H, Barch DM, Nigg JT, Petersen SE, Dale AM, Feldstein-Ewing SW, Nagel BJ, Dosenbach NU. Correction of respiratory artifacts in MRI head motion estimates. Neuroimage 2020; 208:116400. [PMID: 31778819 PMCID: PMC7307712 DOI: 10.1016/j.neuroimage.2019.116400] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 11/19/2019] [Accepted: 11/23/2019] [Indexed: 02/08/2023] Open
Abstract
Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.
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Affiliation(s)
- Damien A. Fair
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA.,Department of Psychiatry, Oregon Health & Sciences University, Portland, OR, USA.,Advanced Imaging Research Center, Oregon Health & Sciences University, Portland, OR, USA.,To whom correspondence should be addressed. ,
| | - Oscar Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA
| | - Abraham Z. Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Anders Perrone
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA
| | - Eric A. Earl
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA
| | - Andrew N. Van
- Department of Biomedical Engineering, Washington University, St. Louis, MO, USA
| | - Jonathan M. Koller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA.,Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Sciences University, Portland OR, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA USA
| | - Rachel L. Klein
- Department of Psychiatry, Oregon Health & Sciences University, Portland, OR, USA
| | - Amy E. Mirro
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline M. Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology & Neurology, Northwestern University, Chicago, IL, USA
| | - Deanna J. Greene
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Bradley L. Schlaggar
- Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology; Johns Hopkins University; Baltimore; MD; USA Department of Pediatrics; Johns Hopkins University
| | - Donald J. Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Richard Watts
- FAS Brain Imaging Center, Yale University, New Haven, CT, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Deanna M. Barch
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.,Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, USA
| | - Joel T. Nigg
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA.,Department of Psychiatry, Oregon Health & Sciences University, Portland, OR, USA
| | - Steven E. Petersen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Biomedical Engineering, Washington University, St. Louis, MO, USA,Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, USA,Department of Neuroscience, Washington University, St. Louis, MO, USA
| | - Anders M. Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA,Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | | | - Bonnie J. Nagel
- Department of Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR, USA.,Department of Psychiatry, Oregon Health & Sciences University, Portland, OR, USA
| | - Nico U.F. Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Biomedical Engineering, Washington University, St. Louis, MO, USA,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA,Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, USA,To whom correspondence should be addressed. ,
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21
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Abstract
Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories: (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs.
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Affiliation(s)
- Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (M-BIC), Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (M-BIC), Maastricht, The 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Agrawal U, Brown EN, Lewis LD. Model-based physiological noise removal in fast fMRI. Neuroimage 2019; 205:116231. [PMID: 31589991 DOI: 10.1016/j.neuroimage.2019.116231] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/23/2019] [Accepted: 09/26/2019] [Indexed: 11/26/2022] Open
Abstract
Recent improvements in the speed and sensitivity of fMRI acquisition techniques suggest that fast fMRI can be used to detect and precisely localize sub-second neural dynamics. This enhanced temporal resolution has enormous potential for neuroscientists. However, physiological noise poses a major challenge for the analysis of fast fMRI data. Physiological noise scales with sensitivity, and its autocorrelation structure is altered in rapidly sampled data, suggesting that new approaches are needed for physiological noise removal in fast fMRI. Existing strategies either rely on external physiological recordings, which can be noisy or difficult to collect, or employ data-driven approaches which make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements. We then created a joint model of neural hemodynamics, and physiological and autocorrelated noise to more accurately remove noise. We first verified that HRAN accurately estimates cardiac and respiratory dynamics and that our model demonstrates goodness-of-fit in fast fMRI data. In task-driven data, we then demonstrated that HRAN is able to remove physiological noise while leaving the neural signal intact, thereby increasing detection of task-driven voxels. Finally, we established that in both simulations and fast fMRI data HRAN is able to improve statistical inferences as compared with gold-standard physiological noise removal techniques. In conclusion, we created a tool that harnesses the novel information in fast fMRI to remove physiological noise, enabling broader use of the technology to study human brain function.
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Affiliation(s)
- Uday Agrawal
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Emery N Brown
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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Jamadar SD, Sforazzini F, Raniga P, Ferris NJ, Paton B, Bailey MJ, Brodtmann A, Yates PA, Donnan GA, Ward SA, Woods RL, Storey E, McNeil JJ, Egan GF. Sexual Dimorphism of Resting-State Network Connectivity in Healthy Ageing. J Gerontol B Psychol Sci Soc Sci 2019; 74:1121-1131. [PMID: 29471348 PMCID: PMC6748717 DOI: 10.1093/geronb/gby004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 01/30/2018] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVES The onset of many illnesses is confounded with age and sex. Increasing age is a risk factor for the development of many illnesses, and sexual dimorphism influences brain anatomy, function, and cognition. Here, we examine frequency-specific connectivity in resting-state networks in a large sample (n = 406) of healthy aged adults. METHOD We quantify frequency-specific connectivity in three resting-state networks known to be implicated in age-related decline: the default mode, dorsal attention, and salience networks, using multiband functional magnetic resonance imaging. Frequency-specific connectivity was quantified in four bands: low (0.015-0.027 Hz), moderately low (0.027-0.073 Hz), moderately high (0.073-0.198 Hz), and high (0.198-0.5 Hz) frequency bands, using mean intensity and spatial extent. Differences in connectivity between the sexes in each of the three networks were examined. RESULTS Each network showed the largest intensity and spatial extent at low frequencies and smallest extent at high frequencies. Males showed greater connectivity than females in the salience network. Females showed greater connectivity than males in the default mode network. DISCUSSION Results in this healthy aged cohort are compatible with those obtained in young samples, suggesting that frequency-specific connectivity, and differences between the sexes, are maintained into older age. Our results indicate that sex should be considered as an influencing factor in studies of resting-state connectivity.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Victoria, Australia
