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Sabot D, Baumann O. Neuroimaging Correlates of Cognitive Behavioral Therapy for Insomnia (CBT-I): A Systematic Literature Review. J Cogn Psychother 2023; 37:82-101. [PMID: 36787999 DOI: 10.1891/jcpsy-d-21-00006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cognitive behavioral therapy for insomnia (CBT-I) is the gold-standard non-pharmacological treatment for insomnia, a complex disorder that comprises psychological, behavioral, and physiological components. This systematic literature review aimed to evaluate a growing body of exploratory studies that have examined CBT-I treatment effects using neuroimaging assessment. Nine studies met current review selection criteria, of which six studies compared insomnia groups with good sleepers, waitlist, and/or control groups. CBT-I administration varied in treatment length and duration across the studies, as did neuroimaging assessment, which included task-based and resting-state functional magnetic resonance imaging (fMRI), and structural magnetic resonance imaging (MRI). Functional connectivity abnormalities were observed in participants, including reduced engagement in task-related brain regions and apparent difficulties in regulating default mode brain areas that appeared to reverse following CBT-I treatment. Taken together, the neuroimaging results complement behavioral measures of treatment efficacy, indicating support for the effectiveness of CBT-I treatment in the recovery of brain function and structure.
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Affiliation(s)
- Debbie Sabot
- School of Psychology, Bond University, Robina QLD 4226 Australia
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2
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Hakonen M, Ikäheimonen A, Hultèn A, Kauttonen J, Koskinen M, Lin FH, Lowe A, Sams M, Jääskeläinen IP. Processing of an Audiobook in the Human Brain Is Shaped by Cultural Family Background. Brain Sci 2022; 12:brainsci12050649. [PMID: 35625035 PMCID: PMC9139798 DOI: 10.3390/brainsci12050649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/10/2022] [Accepted: 05/13/2022] [Indexed: 11/16/2022] Open
Abstract
Perception of the same narrative can vary between individuals depending on a listener’s previous experiences. We studied whether and how cultural family background may shape the processing of an audiobook in the human brain. During functional magnetic resonance imaging (fMRI), 48 healthy volunteers from two different cultural family backgrounds listened to an audiobook depicting the intercultural social life of young adults with the respective cultural backgrounds. Shared cultural family background increased inter-subject correlation of hemodynamic activity in the left-hemispheric Heschl’s gyrus, insula, superior temporal gyrus, lingual gyrus and middle temporal gyrus, in the right-hemispheric lateral occipital and posterior cingulate cortices as well as in the bilateral middle temporal gyrus, middle occipital gyrus and precuneus. Thus, cultural family background is reflected in multiple areas of speech processing in the brain and may also modulate visual imagery. After neuroimaging, the participants listened to the narrative again and, after each passage, produced a list of words that had been on their minds when they heard the audiobook during neuroimaging. Cultural family background was reflected as semantic differences in these word lists as quantified by a word2vec-generated semantic model. Our findings may depict enhanced mutual understanding between persons who share similar cultural family backgrounds.
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Affiliation(s)
- Maria Hakonen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 00076 Espoo, Finland; (A.I.); (A.L.); (M.S.); (I.P.J.)
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Faculty of Sport and Health Sciences, University of Jyväskylä, 40014 Jyväskylä, Finland
- Advanced Magnetic Imaging Centre, School of Science, Aalto University, 00076 Espoo, Finland
- Correspondence:
| | - Arsi Ikäheimonen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 00076 Espoo, Finland; (A.I.); (A.L.); (M.S.); (I.P.J.)
| | - Annika Hultèn
- Imaging Language, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 00076 Espoo, Finland;
| | - Janne Kauttonen
- Digital Business, Haaga-Helia University of Applied Sciences, 00520 Helsinki, Finland;
| | - Miika Koskinen
- Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
| | - Fa-Hsuan Lin
- Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada;
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Anastasia Lowe
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 00076 Espoo, Finland; (A.I.); (A.L.); (M.S.); (I.P.J.)
| | - Mikko Sams
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 00076 Espoo, Finland; (A.I.); (A.L.); (M.S.); (I.P.J.)
- MAGICS Infrastructure, Aalto Studios, Aalto University, 02150 Espoo, Finland
| | - Iiro P. Jääskeläinen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 00076 Espoo, Finland; (A.I.); (A.L.); (M.S.); (I.P.J.)
