1
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Mueller SG. 7T MP2RAGE for cortical myelin segmentation: Impact of aging. PLoS One 2024; 19:e0299670. [PMID: 38626149 PMCID: PMC11020839 DOI: 10.1371/journal.pone.0299670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/14/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND Myelin and iron are major contributors to the cortical MR signal. The aim of this study was to investigate 1. Can MP2RAGE-derived contrasts at 7T in combination with k-means clustering be used to distinguish between heavily and sparsely myelinated layers in cortical gray matter (GM)? 2. Does this approach provide meaningful biological information? METHODS The following contrasts were generated from the 7T MP2RAGE images from 45 healthy controls (age: 19-75, f/m = 23/22) from the ATAG data repository: 1. T1 weighted image (UNI). 2. T1 relaxation image (T1map). 3. INVC/T1map ratio (RATIO). K-means clustering identified 6 clusters/tissue maps (csf, csf/gm-transition, wm, wm/gm transition, heavily myelinated cortical GM (dGM), sparsely myelinated cortical GM (sGM)). These tissue maps were then processed with SPM/DARTEL (volume-based analyses) and Freesurfer (surface-based analyses) and dGM and sGM volume/thickness of young adults (n = 27, 19-27 years) compared to those of older adults (n = 18, 42-75 years) at p<0.001 uncorrected. RESULTS The resulting maps showed good agreement with histological maps in the literature. Volume- and surface analyses found age-related dGM loss/thinning in the mid-posterior cingulate and parahippocampal/entorhinal gyrus and age-related sGM losses in lateral, mesial and orbitofrontal frontal, insular cortex and superior temporal gyrus. CONCLUSION The MP2RAGE derived UNI, T1map and RATIO contrasts can be used to identify dGM and sGM. Considering the close relationship between cortical myelo- and cytoarchitecture, the findings reported here indicate that this new technique might provide new insights into the nature of cortical GM loss in physiological and pathological conditions.
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Affiliation(s)
- Susanne G. Mueller
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA, United States of America
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2
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Rosenblum EW, Williams EM, Champion SN, Frosch MP, Augustinack JC. The prosubiculum in the human hippocampus: A rostrocaudal, feature-driven, and systematic approach. J Comp Neurol 2024; 532:e25604. [PMID: 38477395 PMCID: PMC11060218 DOI: 10.1002/cne.25604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/12/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
The hippocampal subfield prosubiculum (ProS), is a conserved neuroanatomic region in mouse, monkey, and human. This area lies between CA1 and subiculum (Sub) and particularly lacks consensus on its boundaries; reports have varied on the description of its features and location. In this report, we review, refine, and evaluate four cytoarchitectural features that differentiate ProS from its neighboring subfields: (1) small neurons, (2) lightly stained neurons, (3) superficial clustered neurons, and (4) a cell sparse zone. ProS was delineated in all cases (n = 10). ProS was examined for its cytoarchitectonic features and location rostrocaudally, from the anterior head through the body in the hippocampus. The most common feature was small pyramidal neurons, which were intermingled with larger pyramidal neurons in ProS. We quantitatively measured ProS pyramidal neurons, which showed (average, width at pyramidal base = 14.31 µm, n = 400 per subfield). CA1 neurons averaged 15.57 µm and Sub neurons averaged 15.63 µm, both were significantly different than ProS (Kruskal-Wallis test, p < .0001). The other three features observed were lightly stained neurons, clustered neurons, and a cell sparse zone. Taken together, these findings suggest that ProS is an independent subfield, likely with distinct functional contributions to the broader interconnected hippocampal network. Our results suggest that ProS is a cytoarchitecturally varied subfield, both for features and among individuals. This diverse architecture in features and individuals for ProS could explain the long-standing complexity regarding the identification of this subfield.
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Affiliation(s)
- Emma W Rosenblum
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Emily M Williams
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Samantha N Champion
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew P Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jean C Augustinack
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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3
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Faes LK, Lage-Castellanos A, Valente G, Yu Z, Cloos MA, Vizioli L, Moeller S, Yacoub E, De Martino F. Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577070. [PMID: 38328173 PMCID: PMC10849717 DOI: 10.1101/2024.01.24.577070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.