- Monash Institute for Cognitive and Clinical Neurosciences, Monash University, Clayton, Victoria, Australia
| | - Francesco Sforazzini
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- Monash Institute for Cognitive and Clinical Neurosciences, Monash University, Clayton, Victoria, Australia
| | - Parnesh Raniga
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- CSIRO eHealth Research Centre, Herston, Brisbane, Queensland, Australia
| | - Nicholas J Ferris
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Bryan Paton
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- School of Psychology, University of Newcastle, Callaghan, New South Wales, Australia
| | - Michael J Bailey
- Department of Epidemiology and Preventive Medicine, Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Victoria, Australia
| | - Amy Brodtmann
- Behavioural Neuroscience, Florey Institute for Neuroscience and Mental Health, Melbourne Brain Centre, Heidelberg, Victoria, Australia
| | - Paul A Yates
- Department of Aged Care Services, Austin Health, Heidelberg, Victoria, Australia
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Geoffrey A Donnan
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Stephanie A Ward
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
- Monash Ageing Research Centre (MONARC), The Kingston Centre, Cheltenham, Victoria, Australia
| | - Robyn L Woods
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - Elsdon Storey
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Neuroscience (Medicine), Monash University, Alfred Hospital Campus, Melbourne, Victoria, Australia
| | - John J McNeil
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Victoria, Australia
- Monash Institute for Cognitive and Clinical Neurosciences, Monash University, Clayton, Victoria, Australia
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Lührs M, Riemenschneider B, Eck J, Andonegui AB, Poser BA, Heinecke A, Krause F, Esposito F, Sorger B, Hennig J, Goebel R. The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution. Neuroimage 2019; 194:228-43. [PMID: 30910728 DOI: 10.1016/j.neuroimage.2019.03.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/14/2019] [Accepted: 03/19/2019] [Indexed: 11/20/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) enables the update of various brain-activity measures during an ongoing experiment as soon as a new brain volume is acquired. However, the recorded Blood-oxygen-level dependent (BOLD) signal also contains physiological artifacts such as breathing and heartbeat, which potentially cause misleading false positive effects especially problematic in brain-computer interface (BCI) and neurofeedback (NF) setups. The low temporal resolution of echo planar imaging (EPI) sequences (which is in the range of seconds) prevents a proper separation of these artifacts from the BOLD signal. MR-Encephalography (MREG) has been shown to provide the high temporal resolution required to unalias and correct for physiological fluctuations and leads to increased specificity and sensitivity for mapping task-based activation and functional connectivity as well as for detecting dynamic changes in connectivity over time. By comparing a simultaneous multislice echo planar imaging (SMS-EPI) sequence and an MREG sequence using the same nominal spatial resolution in an offline analysis for three different experimental fMRI paradigms (perception of house and face stimuli, motor imagery, Stroop task), the potential of this novel technique for future BCI and NF applications was investigated. First, adapted general linear model pre-whitening which accounts for the high temporal resolution in MREG was implemented to calculate proper statistical results and be able to compare these with the SMS-EPI sequence. Furthermore, the respiration- and cardiac pulsation-related signals were successfully separated from the MREG signal using independent component analysis which were then included as regressors for a GLM analysis. Only the MREG sequence allowed to clearly separate cardiac pulsation and respiration components from the signal time course. It could be shown that these components highly correlate with the recorded respiration and cardiac pulsation signals using a respiratory belt and fingertip pulse plethysmograph. Temporal signal-to-noise ratios of SMS-EPI and MREG were comparable. Functional connectivity analysis using partial correlation showed a reduced standard error in MREG compared to SMS-EPI. Also, direct time course comparisons by down-sampling the MREG signal to the SMS-EPI temporal resolution showed lower variance in MREG. In general, we show that the higher temporal resolution is beneficial for fMRI time course modeling and this aspect can be exploited in offline application but also, is especially attractive, for real-time BCI and NF applications.
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Krishnamurthy R, Wang DJJ, Cervantes B, McAllister A, Nelson E, Karampinos DC, Hu HH. Recent Advances in Pediatric Brain, Spine, and Neuromuscular Magnetic Resonance Imaging Techniques. Pediatr Neurol 2019; 96:7-23. [PMID: 31023603 DOI: 10.1016/j.pediatrneurol.2019.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 02/25/2019] [Accepted: 03/03/2019] [Indexed: 12/21/2022]
Abstract
Magnetic resonance imaging (MRI) is a powerful radiologic tool with the ability to generate a variety of proton-based signal contrast from tissues. Owing to this immense flexibility in signal generation, new MRI techniques are constantly being developed, tested, and optimized for clinical utility. In addition, the safe and nonionizing nature of MRI makes it a suitable modality for imaging in children. In this review article, we summarize a few of the most popular advances in MRI techniques in recent years. In particular, we highlight how these new developments have affected brain, spine, and neuromuscular imaging and focus on their applications in pediatric patients. In the first part of the review, we discuss new approaches such as multiphase and multidelay arterial spin labeling for quantitative perfusion and angiography of the brain, amide proton transfer MRI of the brain, MRI of brachial plexus and lumbar plexus nerves (i.e., neurography), and T2 mapping and fat characterization in neuromuscular diseases. In the second part of the review, we focus on describing new data acquisition strategies in accelerated MRI aimed collectively at reducing the scan time, including simultaneous multislice imaging, compressed sensing, synthetic MRI, and magnetic resonance fingerprinting. In discussing the aforementioned, the review also summarizes the advantages and disadvantages of each method and their current state of commercial availability from MRI vendors.
