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Wu J, Nahab F, Allen JW, Hu R, Dehkharghani S, Qiu D. Alterations in Functional Network Topology Within Normal Hemispheres Contralateral to Anterior Circulation Steno-Occlusive Disease: A Resting-State BOLD Study. Front Neurol 2022; 13:780896. [PMID: 35392638 PMCID: PMC8980268 DOI: 10.3389/fneur.2022.780896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/21/2022] [Indexed: 11/20/2022] Open
Abstract
The purpose of this study was to assess spatially remote effects of hemodynamic impairment on functional network topology contralateral to unilateral anterior circulation steno-occlusive disease (SOD) using resting-state blood oxygen level-dependent (BOLD) imaging, and to investigate the relationships between network connectivity and cerebrovascular reactivity (CVR), a measure of hemodynamic stress. Twenty patients with unilateral, chronic anterior circulation SOD and 20 age-matched healthy controls underwent resting-state BOLD imaging. Five-minute standardized baseline BOLD acquisition was followed by acetazolamide infusion to measure CVR. The BOLD baseline was used to analyze network connectivity contralateral to the diseased hemispheres of SOD patients. Compared to healthy controls, reduced network degree (z-score = −1.158 ± 1.217, P < 0.001, false discovery rate (FDR) corrected), local efficiency (z-score = −1.213 ± 1.120, P < 0.001, FDR corrected), global efficiency (z-score = −1.346 ± 1.119, P < 0.001, FDR corrected), and enhanced modularity (z-score = 1.000 ± 1.205, P = 0.002, FDR corrected) were observed in the contralateral, normal hemispheres of SOD patients. Network degree (P = 0.089, FDR corrected; P = 0.027, uncorrected) and nodal efficiency (P = 0.089, FDR corrected; P = 0.045, uncorrected) showed a trend toward a positive association with CVR. The results indicate remote abnormalities in functional connectivity contralateral to the diseased hemispheres in patients with unilateral SOD, despite the absence of macrovascular disease or demonstrable hemodynamic impairment. The clinical impact of remote functional disruptions requires dedicated investigation but may portend far reaching consequence for even putatively unilateral cerebrovascular disease.
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Affiliation(s)
- Junjie Wu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Fadi Nahab
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Jason W. Allen
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
- Joint Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
| | - Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Seena Dehkharghani
- Department of Radiology, New York University Langone Medical Center, New York, NY, United States
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Joint Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
- *Correspondence: Deqiang Qiu
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2
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Stadlbauer A, Kinfe TM, Zimmermann M, Eyüpoglu I, Brandner N, Buchfelder M, Zaiss M, Dörfler A, Brandner S. Association between tissue hypoxia, perfusion restrictions, and microvascular architecture alterations with lesion-induced impairment of neurovascular coupling. J Cereb Blood Flow Metab 2022; 42:526-539. [PMID: 32787542 PMCID: PMC8985434 DOI: 10.1177/0271678x20947546] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has been mainly utilized for the preoperative localization of eloquent cortical areas. However, lesion-induced impairment of neurovascular coupling (NVC) in the lesion border zone may lead to false-negative fMRI results. The purpose of this study was to determine physiological factors impacting the NVC. Twenty patients suffering from brain lesions were preoperatively examined using multimodal neuroimaging including fMRI, magnetoencephalography (MEG) during language or sensorimotor tasks (depending on lesion location), and a novel physiologic MRI approach for the combined quantification of oxygen metabolism, perfusion state, and microvascular architecture. Congruence of brain activity patterns between fMRI and MEG were found in 13 patients. In contrast, we observed missing fMRI activity in perilesional cortex that demonstrated MEG activity in seven patients, which was interpreted as lesion-induced impairment of NVC. In these brain regions with impaired NVC, physiologic MRI revealed significant brain tissue hypoxia, as well as significantly decreased macro- and microvascular perfusion and microvascular architecture. We demonstrated that perilesional hypoxia with reduced vascular perfusion and architecture is associated with lesion-induced impairment of NVC. Our physiologic MRI approach is a clinically applicable method for preoperative risk assessment for the presence of false-negative fMRI results and may prevent severe postoperative functional deficits.