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Affiliation(s)
- Lonike K. Faes
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Agustin Lage-Castellanos
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana City 11600, Cuba
| | - Giancarlo Valente
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
| | - Zidan Yu
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- MRI Research Center, University of Hawaii, United States
| | - Martijn A. Cloos
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, St Lucia 4066, Australia
| | - Luca Vizioli
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Steen Moeller
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Federico De Martino
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, The Netherlands
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
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4
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Deshpande G, Wang Y, Robinson J. Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain. Brain Inform 2022; 9:2. [PMID: 35038072 PMCID: PMC8764001 DOI: 10.1186/s40708-021-00150-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022] Open
Abstract
Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. .,Department of Psychological Sciences, Auburn University, Auburn, AL, USA. .,Alabama Advanced Imaging Consortium, Birmingham, AL, USA. .,Center for Neuroscience, Auburn University, Auburn, AL, USA. .,Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China. .,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India. .,Centre for Brain Research, Indian Institute of Science, Bangalore, India.
| | - Yun Wang
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.,Department of Psychiatry, Columbia University, New York, NY, USA
| | - Jennifer Robinson
- AU MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.,Department of Psychological Sciences, Auburn University, Auburn, AL, USA.,Alabama Advanced Imaging Consortium, Birmingham, AL, USA.,Center for Neuroscience, Auburn University, Auburn, AL, USA
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5
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Lewis JD, Bezgin G, Fonov VS, Collins DL, Evans AC. A sub+cortical fMRI-based surface parcellation. Hum Brain Mapp 2021; 43:616-632. [PMID: 34761459 PMCID: PMC8720195 DOI: 10.1002/hbm.25675] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.
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Affiliation(s)
- John D Lewis
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Gleb Bezgin
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Verdun, Quebec, Canada
| | - Vladimir S Fonov
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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6
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Sanchez Panchuelo RM, Mougin O, Turner R, Francis ST. Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI. Neuroimage 2021; 234:117976. [PMID: 33781969 PMCID: PMC8204273 DOI: 10.1016/j.neuroimage.2021.117976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 02/27/2021] [Accepted: 03/13/2021] [Indexed: 11/12/2022] Open
Abstract
An efficient multi-slice inversion–recovery EPI (MS-IR-EPI) sequence for fast, high spatial resolution, quantitative T1 mapping is presented, using a segmented simultaneous multi-slice acquisition, combined with slice order shifting across multiple acquisitions. The segmented acquisition minimises the effective TE and readout duration compared to a single-shot EPI scheme, reducing geometric distortions to provide high quality T1 maps with a narrow point-spread function. The precision and repeatability of MS-IR-EPI T1 measurements are assessed using both T1-calibrated and T2-calibrated ISMRM/NIST phantom spheres at 3 and 7 T and compared with single slice IR and MP2RAGE methods. Magnetization transfer (MT) effects of the spectrally-selective fat-suppression (FS) pulses required for in vivo imaging are shown to shorten the measured in-vivo T1 values. We model the effect of these fat suppression pulses on T1 measurements and show that the model can remove their MT contribution from the measured T1, thus providing accurate T1 quantification. High spatial resolution T1 maps of the human brain generated with MS-IR-EPI at 7 T are compared with those generated with the widely implemented MP2RAGE sequence. Our MS-IR-EPI sequence provides high SNR per unit time and sharper T1 maps than MP2RAGE, demonstrating the potential for ultra-high resolution T1 mapping and the improved discrimination of functionally relevant cortical areas in the human brain.
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Affiliation(s)
- Rosa M Sanchez Panchuelo
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom; NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, United Kingdom.
| | - Olivier Mougin
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Robert Turner
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom; NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, United Kingdom
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7
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Surface-based analysis increases the specificity of cortical activation patterns and connectivity results. Sci Rep 2020; 10:5737. [PMID: 32235885 PMCID: PMC7109138 DOI: 10.1038/s41598-020-62832-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 03/11/2020] [Indexed: 12/13/2022] Open
Abstract
Spatial smoothing of functional magnetic resonance imaging (fMRI) data can be performed on volumetric images and on the extracted surface of the brain. Smoothing on the unfolded cortex should theoretically improve the ability to separate signals between brain areas that are near together in the folded cortex but are more distant in the unfolded cortex. However, surface-based method approaches (SBA) are currently not utilized as standard procedure in the preprocessing of neuroimaging data. Recent improvements in the quality of cortical surface modeling and improvements in its usability nevertheless advocate this method. In the current study, we evaluated the benefits of an up-to-date surface-based smoothing in comparison to volume-based smoothing. We focused on the effect of signal contamination between different functional systems using the primary motor and primary somatosensory cortex as an example. We were particularly interested in how this signal contamination influences the results of activity and connectivity analyses for these brain regions. We addressed this question by performing fMRI on 19 subjects during a tactile stimulation paradigm and by using simulated BOLD responses. We demonstrated that volume-based smoothing causes contamination of the primary motor cortex by somatosensory cortical responses, leading to false positive motor activation. These false positive motor activations were not found by using surface-based smoothing for reasonable kernel sizes. Accordingly, volume-based smoothing caused an exaggeration of connectivity estimates between these regions. In conclusion, this study showed that surface-based smoothing decreases signal contamination considerably between neighboring functional brain regions and improves the validity of activity and connectivity results.