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Affiliation(s)
| | - Danny J J Wang
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Barbara Cervantes
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
| | | | - Eric Nelson
- Center for Biobehavioral Health, Nationwide Children's Hospital, Columbus, Ohio
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Luo T, Noll DC, Fessler JA, Nielsen JF. A GRAPPA algorithm for arbitrary 2D/3D non-Cartesian sampling trajectories with rapid calibration. Magn Reson Med 2019; 82:1101-1112. [PMID: 31050011 DOI: 10.1002/mrm.27801] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/03/2019] [Accepted: 04/16/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non-Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non-Cartesian imaging. METHODS We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k-space location with a distinct constellation, that is, local sampling pattern. We calibrate these generalized kernels using the Fourier transform phase shift property applied to fully gridded or separately acquired Cartesian Autocalibration signal (ACS) data. To handle the resulting large number of different kernels, we introduce a fast calibration algorithm based on nonuniform FFT (NUFFT) and adoption of circulant ACS boundary conditions. We applied our method to retrospectively under-sampled rotated stack-of-stars/spirals in vivo datasets, and to a prospectively under-sampled rotated stack-of-spirals functional MRI acquisition with a finger-tapping task. RESULTS We reconstructed all datasets without performing any trajectory-specific manual adaptation of the method. For the retrospectively under-sampled experiments, our method achieved image quality (i.e., error and g-factor maps) comparable to conjugate gradient SENSE (cg-SENSE) and SPIRiT. Functional activation maps obtained from our method were in good agreement with those obtained using cg-SENSE, but required a shorter total reconstruction time (for the whole time-series): 3 minutes (proposed) vs 15 minutes (cg-SENSE). CONCLUSIONS This paper introduces a general 3D non-Cartesian GRAPPA that is fast enough for practical use on today's computers. It is a direct generalization of original GRAPPA to non-Cartesian scenarios. The method should be particularly useful in dynamic imaging where a large number of frames are reconstructed from a single set of ACS data.
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Affiliation(s)
- Tianrui Luo
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Douglas C Noll
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Jeffrey A Fessler
- Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, Michigan
| | - Jon-Fredrik Nielsen
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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Jamadar SD, Ward PGD, Li S, Sforazzini F, Baran J, Chen Z, Egan GF. Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex. Neuroimage 2019; 189:258-266. [DOI: 10.1016/j.neuroimage.2019.01.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/01/2018] [Accepted: 01/03/2019] [Indexed: 01/24/2023] Open
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Lee HL, Li Z, Coulson EJ, Chuang KH. Ultrafast fMRI of the rodent brain using simultaneous multi-slice EPI. Neuroimage 2019; 195:48-58. [PMID: 30910726 DOI: 10.1016/j.neuroimage.2019.03.045] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 03/05/2019] [Accepted: 03/19/2019] [Indexed: 12/25/2022] Open
Abstract
Increasing spatial and temporal resolutions of functional MRI (fMRI) measurement has been shown to benefit the study of neural dynamics and functional interaction. However, acceleration of rodent brain fMRI using parallel and simultaneous multi-slice imaging techniques is hampered by the lack of high-density phased-array coils for the small brain. To overcome this limitation, we adapted phase-offset multiplanar and blipped-controlled aliasing echo planar imaging (EPI) to enable simultaneous multi-slice fMRI of the mouse brain using a single loop coil on a 9.4T scanner. Four slice bands of 0.3 × 0.3 × 0.5 mm3 resolution can be simultaneously acquired to cover the whole brain at a temporal resolution of 300 ms or the whole cerebrum in 150 ms. Instead of losing signal-to-noise ratio (SNR), both spatial and temporal SNR can be increased due to the increased k-space sampling compared to a standard single-band EPI. Task fMRI using a visual stimulation shows close to 80% increase of z-score and 4 times increase of activated area in the visual cortex using the multiband EPI due to the highly increased temporal samples. Resting-state fMRI shows reliable detection of bilateral connectivity by both single-band and multiband EPI, but no significant difference was found. Without the need of a dedicated hardware, we have demonstrated a practical method that can enable unparallelly fast whole-brain fMRI for preclinical studies. This technique can be used to increase sensitivity, distinguish transient response or acquire high spatiotemporal resolution fMRI.
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Affiliation(s)
- Hsu-Lei Lee
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre of Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Zengmin Li
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Elizabeth J Coulson
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
| | - Kai-Hsiang Chuang
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre of Advanced Imaging, The University of Queensland, Brisbane, Australia.
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Abstract
Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Stanford University, Stanford, CA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Gary H Glover
- Department of Radiology, Stanford University, Stanford, CA, USA
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Chen P, Ye E, Jin X, Zhu Y, Wang L. Association between Thalamocortical Functional Connectivity Abnormalities and Cognitive Deficits in Schizophrenia. Sci Rep 2019; 9:2952. [PMID: 30814558 DOI: 10.1038/s41598-019-39367-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 01/22/2019] [Indexed: 02/08/2023] Open
Abstract
Cognitive deficits are considered a core component of schizophrenia and may predict functional outcome. However, the neural underpinnings of neuropsychological impairment remain to be fully elucidated. Data of 59 schizophrenia patients and 72 healthy controls from a public resting-state fMRI database was employed in our study. Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Battery was used to measure deficits of cognitive abilities in schizophrenia. Neural correlates of cognitive deficits in schizophrenia were examined by linear regression analysis of the thalamocortical network activity with scores of seven cognitive domains. We confirmed the combination of reduced prefrontal-thalamic connectivity and increased sensorimotor-thalamic connectivity in patients with schizophrenia. Correlation analysis with cognition revealed that in schizophrenia (1) the thalamic functional connectivity in the bilateral pre- and postcentral gyri was negatively correlated with attention/vigilance and speed of processing (Pearson’s r ≤ −0.443, p ≤ 0.042, FWE corrected), and positively correlated with patients’ negative symptoms (Pearson’s r ≥ 0.375, p ≤ 0.003, FWE corrected); (2) the thalamic functional connectivity in the right cerebellum was positively correlated with speed of processing (Pearson’s r = 0.388, p = 0.01, FWE corrected). Our study demonstrates that thalamic hyperconnectivity with sensorimotor areas is related to the severity of cognitive deficits and clinical symptoms, and extends our understanding of the neural underpinnings of “cognitive dysmetria” in schizophrenia.