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Affiliation(s)
- Andreas Stadlbauer
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany.,Institute of Medical Radiology, University Clinic of St. Pölten, St. Pölten, Austria
| | - Thomas M Kinfe
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany.,Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
| | - Max Zimmermann
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany.,Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
| | - Ilker Eyüpoglu
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
| | - Nadja Brandner
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Buchfelder
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
| | - Moritz Zaiss
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
| | - Sebastian Brandner
- Department of Neurosurgery, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
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3
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Dowdle LT, Ghose G, Chen CCC, Ugurbil K, Yacoub E, Vizioli L. Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI. Prog Neurobiol 2021; 207:102171. [PMID: 34492308 DOI: 10.1016/j.pneurobio.2021.102171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 01/25/2023]
Abstract
Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States.
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States
| | - Clark C C Chen
- Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States.
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4
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Zhang W, Llera A, Hashemi MM, Kaldewaij R, Koch SBJ, Beckmann CF, Klumpers F, Roelofs K. Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study. Hum Brain Mapp 2020; 41:3089-3099. [PMID: 32293072 PMCID: PMC7336146 DOI: 10.1002/hbm.25000] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/11/2020] [Accepted: 03/19/2020] [Indexed: 01/25/2023] Open
Abstract
Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach, to identify which connectivity changes are most prominently associated with a state of acute stress and individual differences therein. Resting-state functional magnetic resonance imaging scans were taken from 334 healthy participants (79 females) before and after a formal stress induction. For each individual scan, mean time-series were extracted from 46 functional parcels of three major brain networks previously shown to be potentially sensitive to stress effects (default mode network (DMN), salience network (SN), and executive control networks). A data-driven approach was then used to obtain discriminative spatial linear filters that classified the pre- and post-stress scans. To assess potential relevance for understanding individual differences, probability of classification using the most discriminative filters was linked to individual cortisol stress responses. Our model correctly classified pre- versus post-stress states with highly significant accuracy (above 75%; leave-one-out validation relative to chance performance). Discrimination between pre- and post-stress states was mainly based on connectivity changes in regions from the SN and DMN, including the dorsal anterior cingulate cortex, amygdala, posterior cingulate cortex, and precuneus. Interestingly, the probability of classification using these connectivity changes were associated with individual cortisol increases. Our results confirm the involvement of DMN and SN using a data-driven approach, and specifically single out key regions that might receive additional attention in future studies for their relevance also for individual differences.
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Affiliation(s)
- Wei Zhang
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Alberto Llera
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.,Karakter Child and Adolescent Psychiatry, Nijmegen, The Netherlands
| | - Mahur M Hashemi
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Reinoud Kaldewaij
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Saskia B J Koch
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.,Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Floris Klumpers
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Karin Roelofs
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
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5
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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] [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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6
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Danks D, Plis S. Amalgamating evidence of dynamics. SYNTHESE 2019; 196:3213-3230. [PMID: 31527987 PMCID: PMC6746411 DOI: 10.1007/s11229-017-1568-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 09/11/2017] [Indexed: 06/10/2023]
Abstract
Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system's behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially none of them correspond to the underlying system's causal timescale. Second, missing variables have a significantly different impact on time series measurements than they do in the traditional static setting; in particular, they make causal and structural inference much more difficult. In this paper, we argue that amalgamation should proceed by integrating causal knowledge, rather than at the level of "raw" evidence. We defend this claim by first outlining both of these problems, and then showing that they can be solved only if we operate on causal structures. We therefore must use causal discovery methods that are reliable given these problems. Such methods do exist, but their successful application requires careful consideration of the problems that we highlight.