- International Social Neuroscience Laboratory, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, 101000 Moscow, Russia
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3
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Vizioli L, Yacoub E, Lewis LD. How pushing the spatiotemporal resolution of fMRI can advance neuroscience. Prog Neurobiol 2021; 207:102184. [PMID: 34767874 DOI: 10.1016/j.pneurobio.2021.102184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States.
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA United States; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA United States
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4
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Riemenschneider B, Akin B, LeVan P, Hennig J. Trading off spatio-temporal properties in 3D high-speed fMRI using interleaved stack-of-spirals trajectories. Magn Reson Med 2021; 86:777-790. [PMID: 33749021 DOI: 10.1002/mrm.28742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 01/27/2021] [Accepted: 01/31/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE Highly undersampled acquisitions have been proposed to push the limits of temporal resolution in functional MRI. This contribution is aimed at identifying parameter sets that let the user trade-off between ultra-high temporal resolution and spatial signal quality by varying the sampling densities. The proposed method maintains the synergies of a temporal resolution that enables direct filtering of physiological artifacts for highest statistical power, and 3D read-outs with optimal use of encoding capabilities of multi-coil arrays for efficient sampling and high signal-to-noise ratio (SNR). METHODS One- to four-shot interleaved spherical stack-of-spiral trajectories with repetition times from 96 to 352 ms at a nominal resolution of 3 mm using different sampling densities were compared for image quality and temporal SNR (tSNR). The one- and three-shot trajectories were employed in a resting state study for functional characterization. RESULTS Compared to a previously described single-shot trajectory, denser sampled trajectories of the same type are shown to be less prone to blurring and off-resonance vulnerability that appear in addition to the variable density artifacts of the point spread function. While the multi-shot trajectories lead to a decrease in tSNR efficiency, the high SNR due to the 3D read-out, combined with notable increases in image quality, leads to superior overall results of the three-shot interleaved stack of spirals. A resting state analysis of 15 subjects shows significantly improved functional sensitivity in areas of high off-resonance gradients. CONCLUSION Mild variable-density sampling leads to excellent tSNR behavior and no increased off-resonance vulnerability, and is suggested unless maximum temporal resolution is sought.
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Affiliation(s)
- Bruno Riemenschneider
- Department of Radiology, Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Burak Akin
- Department of Radiology, Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Barghoorn A, Riemenschneider B, Hennig J, LeVan P. Improving the sensitivity of spin-echo fMRI at 3T by highly accelerated acquisitions. Magn Reson Med 2021; 86:245-257. [PMID: 33624352 DOI: 10.1002/mrm.28715] [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: 01/22/2020] [Revised: 01/14/2021] [Accepted: 01/14/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE Spin-echo (SE) functional MRI (fMRI) can be highly advantageous compared to gradient-echo (GE) fMRI with respect to magnetic field-inhomogeneity artifacts. However, at 3T, the majority of blood oxygenation level-dependent (BOLD) fMRI experiments are performed using T 2 ∗ -weighted GE sequences because of their superior sensitivity compared to SE-fMRI. The presented SE implementation of a highly accelerated GE pulse sequence therefore aims to improve the sensitivity of SE-fMRI while profiting from a reduction of susceptibility-induced signal dropout. METHODS Spin-echo MR encephalography (SE-MREG) is compared with the more conventionally used spin-echo echo-planar imaging (SE-EPI) and spin-echo simultaneous multislice (SE-SMS) at 3T in terms of capability to detect neuronal activations and resting-state functional connectivity. For activation analysis, healthy subjects underwent consecutive SE-MREG (pulse repetition time [TR] = 0.25 seconds), SE-SMS (TR = 1.3 seconds), and SE-EPI (TR = 4.4 seconds) scans in pseudorandomized order applied to a visual block design paradigm for generation of t-statistics maps. For the investigation of functional connectivity, additional resting-state data were acquired for 5 minutes and a seed-based correlation analysis using Stanford's FIND (Functional Imaging in Neuropsychiatric Disorders) atlas was performed. RESULTS The increased sampling rate of SE-MREG relative to SE-SMS and SE-EPI improves the sensitivity to detect BOLD activation by 33% and 54%, respectively, and increases the capability to extract resting-state networks. Compared with a brain region that is not affected by magnetic field inhomogeneities, SE-MREG shows 2.5 times higher relative signal strength than GE-MREG in mesial temporal structures. CONCLUSION SE-MREG offers a viable possibility for whole-brain fMRI with consideration of brain regions that are affected by strong susceptibility-induced magnetic field gradients.