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Peng L, Zeng LL, Liu Q, Wang L, Qin J, Xu H, Shen H, Li H, Hu D. Functional connectivity changes in the entorhinal cortex of taxi drivers. Brain Behav 2018; 8:e01022. [PMID: 30112812 PMCID: PMC6160637 DOI: 10.1002/brb3.1022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 05/01/2018] [Accepted: 05/09/2018] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION As a major interface between the hippocampus and the neocortex, the entorhinal cortex (EC) is widely known to play a pivotal role in spatial memory and navigation. Previous studies have suggested that the EC can be divided into the anterior-lateral (alEC) and the posterior-medial subregions (pmEC), with the former receiving object-related information from the perirhinal cortex and the latter receiving scene-related information from the parahippocampal cortex. However, the functional connectivity maps of the EC subregions in the context of extensive navigation experience remain elusive. In this study, we analyzed the functional connectivity of the EC in subjects with long-term navigation experience and aimed to find the navigation-related change in the functional properties of the human EC. METHODS We investigated the resting-state functional connectivity changes in the EC subregions by comparing the EC functional connectivity maps of 20 taxi drivers with those of 20 nondriver controls. Furthermore, we examined whether the functional connectivity changes of the EC were related to the number of taxi driving years. RESULTS Significantly reduced functional connectivity was found in the taxi drivers between the left pmEC and the right anterior cingulate cortex (ACC), right angular gyrus, and bilateral precuneus as well as some temporal regions, and between the right pmEC and the left inferior temporal gyrus. Notably, the strength of the functional connectivity between the left pmEC and the left precuneus, as well as the right ACC, was negatively correlated with the years of taxi driving. CONCLUSION This is the first study to explore the impact of long-term navigation experience on the connectivity patterns of the EC, the results of which may shed new light on the potential influence of extensive navigational training on the functional organization of the EC in healthy human brains.
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Affiliation(s)
- Limin Peng
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Ling-Li Zeng
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Qiang Liu
- Research Centre of Brain Function and Psychological Science, Shenzhen University, Shenzhen, Guangdong, China
| | - Lubin Wang
- Cognitive and Mental Health Research Center, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Jian Qin
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Huaze Xu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Hui Shen
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Hong Li
- Research Centre of Brain Function and Psychological Science, Shenzhen University, Shenzhen, Guangdong, China
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
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DSouza AM, Abidin AZ, Chockanathan U, Schifitto G, Wismüller A. Mutual connectivity analysis of resting-state functional MRI data with local models. Neuroimage 2018; 178:210-223. [PMID: 29777828 DOI: 10.1016/j.neuroimage.2018.05.038] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/11/2018] [Accepted: 05/14/2018] [Indexed: 12/16/2022] Open
Abstract
Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and reveal insights into interactions amongst different brain regions. However, most connectivity analysis approaches adopted in practice are linear and non-directional. In this paper, we demonstrate the advantage of a data-driven, directed connectivity analysis approach called Mutual Connectivity Analysis using Local Models (MCA-LM) that approximates connectivity by modeling nonlinear dependencies of signal interaction, over more conventionally used approaches, such as Pearson's and partial correlation, Patel's conditional dependence measures, etcetera. We demonstrate on realistic simulations of fMRI data that, at long sampling intervals, i.e. high repetition time (TR) of fMRI signals, MCA-LM performs better than or comparable to correlation-based methods and Patel's measures. However, at fast image acquisition rates corresponding to low TR, MCA-LM significantly outperforms these methods. This insight is particularly useful in the light of recent advances in fast fMRI acquisition techniques. Methods that can capture the complex dynamics of brain activity, such as MCA-LM, should be adopted to extract as much information as possible from the improved representation. Furthermore, MCA-LM works very well for simulations generated at weak neuronal interaction strengths, and simulations modeling inhibitory and excitatory connections as it disentangles the two opposing effects between pairs of regions/voxels. Additionally, we demonstrate that MCA-LM is capable of capturing meaningful directed connectivity on experimental fMRI data. Such results suggest that it introduces sufficient complexity into modeling fMRI time-series interactions that simple, linear approaches cannot, while being data-driven, computationally practical and easy to use. In conclusion, MCA-LM can provide valuable insights towards better understanding brain activity.
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Affiliation(s)
- Adora M DSouza
- Department of Electrical Engineering, University of Rochester, Rochester, NY, USA.
| | - Anas Z Abidin
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
| | - Udaysankar Chockanathan
- Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA
| | - Axel Wismüller
- Department of Electrical Engineering, University of Rochester, Rochester, NY, USA; Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilians University, Munich, Germany
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Demetriou L, Kowalczyk OS, Tyson G, Bello T, Newbould RD, Wall MB. A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI. Neuroimage 2018; 176:404-416. [PMID: 29738911 DOI: 10.1016/j.neuroimage.2018.05.011] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 04/30/2018] [Accepted: 05/02/2018] [Indexed: 11/25/2022] Open
Abstract
Accelerated functional Magnetic Resonance Imaging (fMRI) with 'multiband' protocols is now relatively widespread. These protocols can be used to dramatically reduce the repetition time (TR) and produce a time-series sampled at a higher temporal resolution, which may produce benefits in the statistical methods typically used to analyse fMRI data. We tested the effects of higher temporal resolutions for fMRI on statistical outcome measures in a comprehensive manner on two different MRI scanner platforms. Spatial resolution was maintained at a constant of 3 mm isotropic voxels, and an in-plane acceleration factor of 2 was used for all experiments. Experiment 1 tested a range of acceleration factors (1-6) against a standard EPI protocol on a single composite task that mapped a number of basic sensory, motor, and cognitive networks. Experiment 2 compared the standard protocol with acceleration factors of 2 and 3 on both resting-state and two task paradigms (an N-back task, and faces/places task), with a number of different analysis approaches. Results from experiment 1 showed modest but relatively inconsistent effects of the higher sampling rate on statistical outcome measures. Experiment 2 showed strong benefits of the multiband protocols on results derived from resting-state data, but more varied effects on results from the task paradigms. Notably, the multiband protocols were superior when Multi-Voxel Pattern Analysis was used to interrogate the faces/places data, but showed less benefit in conventional General Linear Model analyses of the same data. In general, ROI-derived measures of statistical effects benefitted only modestly from higher sampling resolution, with greater effects seen when using a measure of the top range of statistical values. Across both experiments, results from the two scanner platforms were broadly comparable. The statistical benefits of high temporal resolution fMRI with multiband protocols may therefore depend on a number of factors, including the nature of the investigation (resting-state vs. task-based), the experimental design, the particular statistical outcome measure, and the type of analysis used.