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Affiliation(s)
- David Danks
- Departments of Philosophy & Psychology, 161 Baker Hall, Carnegie Mellon University, Tel.: +1 412-268-8047, Fax: +1 412-268-1440
| | - Sergey Plis
- The Mind Research Network, 1101 Yale Blvd NE
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7
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Zhang W, Hashemi MM, Kaldewaij R, Koch SBJ, Beckmann C, Klumpers F, Roelofs K. Acute stress alters the 'default' brain processing. Neuroimage 2019; 189:870-877. [PMID: 30703518 DOI: 10.1016/j.neuroimage.2019.01.063] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 01/16/2019] [Accepted: 01/24/2019] [Indexed: 11/15/2022] Open
Abstract
Active adaptation to acute stress is essential for coping with daily life challenges. The stress hormone cortisol, as well as large scale re-allocations of brain resources have been implicated in this adaptation. Stress-induced shifts between large-scale brain networks, including salience (SN), central executive (CEN) and default mode networks (DMN), have however been demonstrated mainly under task-conditions. It remains unclear whether such network shifts also occur in the absence of ongoing task-demands, and most critically, whether these network shifts are predictive of individual variation in the magnitude of cortisol stress-responses. In a sample of 335 healthy participants, we investigated stress-induced functional connectivity changes (delta-FC) of the SN, CEN and DMN, using resting-state fMRI data acquired before and after a socially evaluated cold-pressor test and a mental arithmetic task. To investigate which network changes are associated with acute stress, we evaluated the association between cortisol increase and delta-FC of each network. Stress-induced cortisol increase was associated with increased connectivity within the SN, but with decreased coupling of DMN at both local (within network) and global (synchronization with brain regions also outside the network) levels. These findings indicate that acute stress prompts immediate connectivity changes in large-scale resting-state networks, including the SN and DMN in the absence of explicit ongoing task-demands. Most interestingly, this brain reorganization is coupled with individuals' cortisol stress-responsiveness. These results suggest that the observed stress-induced network reorganization might function as a neural mechanism determining individual stress reactivity and, therefore, it could serve as a promising marker for future studies on stress resilience and vulnerability.
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Affiliation(s)
- Wei Zhang
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands.
| | - Mahur M Hashemi
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | - Reinoud Kaldewaij
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | - Saskia B J Koch
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | - Christian Beckmann
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands; Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Floris Klumpers
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | - Karin Roelofs
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
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8
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Sklerov M, Dayan E, Browner N. Functional neuroimaging of the central autonomic network: recent developments and clinical implications. Clin Auton Res 2018; 29:555-566. [PMID: 30470943 PMCID: PMC6858471 DOI: 10.1007/s10286-018-0577-0] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 11/07/2018] [Indexed: 12/08/2023]
Abstract
Purpose The central autonomic network (CAN) is an intricate system of brainstem, subcortical, and cortical structures that play key roles in the function of the autonomic nervous system. Prior to the advent of functional neuroimaging, in vivo studies of the human CAN were limited. The purpose of this review is to highlight the contribution of functional neuroimaging, specifically functional magnetic resonance imaging (fMRI), to the study of the CAN, and to discuss recent advances in this area. Additionally, we aim to emphasize exciting areas for future research. Methods We reviewed the existing literature in functional neuroimaging of the CAN. Here, we focus on fMRI research conducted in healthy human subjects, as well as research that has been done in disease states, to understand CAN function. To minimize confounding, papers examining CAN function in the context of cognition, emotion, pain, and affective disorders were excluded. Results fMRI has led to significant advances in the understanding of human CAN function. The CAN is composed of widespread brainstem and forebrain structures that are intricately connected and play key roles in reflexive and modulatory control of autonomic function. Conclusions fMRI technology has contributed extensively to current knowledge of CAN function. It holds promise to serve as a biomarker in disease states. With ongoing advancements in fMRI technology, there is great opportunity and need for future research involving the CAN.
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Affiliation(s)
- Miriam Sklerov
- Department of Neurology, University of North Carolina, 170 Manning Drive, CB# 7025, Chapel Hill, NC, 27599, USA.