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Affiliation(s)
- Antonia Barghoorn
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bruno Riemenschneider
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Departments of Radiology and Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
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6
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Baumann O, Mattingley JB. Extrahippocampal contributions to spatial navigation in humans: A review of the neuroimaging evidence. Hippocampus 2021; 31:640-657. [DOI: 10.1002/hipo.23313] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/18/2021] [Accepted: 01/24/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Oliver Baumann
- School of Psychology Bond University Robina Queensland Australia
| | - Jason B. Mattingley
- Queensland Brain Institute The University of Queensland Brisbane Queensland Australia
- School of Psychology The University of Queensland Brisbane Queensland Australia
- Canadian Institute for Advanced Research (CIFAR) Toronto Ontario Canada
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7
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Hennig J, Kiviniemi V, Riemenschneider B, Barghoorn A, Akin B, Wang F, LeVan P. 15 Years MR-encephalography. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:85-108. [PMID: 33079327 PMCID: PMC7910380 DOI: 10.1007/s10334-020-00891-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/02/2020] [Accepted: 09/29/2020] [Indexed: 02/07/2023]
Abstract
Objective This review article gives an account of the development of the MR-encephalography (MREG) method, which started as a mere ‘Gedankenexperiment’ in 2005 and gradually developed into a method for ultrafast measurement of physiological activities in the brain. After going through different approaches covering k-space with radial, rosette, and concentric shell trajectories we have settled on a stack-of-spiral trajectory, which allows full brain coverage with (nominal) 3 mm isotropic resolution in 100 ms. The very high acceleration factor is facilitated by the near-isotropic k-space coverage, which allows high acceleration in all three spatial dimensions. Methods The methodological section covers the basic sequence design as well as recent advances in image reconstruction including the targeted reconstruction, which allows real-time feedback applications, and—most recently—the time-domain principal component reconstruction (tPCR), which applies a principal component analysis of the acquired time domain data as a sparsifying transformation to improve reconstruction speed as well as quality. Applications Although the BOLD-response is rather slow, the high speed acquisition of MREG allows separation of BOLD-effects from cardiac and breathing related pulsatility. The increased sensitivity enables direct detection of the dynamic variability of resting state networks as well as localization of single interictal events in epilepsy patients. A separate and highly intriguing application is aimed at the investigation of the glymphatic system by assessment of the spatiotemporal patterns of cardiac and breathing related pulsatility. Discussion MREG has been developed to push the speed limits of fMRI. Compared to multiband-EPI this allows considerably faster acquisition at the cost of reduced image quality and spatial resolution.
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Affiliation(s)
- Juergen Hennig
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany. .,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Bruno Riemenschneider
- Department of Radiology, Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, USA
| | - Antonia Barghoorn
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Burak Akin
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fei Wang
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Departments of Radiology and Paediatrics, Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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8
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Wang F, Hennig J, LeVan P. Time-domain principal component reconstruction (tPCR): A more efficient and stable iterative reconstruction framework for non-Cartesian functional MRI. Magn Reson Med 2020; 84:1321-1335. [PMID: 32068309 DOI: 10.1002/mrm.28208] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 12/27/2019] [Accepted: 01/19/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To improve the reconstruction efficiency (i.e., computational load) and stability of iterative reconstruction for non-Cartesian fMRI when using high undersampling rates and/or in the presence of strong off-resonance effects. THEORY AND METHODS The magnetic resonance encephalography (MREG) sequence with 3D non-Cartesian trajectory and 0.1s repetition time (TR) was applied to acquire fMRI datasets. Different from a conventional time-point-by-time-point sequential reconstruction (SR), the proposed time-domain principal component reconstruction (tPCR) performs three steps: (1) decomposing the k-t-space fMRI datasets into time-domain principal component space using singular value decomposition, (2) reconstructing each principal component with redistributed computation power according to their weights, and (3) combining the reconstructed principal components back to image-t-space. The comparison of reconstruction accuracy was performed by simulation experiments and then verified in real fMRI data. RESULTS The simulation experiments showed that the proposed tPCR was able to significantly reduce reconstruction errors, and subsequent functional activation errors, relative to SR at identical computational cost. Alternatively, at fixed reconstruction accuracy, computation time was greatly reduced. The improved performance was particularly obvious for L1-norm nonlinear reconstructions relative to L2-norm linear reconstructions and robust to different regularization strength, undersampling rates, and off-resonance effects intensity. By examining activation maps, tPCR was also found to give similar improvements in real fMRI experiments. CONCLUSION The proposed proof-of-concept tPCR framework could improve (1) the reconstruction efficiency of iterative reconstruction, and (2) the reconstruction stability especially for nonlinear reconstructions. As a practical consideration, the improved reconstruction speed promotes the application of highly undersampled non-Cartesian fast fMRI.