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Affiliation(s)
- Lysia Demetriou
- Imanova Centre for Imaging Sciences, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - Oliwia S Kowalczyk
- Department of Psychology, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
| | - Gabriella Tyson
- Department of Psychology, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK
| | - Thomas Bello
- Department of Biomedical Engineering, The University of Arizona, 1127 E. James E. Rogers Way, P.O. Box 210020, Tucson, AZ 85721-0020, USA
| | - Rexford D Newbould
- Imanova Centre for Imaging Sciences, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK
| | - Matthew B Wall
- Imanova Centre for Imaging Sciences, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK; Division of Brain Sciences, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK; Clinical Psychopharmacology Unit, Research Department of Clinical, Educational and Health Psychology, University College London, Gower St, London, WC1E 6BT, UK.
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Li Y, Edalati M, Du X, Wang H, Cao JJ. Self-calibrated correlation imaging with k-space variant correlation functions. Magn Reson Med 2018; 79:1483-1494. [PMID: 28686810 PMCID: PMC5756703 DOI: 10.1002/mrm.26818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 06/08/2017] [Accepted: 06/12/2017] [Indexed: 12/29/2022]
Abstract
PURPOSE Correlation imaging is a previously developed high-speed MRI framework that converts parallel imaging reconstruction into the estimate of correlation functions. The presented work aims to demonstrate this framework can provide a speed gain over parallel imaging by estimating k-space variant correlation functions. METHODS Because of Fourier encoding with gradients, outer k-space data contain higher spatial-frequency image components arising primarily from tissue boundaries. As a result of tissue-boundary sparsity in the human anatomy, neighboring k-space data correlation varies from the central to the outer k-space. By estimating k-space variant correlation functions with an iterative self-calibration method, correlation imaging can benefit from neighboring k-space data correlation associated with both coil sensitivity encoding and tissue-boundary sparsity, thereby providing a speed gain over parallel imaging that relies only on coil sensitivity encoding. This new approach is investigated in brain imaging and free-breathing neonatal cardiac imaging. RESULTS Correlation imaging performs better than existing parallel imaging techniques in simulated brain imaging acceleration experiments. The higher speed enables real-time data acquisition for neonatal cardiac imaging in which physiological motion is fast and non-periodic. CONCLUSION With k-space variant correlation functions, correlation imaging gives a higher speed than parallel imaging and offers the potential to image physiological motion in real-time. Magn Reson Med 79:1483-1494, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yu Li
- Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, Greenvale, New York, USA
| | - Masoud Edalati
- Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, Greenvale, New York, USA
| | - Xingfu Du
- Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, Greenvale, New York, USA
| | - Hui Wang
- Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, Greenvale, New York, USA
| | - Jie J Cao
- Cardiac Imaging, DeMatteis Center for Cardiac Research and Education, St. Francis Hospital, Greenvale, New York, USA
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Dumoulin SO, Fracasso A, van der Zwaag W, Siero JC, Petridou N. Ultra-high field MRI: Advancing systems neuroscience towards mesoscopic human brain function. Neuroimage 2018; 168:345-57. [DOI: 10.1016/j.neuroimage.2017.01.028] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2016] [Revised: 11/06/2016] [Accepted: 01/12/2017] [Indexed: 01/26/2023] Open
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Li X, Ma X, Li L, Zhang Z, Zhang X, Tong Y, Wang L, Sen Song, Guo H. Dual-TRACER: High resolution fMRI with constrained evolution reconstruction. Neuroimage 2018; 164:172-182. [DOI: 10.1016/j.neuroimage.2017.02.087] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 02/16/2017] [Accepted: 02/27/2017] [Indexed: 11/25/2022] Open
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Alderson T, Kehoe E, Maguire L, Farrell D, Lawlor B, Kenny RA, Lyons D, Bokde ALW, Coyle D. Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:370. [PMID: 29167639 PMCID: PMC5682321 DOI: 10.3389/fnagi.2017.00370] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 10/26/2017] [Indexed: 11/21/2022] Open
Abstract
Alzheimer’s disease (AD) and its prodromal state amnestic mild cognitive impairment (aMCI) are characterized by widespread abnormalities in inter-areal white matter fiber pathways and parallel disruption of default mode network (DMN) resting state functional and effective connectivity. In healthy subjects, DMN and task positive network interaction are modulated by the thalamus suggesting that abnormal task-based DMN deactivation in aMCI may be a consequence of impaired thalamo-cortical white matter circuitry. Thus, this article uses a multimodal approach to assess white matter integrity between thalamus and DMN components and associated effective connectivity in healthy controls (HCs) relative to aMCI patients. Twenty-six HC and 20 older adults with aMCI underwent structural, functional and diffusion MRI scanning using the high angular resolution diffusion-weighted acquisition protocol. The DMN of each subject was identified using independent component analysis (ICA) and resting state effective connectivity was calculated between thalamus and DMN nodes. White matter integrity changes between thalamus and DMN were investigated with constrained spherical deconvolution (CSD) tractography. Significant structural deficits in thalamic white matter projection fibers to posterior DMN components posterior cingulate cortex (PCC) and lateral inferior parietal lobe (IPL) were identified together with significantly reduced effective connectivity from left thalamus to left IPL. Crucially, impaired thalamo-cortical white matter circuitry correlated with memory performance. Disrupted thalamo-cortical structure was accompanied by significant reductions in IPL and PCC cortico-cortical effective connectivity. No structural deficits were found between DMN nodes. Abnormal posterior DMN activity may be driven by changes in thalamic white matter connectivity; a view supported by the close anatomical and functional association of thalamic nuclei effected by AD pathology and the posterior DMN nodes. We conclude that dysfunctional posterior DMN activity in aMCI is consistent with disrupted cortico-thalamo-cortical processing and thalamic-based dissemination of hippocampal disease agents to cortical hubs.