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, 130 Mason Farm Road, CB# 7513, Chapel Hill, NC, 27599, USA
| | - Nina Browner
- Department of Neurology, University of North Carolina, 170 Manning Drive, CB# 7025, Chapel Hill, NC, 27599, USA
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9
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Frass-Kriegl R, Navarro de Lara LI, Pichler M, Sieg J, Moser E, Windischberger C, Laistler E. Flexible 23-channel coil array for high-resolution magnetic resonance imaging at 3 Tesla. PLoS One 2018; 13:e0206963. [PMID: 30383832 PMCID: PMC6211745 DOI: 10.1371/journal.pone.0206963] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 10/23/2018] [Indexed: 11/18/2022] Open
Abstract
PURPOSE The purpose of this work is the design, implementation and evaluation of a mechanically flexible receive-only coil array for magnetic resonance imaging (MRI) at 3 T that can be applied to various target organs and provides high parallel imaging performance. METHODS A 23-channel array was designed based on a rigid-flex printed circuit board (PCB). The flexible multi-layer part contains the copper traces forming the coil elements. The rigid part of the PCB houses the solder joints and lumped elements. The coil housing consists of rigid caps mounted above the rigid parts. Adhesive PTFE sheets cover all flexible parts. The developed array was tested on the bench as well as in phantom and in vivo MRI experiments employing parallel imaging acceleration factors up to six. RESULTS Efficient mutual decoupling between receive elements and detuning between receive array and body coil was achieved. An increased signal-to-noise ratio in comparison to commercial reference coils is demonstrated, especially in regions close to the developed array and for high parallel imaging acceleration factors. Exemplary in vivo images of head, ankle, knee, shoulder and hand are presented. CONCLUSION Based on high sensitivity close to the array and low g-factors, this flexible coil is well suited for studies of occipital and temporal cortex, as well as musculoskeletal targets like knee, ankle, elbow and wrist.
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Affiliation(s)
- Roberta Frass-Kriegl
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Lucia Isabel Navarro de Lara
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Michael Pichler
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Jürgen Sieg
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Ewald Moser
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Christian Windischberger
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Elmar Laistler
- Division MR Physics, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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10
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Wu J, Saindane AM, Zhong X, Qiu D. Simultaneous perfusion and permeability assessments using multiband multi-echo EPI (M2-EPI) in brain tumors. Magn Reson Med 2018; 81:1755-1768. [PMID: 30298595 DOI: 10.1002/mrm.27532] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 08/23/2018] [Accepted: 08/24/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE To study a multiband multi-echo EPI (M2-EPI) sequence for dynamic susceptibility contrast (DSC) perfusion imaging with leakage correction and vascular permeability measurements, and to evaluate the benefits of increased temporal resolution provided by this acquisition strategy on the accuracy of perfusion and permeability estimations. METHODS A novel M2-EPI sequence was developed, and a pharmacokinetic model accounting for contrast agent extravasation was used to produce perfusion maps and additional vascular permeability maps. The advantage of M2-EPI for DSC perfusion imaging was demonstrated in vivo in 5 patients with brain tumors, and numerical simulations were performed to evaluate the advantage of improved temporal resolution afforded by the technique. RESULTS In contrast to underestimations of cerebral blood volume (CBV) in tumors using the single-echo acquisition strategy, M2-EPI provided more plausible estimates of CBV. A quantitative evaluation showed higher estimated values of CBV and mean transit time in tumor tissues using M2-EPI (CBV: 3.08 ± 0.78 mL/100 g versus 1.56 ± 1.38 mL/100 g [P = .006]; mean transit time: 4.94 ± 1.17 seconds versus 1.83 ± 2.06 seconds [P = 0.033]). Numerical simulations showed that higher temporal resolution provided by M2-EPI was associated with more accurate estimates of cerebral blood flow, CBV, and permeability parameters. CONCLUSION The novel M2-EPI acquisition strategy for DSC imaging facilitates leakage-corrected perfusion measurements with additional permeability assessments and more accurate estimates of perfusion/permeability parameters, and may be used as a quantitative tool for the diagnosis, prognosis, and treatment monitoring of brain tumors.