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Affiliation(s)
- Fei Wang
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Departments of Radiology and Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
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Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
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10
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Wei Y, Leerssen J, Wassing R, Stoffers D, Perrier J, Van Someren EJW. Reduced dynamic functional connectivity between salience and executive brain networks in insomnia disorder. J Sleep Res 2019; 29:e12953. [PMID: 32164035 PMCID: PMC7154624 DOI: 10.1111/jsr.12953] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 11/06/2019] [Accepted: 11/06/2019] [Indexed: 11/28/2022]
Abstract
Research into insomnia disorder has pointed to large-scale brain network dysfunctions. Dynamic functional connectivity is instrumental to cognitive functions but has not been investigated in insomnia disorder. This study assessed between-network functional connectivity strength and variability in patients with insomnia disorder as compared with matched controls without sleep complaints. Twelve-minute resting-state functional magnetic resonance images and T1-weighed images were acquired in 65 people diagnosed with insomnia disorder (21-69 years, 48 female) and 65 matched controls without sleep complaints (22-70 years, 42 female). Pairwise correlations between the activity time series of 14 resting-state networks and temporal variability of the correlations were compared between cases and controls. After false discovery rate correction for multiple comparisons, people with insomnia disorder and controls did not differ significantly in terms of mean between-network functional connectivity strength; people with insomnia disorder did, however, show less functional connectivity variability between the anterior salience network and the left executive-control network. The finding suggests less flexible interactions between the networks during the resting state in people with insomnia disorder.
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Affiliation(s)
- Yishul Wei
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Jeanne Leerssen
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Rick Wassing
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.,Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research, University of Sydney, Sydney, Australia
| | | | - Joy Perrier
- UNICAEN, INSERM, COMETE, Normandie University, Caen, France
| | - Eus J W Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.,Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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11
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Applications of dynamic functional connectivity to pain and its modulation. Pain Rep 2019; 4:e752. [PMID: 31579848 DOI: 10.1097/pr9.0000000000000752] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/21/2019] [Accepted: 04/07/2019] [Indexed: 12/30/2022] Open
Abstract
Since early work attempting to characterize the brain's role in pain, it has been clear that pain is not generated by a specific brain region, but rather by coordinated activity across a network of brain regions, the "neuromatrix." The advent of noninvasive whole-brain neuroimaging, including functional magnetic resonance imaging, has provided insight on coordinated activity in the pain neuromatrix and how correlations in activity between regions, referred to as "functional connectivity," contribute to pain and its modulation. Initial functional connectivity investigations assumed interregion connectivity remained stable over time, and measured variability across individuals. However, new dynamic functional connectivity (dFC) methods allow researchers to measure how connectivity changes over time within individuals, permitting insights on the dynamic reorganization of the pain neuromatrix in humans. We review how dFC methods have been applied to pain, and insights afforded on how brain connectivity varies across time, either spontaneously or as a function of psychological states, cognitive demands, or the external environment. Specifically, we review psychophysiological interaction, dynamic causal modeling, state-based dynamic community structure, and sliding-window analyses and their use in human functional neuroimaging of acute pain, chronic pain, and pain modulation. We also discuss promising uses of dFC analyses for the investigation of chronic pain conditions and predicting pain treatment efficacy and the relationship between state- and trait-based pain measures. Throughout this review, we provide information regarding the advantages and shortcomings of each approach, and highlight potential future applications of these methodologies for better understanding the brain processes associated with pain.