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Affiliation(s)
- Thomas Alderson
- Intelligent Systems Research Centre, University Ulster, Derry, United Kingdom
| | - Elizabeth Kehoe
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Liam Maguire
- Intelligent Systems Research Centre, University Ulster, Derry, United Kingdom
| | - Dervla Farrell
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Brian Lawlor
- Mercer's Institute for Research on Ageing, St. James's Hospital, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Rose A Kenny
- Mercer's Institute for Research on Ageing, St. James's Hospital, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Arun L W Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Damien Coyle
- Intelligent Systems Research Centre, University Ulster, Derry, United Kingdom
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Aggarwal P, Gupta A. Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI. Comput Biol Med 2017; 91:255-266. [PMID: 29101794 DOI: 10.1016/j.compbiomed.2017.10.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/14/2017] [Accepted: 10/19/2017] [Indexed: 12/31/2022]
Abstract
A number of reconstruction methods have been proposed recently for accelerated functional Magnetic Resonance Imaging (fMRI) data collection. However, existing methods suffer with the challenge of greater artifacts at high acceleration factors. This paper addresses the issue of accelerating fMRI collection via undersampled k-space measurements combined with the proposed method based on l1-l1 norm constraints, wherein we impose first l1-norm sparsity on the voxel time series (temporal data) in the transformed domain and the second l1-norm sparsity on the successive difference of the same temporal data. Hence, we name the proposed method as Double Temporal Sparsity based Reconstruction (DTSR) method. The robustness of the proposed DTSR method has been thoroughly evaluated both at the subject level and at the group level on real fMRI data. Results are presented at various acceleration factors. Quantitative analysis in terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative analysis in terms of reproducibility of brain Resting State Networks (RSNs) demonstrate that the proposed method is accurate and robust. In addition, the proposed DTSR method preserves brain networks that are important for studying fMRI data. Compared to the existing methods, the DTSR method shows promising potential with an improvement of 10-12 dB in PSNR with acceleration factors upto 3.5 on resting state fMRI data. Simulation results on real data demonstrate that DTSR method can be used to acquire accelerated fMRI with accurate detection of RSNs.
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Affiliation(s)
- Priya Aggarwal
- Signal Processing and Bio-medical Imaging Lab, Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology (IIIT), Delhi, India.
| | - Anubha Gupta
- Signal Processing and Bio-medical Imaging Lab, Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology (IIIT), Delhi, India.
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Abstract
It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central χ (NC-χ) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed to capture full model uncertainty by simulating from the joint posterior distribution of all model parameters and the binary variable selection indicators. Simulated regression data is used to demonstrate that the Rician model is able to detect the signal much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the Rician model.
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Affiliation(s)
- Bertil Wegmann
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden.,Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Mattias Villani
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
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42
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Di X, Biswal BB. Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution. Front Neurosci 2017; 11:573. [PMID: 29089865 PMCID: PMC5651039 DOI: 10.3389/fnins.2017.00573] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 10/02/2017] [Indexed: 11/18/2022] Open
Abstract
Psychophysiological interaction (PPI) is a regression based method to study task modulated brain connectivity. Despite its popularity in functional MRI (fMRI) studies, its reliability and reproducibility have not been evaluated. We investigated reproducibility and reliability of PPI effects during a simple visual task, and examined the effect of deconvolution on the PPI results. A large open-access dataset was analyzed (n = 138), where a visual task was scanned twice with repetition times (TRs) of 645 and 1,400 ms, respectively. We first replicated our previous results by using the left and right middle occipital gyrus as seeds. Then regions of interest (ROI)-wise analysis was performed among 20 visual-related thalamic and cortical regions, and negative PPI effects were found between many ROIs with the posterior fusiform gyrus as a hub region. Both the seed-based and ROI-wise results were similar between the two runs and between the two PPI methods with and without deconvolution. The non-deconvolution method and the short TR run in general had larger effect sizes and greater extents. However, the deconvolution method performed worse in the 645 ms TR run than the 1,400 ms TR run in the voxel-wise analysis. Given the general similar results between the two methods and the uncertainty of deconvolution, we suggest that deconvolution may be not necessary for PPI analysis on block-designed data. Lastly, intraclass correlations (ICC) between the two runs were much lower for the PPI effects than the activation main effects, which raise cautions on performing inter-subject correlations and group comparisons on PPI effects.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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43
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Huang H, Wang J, Seger C, Lu M, Deng F, Wu X, He Y, Niu C, Wang J, Huang R. Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis. Brain Struct Funct 2017; 223:131-144. [DOI: 10.1007/s00429-017-1479-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 07/17/2017] [Indexed: 01/08/2023]
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Abstract
The rapid developments in functional MRI (fMRI) acquisition methods and hardware technologies in recent years, particularly at high field (≥7 T), have enabled unparalleled visualization of functional detail at a laminar or columnar level, bringing fMRI close to the intrinsic resolution of brain function. These advances highlight the potential of high resolution fMRI to be a valuable tool to study the fundamental processing performed in cortical micro-circuits, and their interactions such as feedforward and feedback processes. Notably, because fMRI measures neuronal activity via hemodynamics, the ultimate resolution it affords depends on the spatial specificity of hemodynamics to neuronal activity at a detailed spatial scale, and by the evolution of this specificity over time. Several laminar (≤1 mm spatial resolution) fMRI studies have examined spatial characteristics of the measured hemodynamic signals across cortical depth, in light of understanding or improving the spatial specificity of laminar fMRI. Few studies have examined temporal features of the hemodynamic response across cortical depth. Temporal features of the hemodynamic response offer an additional means to improve the specificity of fMRI, and could help target neuronal processes and neurovascular coupling relationships across laminae, for example by differences in the onset times of the response across cortical depth. In this review, we discuss factors that affect the timing of neuronal and hemodynamic responses across laminae, touching on the neuronal laminar organization, and focusing on the laminar vascular organization. We provide an overview of hemodynamics across the cortical vascular tree based on optical imaging studies, and review temporal aspects of hemodynamics that have been examined across cortical depth in high spatiotemporal resolution fMRI studies. Last, we discuss the limits and potential of high spatiotemporal resolution fMRI to study laminar neurovascular coupling and neuronal processes.