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Affiliation(s)
- Junjie Wu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Amit M Saindane
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Xiaodong Zhong
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.,MR R&D Collaborations, Siemens Healthcare, Atlanta, Georgia
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.,Joint Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
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Craig MM, Manktelow AE, Sahakian BJ, Menon DK, Stamatakis EA. Spectral Diversity in Default Mode Network Connectivity Reflects Behavioral State. J Cogn Neurosci 2018; 30:526-539. [DOI: 10.1162/jocn_a_01213] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Default mode network (DMN) functional connectivity is thought to occur primarily in low frequencies (<0.1 Hz), resulting in most studies removing high frequencies during data preprocessing. In contrast, subtractive task analyses include high frequencies, as these are thought to be task relevant. An emerging line of research explores resting fMRI data at higher-frequency bands, examining the possibility that functional connectivity is a multiband phenomenon. Furthermore, recent studies suggest DMN involvement in cognitive processing; however, without a systematic investigation of DMN connectivity during tasks, its functional contribution to cognition cannot be fully understood. We bridged these concurrent lines of research by examining the contribution of high frequencies in the relationship between DMN and dorsal attention network at rest and during task execution. Our findings revealed that the inclusion of high frequencies alters between network connectivity, resulting in reduced anticorrelation and increased positive connectivity between DMN and dorsal attention network. Critically, increased positive connectivity was observed only during tasks, suggesting an important role for high-frequency fluctuations in functional integration. Moreover, within-DMN connectivity during task execution correlated with RT only when high frequencies were included. These results show that DMN does not simply deactivate during task execution and suggest active recruitment while performing cognitively demanding paradigms.
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Vadapalli R. Assessing functional connectivity of brain network in children with nocturnal enuresis using resting state functional magnetic resonance imaging. Neurol India 2018; 66:1367-1369. [DOI: 10.4103/0028-3886.241392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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13
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Boubela RN, Kalcher K, Huf W, Seidel EM, Derntl B, Pezawas L, Našel C, Moser E. fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions. Sci Rep 2015; 5:10499. [PMID: 25994551 PMCID: PMC4440210 DOI: 10.1038/srep10499] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Accepted: 03/26/2015] [Indexed: 11/30/2022] Open
Abstract
Imaging the amygdala with functional MRI is confounded by multiple averse factors, notably signal dropouts due to magnetic inhomogeneity and low signal-to-noise ratio, making it difficult to obtain consistent activation patterns in this region. However, even when consistent signal changes are identified, they are likely to be due to nearby vessels, most notably the basal vein of rosenthal (BVR). Using an accelerated fMRI sequence with a high temporal resolution (TR = 333 ms) combined with susceptibility-weighted imaging, we show how signal changes in the amygdala region can be related to a venous origin. This finding is confirmed here in both a conventional fMRI dataset (TR = 2000 ms) as well as in information of meta-analyses, implying that “amygdala activations” reported in typical fMRI studies are likely confounded by signals originating in the BVR rather than in the amygdala itself, thus raising concerns about many conclusions on the functioning of the amygdala that rely on fMRI evidence alone.