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12
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Cao J, Lu KH, Oleson ST, Phillips RJ, Jaffey D, Hendren CL, Powley TL, Liu Z. Gastric stimulation drives fast BOLD responses of neural origin. Neuroimage 2019; 197:200-211. [PMID: 31029867 DOI: 10.1016/j.neuroimage.2019.04.064] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 04/02/2019] [Accepted: 04/23/2019] [Indexed: 11/27/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is commonly thought to be too slow to capture any neural dynamics faster than 0.1 Hz. However, recent findings demonstrate the feasibility of detecting fMRI activity at higher frequencies beyond 0.2 Hz. The origin, reliability, and generalizability of fast fMRI responses are still under debate and await confirmation through animal experiments with fMRI and invasive electrophysiology. Here, we acquired single-echo and multi-echo fMRI, as well as local field potentials, from anesthetized rat brains given gastric electrical stimulation modulated at 0.2, 0.4 and 0.8 Hz. Such gastric stimuli could drive widespread fMRI responses at corresponding frequencies from the somatosensory and cingulate cortices. Such fast fMRI responses were linearly dependent on echo times and thus indicative of blood oxygenation level dependent nature (BOLD). Local field potentials recorded during the same gastric stimuli revealed transient and phase-locked broadband neural responses, preceding the fMRI responses by as short as 0.5 s. Taken together, these results suggest that gastric stimulation can drive widespread and rapid fMRI responses of BOLD and neural origin, lending support to the feasibility of using fMRI to detect rapid changes in neural activity up to 0.8 Hz under visceral stimulation.
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Affiliation(s)
- Jiayue Cao
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States; Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, IN, United States
| | - Kun-Han Lu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States; Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, IN, United States
| | - Steven T Oleson
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Robert J Phillips
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States; Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, IN, United States
| | - Deborah Jaffey
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
| | - Christina L Hendren
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Terry L Powley
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States; Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States; Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, IN, United States
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States; Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States; Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, IN, United States.
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13
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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] [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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14
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Ofer I, LeRose C, Mast H, LeVan P, Metternich B, Egger K, Urbach H, Schulze-Bonhage A, Wagner K. Association between seizure freedom and default mode network reorganization in patients with unilateral temporal lobe epilepsy. Epilepsy Behav 2019; 90:238-246. [PMID: 30538081 DOI: 10.1016/j.yebeh.2018.10.025] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 10/19/2018] [Accepted: 10/21/2018] [Indexed: 01/10/2023]
Abstract
RATIONALE The spontaneous synchronized activity and intrinsic organization of the Default Mode Network (DMN) has been found to be altered because of epileptic activity of temporal lobe origin. Thus, the aim of the present study was to compare DMN's topological properties in patients with seizure-free (SF) and not seizure-free (NSF) temporal lobe epilepsy (TLE). METHODS Functional connectivity within the DMN was determined from an 8-minute resting state functional magnetic resonance imaging (fMRI) in 27 patients with TLE (12 SF, 15 NSF) and 15 healthy controls (HC). The DMN regions of interest were extracted according to the automated anatomical labeling (AAL) atlas. Network properties were assessed using standard graph-theoretical measures. RESULTS Analyses revealed, irrespectively of focus lateralization, borderline significance for longer paths (p = 0.049) and in trend reduced local efficiency within the DMN of SF when compared with that of NSF (p = 0.075). The SF and NSF patients did not differ in global network topology from HC (p > 0.05). At the nodal network level, the degree of central hubs was significantly reduced in SF when compared with that in NSF (0.002 ≤ p ≤ 0.080) and HC (0.001 ≤ p ≤ 0.066) while simultaneously, right anterior superior temporal gyrus revealed significantly higher degree in SF than in NSF (p = 0.005) and HC (p = 0.016). CONCLUSION Seizure freedom seems to be associated with hub redistributions that may underlie longer paths and (in trend) reduced local efficiency of the network. An associated slower system response might reduce the probability of a rapid spread of epileptic discharges over the whole network and may help to prevent hypersynchronous neuronal activity in brain networks that may result in epileptic seizures.
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Affiliation(s)
- Isabell Ofer
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany.