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Affiliation(s)
- Natalia Petridou
- Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands.
| | - Jeroen C W Siero
- Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands; Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
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Gorges M, Roselli F, Müller HP, Ludolph AC, Rasche V, Kassubek J. Functional Connectivity Mapping in the Animal Model: Principles and Applications of Resting-State fMRI. Front Neurol 2017; 8:200. [PMID: 28539914 PMCID: PMC5423907 DOI: 10.3389/fneur.2017.00200] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 04/24/2017] [Indexed: 12/25/2022] Open
Abstract
"Resting-state" fMRI has substantially contributed to the understanding of human and non-human functional brain organization by the analysis of correlated patterns in spontaneous activity within dedicated brain systems. Spontaneous neural activity is indirectly measured from the blood oxygenation level-dependent signal as acquired by echo planar imaging, when subjects quietly "resting" in the scanner. Animal models including disease or knockout models allow a broad spectrum of experimental manipulations not applicable in humans. The non-invasive fMRI approach provides a promising tool for cross-species comparative investigations. This review focuses on the principles of "resting-state" functional connectivity analysis and its applications to living animals. The translational aspect from in vivo animal models toward clinical applications in humans is emphasized. We introduce the fMRI-based investigation of the non-human brain's hemodynamics, the methodological issues in the data postprocessing, and the functional data interpretation from different abstraction levels. The longer term goal of integrating fMRI connectivity data with structural connectomes obtained with tracing and optical imaging approaches is presented and will allow the interrogation of fMRI data in terms of directional flow of information and may identify the structural underpinnings of observed functional connectivity patterns.
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Affiliation(s)
- Martin Gorges
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Francesco Roselli
- Department of Neurology, University of Ulm, Ulm, Germany
- Department of Anatomy and Cell Biology, University of Ulm, Ulm, Germany
| | | | | | - Volker Rasche
- Core Facility Small Animal MRI, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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Aggarwal P, Shrivastava P, Kabra T, Gupta A. Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage. Brain Inform 2017; 4:65-83. [PMID: 28074352 DOI: 10.1007/s40708-016-0059-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 12/23/2016] [Indexed: 11/22/2022] Open
Abstract
This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l1 minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.
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47
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Wisse LE, Daugherty AM, Olsen RK, Berron D, Carr VA, Stark CE, Amaral RS, Amunts K, Augustinack JC, Bender AR, Bernstein JD, Boccardi M, Bocchetta M, Burggren A, Chakravarty MM, Chupin M, Ekstrom A, de Flores R, Insausti R, Kanel P, Kedo O, Kennedy KM, Kerchner GA, LaRocque KF, Liu X, Maass A, Malykhin N, Mueller SG, Ofen N, Palombo DJ, Parekh MB, Pluta JB, Pruessner JC, Raz N, Rodrigue KM, Schoemaker D, Shafer AT, Steve TA, Suthana N, Wang L, Winterburn JL, Yassa MA, Yushkevich PA, la Joie R. A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus 2017; 27:3-11. [PMID: 27862600 PMCID: PMC5167633 DOI: 10.1002/hipo.22671] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 10/06/2016] [Accepted: 10/17/2016] [Indexed: 01/08/2023]
Abstract
The advent of high-resolution magnetic resonance imaging (MRI) has enabled in vivo research in a variety of populations and diseases on the structure and function of hippocampal subfields and subdivisions of the parahippocampal gyrus. Because of the many extant and highly discrepant segmentation protocols, comparing results across studies is difficult. To overcome this barrier, the Hippocampal Subfields Group was formed as an international collaboration with the aim of developing a harmonized protocol for manual segmentation of hippocampal and parahippocampal subregions on high-resolution MRI. In this commentary we discuss the goals for this protocol and the associated key challenges involved in its development. These include differences among existing anatomical reference materials, striking the right balance between reliability of measurements and anatomical validity, and the development of a versatile protocol that can be adopted for the study of populations varying in age and health. The commentary outlines these key challenges, as well as the proposed solution of each, with concrete examples from our working plan. Finally, with two examples, we illustrate how the harmonized protocol, once completed, is expected to impact the field by producing measurements that are quantitatively comparable across labs and by facilitating the synthesis of findings across different studies. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Laura E.M. Wisse
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ana M. Daugherty
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Champaign, USA
| | - Rosanna K. Olsen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - David Berron
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany
| | - Valerie A. Carr
- Department of Psychology, Stanford University, Palo Alto, USA
- Department of Psychology, San Jose State University, San Jose, USA
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, USA
| | - Robert S.C. Amaral
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, INM-1, Research Center Jülich, Jülich, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jean C. Augustinack
- AA Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, USA
| | - Andrew R. Bender
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Jeffrey D. Bernstein
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | - Marina Boccardi
- LANVIE Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Alison Burggren
- Department of Psychiatry and Biobehavioural Sciences, University of California Los Angeles, Los Angeles, USA
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Marie Chupin
- INSERM, CNRS, UMR-S975, Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
| | - Arne Ekstrom
- Center for Neuroscience, University of California Davis, Davis, USA
- Department of Psychology, University of California Davis, Davis, USA
| | - Robin de Flores
- INSERM U1077, Université de Caen Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, Centre Hospitalier Universitaire de Caen, Caen, France
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory and C.R.I.B., School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Prabesh Kanel
- Department of Computer Science, Florida State University, Tallahassee, USA
| | - Olga Kedo
- Institute of Neuroscience and Medicine, INM-1, Research Center Jülich, Jülich, Germany
| | - Kristen M. Kennedy
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, USA
| | - Geoffrey A. Kerchner
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | | | - Xiuwen Liu
- Department of Computer Science, Florida State University, Tallahassee, USA
| | - Anne Maass
- School of Public Health and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, USA