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Affiliation(s)
- Roland N Boubela
- 1] Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria [2] MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
| | - Klaudius Kalcher
- 1] Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria [2] MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Huf
- 1] Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria [2] MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
| | - Eva-Maria Seidel
- Social, Cognitive and Affective Neuroscience Unit, Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Birgit Derntl
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Lukas Pezawas
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Christian Našel
- Department of Radiology, Tulln Hospital, Karl Landsteiner University of Health Sciences, Tulln, Austria
| | - Ewald Moser
- 1] Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria [2] MR Centre of Excellence, Medical University of Vienna, Vienna, Austria [3] Brain Behaviour Laboratory, Department of Psychiatry, University of Pennsylvania Medical Center, Philadelphia, PA, USA
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14
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Dong L, Luo C, Cao W, Zhang R, Gong J, Gong D, Yao D. Spatiotemporal consistency of local neural activities: A new imaging measure for functional MRI data. J Magn Reson Imaging 2014; 42:729-36. [DOI: 10.1002/jmri.24831] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 12/05/2014] [Indexed: 11/08/2022] Open
Affiliation(s)
- Li Dong
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
| | - Weifang Cao
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
| | - Rui Zhang
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
| | - Jinnan Gong
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
| | - Diankun Gong
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education; School of Life Science and Technology, University of Electronic Science and Technology of China; Chengdu P.R. China
- Center for Information in BioMedicine; University of Electronic Science and Technology of China; Chengdu P.R. China
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15
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Huf W, Kalcher K, Boubela RN, Rath G, Vecsei A, Filzmoser P, Moser E. On the generalizability of resting-state fMRI machine learning classifiers. Front Hum Neurosci 2014; 8:502. [PMID: 25120443 PMCID: PMC4114329 DOI: 10.3389/fnhum.2014.00502] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 06/20/2014] [Indexed: 12/13/2022] Open
Abstract
Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.
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Affiliation(s)
- Wolfgang Huf
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria ; MR Centre of Excellence, Medical University of Vienna Vienna, Austria ; Department of Statistics and Probability Theory, Vienna University of Technology Vienna, Austria
| | - Klaudius Kalcher
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria ; MR Centre of Excellence, Medical University of Vienna Vienna, Austria ; Department of Statistics and Probability Theory, Vienna University of Technology Vienna, Austria
| | - Roland N Boubela
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria ; MR Centre of Excellence, Medical University of Vienna Vienna, Austria ; Department of Statistics and Probability Theory, Vienna University of Technology Vienna, Austria
| | - Georg Rath
- MR Centre of Excellence, Medical University of Vienna Vienna, Austria ; Department of Radiodiagnostics and Nuclear Medicine, Medical University of Vienna Vienna, Austria
| | - Andreas Vecsei
- Department of Pediatrics and Adolescent Medicine, St. Anna Children's Hospital, Medical University of Vienna Vienna, Austria
| | - Peter Filzmoser
- Department of Statistics and Probability Theory, Vienna University of Technology Vienna, Austria
| | - Ewald Moser
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria ; MR Centre of Excellence, Medical University of Vienna Vienna, Austria ; Department of Psychiatry, University of Pennsylvania Medical Center Philadelphia, PA, USA
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16
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Kalcher K, Boubela RN, Huf W, Bartova L, Kronnerwetter C, Derntl B, Pezawas L, Filzmoser P, Nasel C, Moser E. The spectral diversity of resting-state fluctuations in the human brain. PLoS One 2014; 9:e93375. [PMID: 24728207 PMCID: PMC3984093 DOI: 10.1371/journal.pone.0093375] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 03/03/2014] [Indexed: 01/15/2023] Open
Abstract
In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms) and compared to 20 resting-state datasets from standard, high-TR (1800 ms) EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1–0.25 Hz; 0.25–0.75 Hz; 0.75–1.4 Hz) was computed for both the low-TR and (for the two lower-frequency ranges) the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low-TR EPI sequences for resting-state and potentially also task-related fMRI experiments.
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Affiliation(s)
- Klaudius Kalcher
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
- Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria
| | - Roland N. Boubela
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
- Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria
| | - Wolfgang Huf
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
- Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria
| | - Lucie Bartova
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Claudia Kronnerwetter
- MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
- Department of Radiodiagnostics and Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Birgit Derntl
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Lukas Pezawas
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Peter Filzmoser
- Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria
| | | | - Ewald Moser
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- MR Centre of Excellence, Medical University of Vienna, Vienna, Austria
- Brain Behaviour Laboratory, Department of Psychiatry, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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