| | | | - Hansjoerg Mast
- Faculty of Medicine, University of Freiburg, Germany; Department of Neuroradiology, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Faculty of Medicine, University of Freiburg, Germany; Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Germany
| | - Birgitta Metternich
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Karl Egger
- Faculty of Medicine, University of Freiburg, Germany; Department of Neuroradiology, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Horst Urbach
- Faculty of Medicine, University of Freiburg, Germany; Department of Neuroradiology, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
| | - Kathrin Wagner
- Epilepsy Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; Freiburg Brain Imaging Center, Medical Center - Faculty of Medicine, University of Freiburg, Germany
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15
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Lennartz C, Schiefer J, Rotter S, Hennig J, LeVan P. Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra. Front Neurosci 2018; 12:287. [PMID: 29867310 PMCID: PMC5951985 DOI: 10.3389/fnins.2018.00287] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 04/11/2018] [Indexed: 01/01/2023] Open
Abstract
In functional magnetic resonance imaging (fMRI), functional connectivity is conventionally characterized by correlations between fMRI time series, which are intrinsically undirected measures of connectivity. Yet, some information about the directionality of network connections can nevertheless be extracted from the matrix of pairwise temporal correlations between all considered time series, when expressed in the frequency-domain as a cross-spectral density matrix. Using a sparsity prior, it then becomes possible to determine a unique directed network topology that best explains the observed undirected correlations, without having to rely on temporal precedence relationships that may not be valid in fMRI. Applying this method on simulated data with 100 nodes yielded excellent retrieval of the underlying directed networks under a wide variety of conditions. Importantly, the method did not depend on temporal precedence to establish directionality, thus reducing susceptibility to hemodynamic variability. The computational efficiency of the algorithm was sufficient to enable whole-brain estimations, thus circumventing the problem of missing nodes that otherwise occurs in partial-brain analyses. Applying the method to real resting-state fMRI data acquired with a high temporal resolution, the inferred networks showed good consistency with structural connectivity obtained from diffusion tractography in the same subjects. Interestingly, this agreement could also be seen when considering high-frequency rather than low-frequency connectivity (average correlation: r = 0.26 for f < 0.3 Hz, r = 0.43 for 0.3 < f < 5 Hz). Moreover, this concordance was significantly better (p < 0.05) than for networks obtained with conventional functional connectivity based on correlations (average correlation r = 0.18). The presented methodology thus appears to be well-suited for fMRI, particularly given its lack of explicit dependence on temporal lag structure, and is readily applicable to whole-brain effective connectivity estimation.
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Affiliation(s)
- Carolin Lennartz
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Jonathan Schiefer
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Stefan Rotter
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
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16
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From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals. PLoS Comput Biol 2018; 14:e1006056. [PMID: 29579045 PMCID: PMC5886625 DOI: 10.1371/journal.pcbi.1006056] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 04/05/2018] [Accepted: 02/26/2018] [Indexed: 11/28/2022] Open
Abstract
Knowing brain connectivity is of great importance both in basic research and for clinical applications. We are proposing a method to infer directed connectivity from zero-lag covariances of neuronal activity recorded at multiple sites. This allows us to identify causal relations that are reflected in neuronal population activity. To derive our strategy, we assume a generic linear model of interacting continuous variables, the components of which represent the activity of local neuronal populations. The suggested method for inferring connectivity from recorded signals exploits the fact that the covariance matrix derived from the observed activity contains information about the existence, the direction and the sign of connections. Assuming a sparsely coupled network, we disambiguate the underlying causal structure via L1-minimization, which is known to prefer sparse solutions. In general, this method is suited to infer effective connectivity from resting state data of various types. We show that our method is applicable over a broad range of structural parameters regarding network size and connection probability of the network. We also explored parameters affecting its activity dynamics, like the eigenvalue spectrum. Also, based on the simulation of suitable Ornstein-Uhlenbeck processes to model BOLD dynamics, we show that with our method it is possible to estimate directed connectivity from zero-lag covariances derived from such signals. In this study, we consider measurement noise and unobserved nodes as additional confounding factors. Furthermore, we investigate the amount of data required for a reliable estimate. Additionally, we apply the proposed method on full-brain resting-state fast fMRI datasets. The resulting network exhibits a tendency for close-by areas being connected as well as inter-hemispheric connections between corresponding areas. In addition, we found that a surprisingly large fraction of more than one third of all identified connections were of inhibitory nature. Changes in brain connectivity are considered an important biomarker for certain brain diseases. This directly raises the question of accessibility of connectivity from measured brain signals. Here we show how directed effective connectivity can be inferred from continuous brain signals, like fMRI. The main idea is to extract the connectivity from the inverse zero-lag covariance matrix of the measured signals. This is done using L1-minimization via gradient descent algorithm on the manifold of unitary matrices. This ensures that the resulting network always fits the same covariance structure as the measured data, assuming a canonical linear model. Applying the estimation method on noise-free covariance matrices shows that the method works nicely on sparsely coupled networks with more than 40 nodes, provided network interaction is strong enough. Applying the estimation on simulated Ornstein-Uhlenbeck processes supposed to model BOLD signals demonstrates robustness against observation noise and unobserved nodes. In general, the proposed method can be applied to time-resolved covariance matrices in the frequency domain (cross-spectral densities), leading to frequency-resolved networks. We are able to demonstrate that our method leads to reliable results, if the sampled signals are long enough.
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