| | - Nicolai Malykhin
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
- The Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Susanne G. Mueller
- Department of Radiology, University of California, San Francisco, USA
- Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, USA
| | - Noa Ofen
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | | | - Mansi B. Parekh
- Department of Radiology, Stanford University, Palo Alto, USA
| | - John B. Pluta
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jens C. Pruessner
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Naftali Raz
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | - Karen M. Rodrigue
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, USA
| | - Dorothee Schoemaker
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Andrea T. Shafer
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | - Trevor A. Steve
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioural Sciences, University of California Los Angeles, Los Angeles, USA
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, USA
| | - Lei Wang
- Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Julie L. Winterburn
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, USA
- Department of Neurology, University of California Irvine, Irvine, USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Renaud la Joie
- INSERM U1077, Université de Caen Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, Centre Hospitalier Universitaire de Caen, Caen, France
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48
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Griffanti L, Douaud G, Bijsterbosch J, Evangelisti S, Alfaro-Almagro F, Glasser MF, Duff EP, Fitzgibbon S, Westphal R, Carone D, Beckmann CF, Smith SM. Hand classification of fMRI ICA noise components. Neuroimage 2017; 154:188-205. [PMID: 27989777 DOI: 10.1016/j.neuroimage.2016.12.036] [Citation(s) in RCA: 290] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 12/09/2016] [Accepted: 12/14/2016] [Indexed: 11/21/2022] Open
Abstract
We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.
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49
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Chen Y, Liao Y, Yuan L, Liu H, Yun SD, Shah NJ, Chen Z, Zhong J. Referenceless one-dimensional Nyquist ghost correction in multicoil single-shot spatiotemporally encoded MRI. Magn Reson Imaging 2016; 37:222-233. [PMID: 27916658 DOI: 10.1016/j.mri.2016.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 11/29/2016] [Accepted: 11/29/2016] [Indexed: 10/20/2022]
Abstract
Single-shot spatiotemporally encoded (SPEN) MRI is a novel fast imaging method capable of retaining the time efficiency of single-shot echo planar imaging (EPI) but with distortion artifacts significantly reduced. Akin to EPI, the phase inconsistencies between mismatched even and odd echoes also result in the so-called Nyquist ghosts. However, the characteristic of the SPEN signals provides the possibility of obtaining ghost-free images directly from even and odd echoes respectively, without acquiring additional reference scans. In this paper, a theoretical analysis of the Nyquist ghosts manifested in single-shot SPEN MRI is presented, a one-dimensional correction scheme is put forward capable of maintaining definition of image features without blurring when the phase inconsistency along SPEN encoding direction is negligible, and a technique is introduced for convenient and robust correction of data from multi-channel receiver coils. The effectiveness of the proposed processing pipeline is validated by a series of experiments conducted on simulation data, in vivo rats and healthy human brains. The robustness of the method is further verified by implementing distortion correction on ghost corrected data.
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Affiliation(s)
- Ying Chen
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, China.
| | - Yupeng Liao
- Institute of Neuroscience and Medicine-4, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
| | - Lisha Yuan
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, China.
| | - Hui Liu
- MR Collaboration Northeast Asia, Siemens Healthcare, Shanghai, China.
| | - Seong Dae Yun
- Institute of Neuroscience and Medicine-4, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
| | - Nadim Joni Shah
- Institute of Neuroscience and Medicine-4, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
| | - Zhong Chen
- Department of Electronic Science, Xiamen University, Xiamen, China.
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, China; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, China; Department of Imaging Sciences, University of Rochester, Rochester, USA.
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50
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Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet DM, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Woods SW, Cannon TD, Constable RT. Multisite reliability of MR-based functional connectivity. Neuroimage 2016; 146:959-970. [PMID: 27746386 DOI: 10.1016/j.neuroimage.2016.10.020] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Revised: 10/10/2016] [Accepted: 10/12/2016] [Indexed: 11/26/2022] Open
Abstract
Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60-80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07-0.17), but increased with increasing scan duration (ICC=0.21-0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.
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Affiliation(s)
- Stephanie Noble
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA.
| | - Dustin Scheinost
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Emily S Finn
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA
| | - Xilin Shen
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Xenophon Papademetris
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA; Yale University, Department of Biomedical Engineering, New Haven, CT, USA
| | - Sarah C McEwen
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Carrie E Bearden
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Jean Addington
- University of Calgary, Department of Psychiatry, Calgary, Alberta, Canada
| | - Bradley Goodyear
- University of Calgary, Departments of Radiology, Clinical Neurosciences and Psychiatry, Calgary, Alberta, Canada
| | - Kristin S Cadenhead
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Heline Mirzakhanian
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Barbara A Cornblatt
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Doreen M Olvet
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry, San Francisco, CA, USA
| | | | - Diana O Perkins
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Aysenil Belger
- University of North Carolina, Chapel Hill, Department of Psychiatry, Chapel Hill, NC, USA
| | - Larry J Seidman
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Theo G M van Erp
- University of California, Irvine, Department of Psychiatry and Human Behavior, Irvine, CA, USA
| | - Elaine F Walker
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Stephan Hamann
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Scott W Woods
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Tyrone D Cannon
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - R Todd Constable
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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