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8
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Jalbrzikowski M, Liu F, Foran W, Roeder K, Devlin B, Luna B. Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth. Schizophr Bull 2020; 46:395-407. [PMID: 31424081 PMCID: PMC7442350 DOI: 10.1093/schbul/sbz053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Resting-state functional neuroimaging captures large-scale network organization; whether this organization is intact or disrupted during adolescent development across the psychosis spectrum is unresolved. We investigated the integrity of network organization in psychosis spectrum youth and those with first episode psychosis (FEP) from late childhood through adulthood. METHODS We analyzed data from Philadelphia Neurodevelopmental Cohort (PNC; typically developing = 450, psychosis spectrum = 273, 8-22 years), a longitudinal cohort of typically developing youth (LUNA; N = 208, 1-3 visits, 10-25 years), and a sample of FEP (N = 39) and matched controls (N = 34). We extracted individual time series and calculated correlations from brain regions and averaged them for 4 age groups: late childhood, early adolescence, late adolescence, adulthood. Using multiple analytic approaches, we assessed network stability across 4 age groups, compared stability between controls and psychosis spectrum youth, and compared group-level network organization of FEP to controls. We explored whether variability in cognition or clinical symptomatology was related to network organization. RESULTS Network organization was stable across the 4 age groups in the PNC and LUNA typically developing youth and PNC psychosis spectrum youth. Psychosis spectrum and typically developing youth had similar functional network organization during all age ranges. Network organization was intact in PNC youth who met full criteria for psychosis and in FEP. Variability in cognitive functioning or clinical symptomatology was not related to network organization in psychosis spectrum youth or FEP. DISCUSSION These findings provide rigorous evidence supporting intact functional network organization in psychosis risk and psychosis from late childhood through adulthood.
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Affiliation(s)
- Maria Jalbrzikowski
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,To whom correspondence should be addressed; tel: 201-403-5598, e-mail:
| | - Fuchen Liu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | - Kathryn Roeder
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA,Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,Department of Psychology, University of Pittsburgh, Pittsburgh, PA,Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA
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9
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Degryse J, Moerkerke B. A likelihood ratio approach for functional localization in fMRI. J Neurosci Methods 2020; 330:108417. [PMID: 31628960 DOI: 10.1016/j.jneumeth.2019.108417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 08/27/2019] [Accepted: 08/27/2019] [Indexed: 11/17/2022]
Abstract
BACKGROUND To increase power when analyzing fMRI data, researchers often define functional regions of interest (fROIs). It is crucial that this fROI is defined with an optimal balance between both false positives and false negatives to ensure maximal spatial accuracy and to avoid potentially biased results in the main fMRI experiment. Additionally, since the fROI is defined in each subject separately, the used method should attune to the general level of activation of the individual. NEW METHOD We investigate the benefits of the maximized likelihood ratio (mLR) method. This method is based on the likelihood paradigm where likelihood ratios are used to reflect relative statistical evidence in favor of an a priori defined practically relevant alternative hypothesis as compared to the null hypothesis of no activation. RESULTS Through both simulations and real data, we show that the mLR method provides cumulative evidence for voxels that are active with an effect size that is larger than the one a priori defined in the alternative. Furthermore, an optimal balance between Type I and Type II errors is achieved when the alternative is an underestimation of the true effect size. COMPARISON WITH EXISTING METHODS The mLR method is compared with false discovery rate corrected null hypothesis significance testing and regular likelihood ratio testing. It performs as good as or outperformed both methods in both detection of practically relevant voxels and the trade- off between false positives and false negatives. CONCLUSIONS The mLR method provides fROIs that are both spatially accurate and practically relevant.
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Affiliation(s)
- Jasper Degryse
- Department of Data Analysis, Ghent University, H. Dunantlaan 1, 9000 Gent, Belgium.
| | - Beatrijs Moerkerke
- Department of Data Analysis, Ghent University, H. Dunantlaan 1, 9000 Gent, Belgium.
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10
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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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11
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Trampel R, Bazin PL, Pine K, Weiskopf N. In-vivo magnetic resonance imaging (MRI) of laminae in the human cortex. Neuroimage 2019; 197:707-715. [DOI: 10.1016/j.neuroimage.2017.09.037] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 09/13/2017] [Accepted: 09/19/2017] [Indexed: 11/16/2022] Open
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12
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Turner R. Myelin and Modeling: Bootstrapping Cortical Microcircuits. Front Neural Circuits 2019; 13:34. [PMID: 31133821 PMCID: PMC6517540 DOI: 10.3389/fncir.2019.00034] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 04/25/2019] [Indexed: 12/15/2022] Open
Abstract
Histological studies of myelin-stained sectioned cadaver brain and in vivo myelin-weighted magnetic resonance imaging (MRI) show that the cerebral cortex is organized into cortical areas with generally well-defined boundaries, which have consistent internal patterns of myelination. The process of myelination is largely driven by neural experience, in which the axonal passage of action potentials stimulates neighboring oligodendrocytes to perform their task. This bootstrapping process, such that the traffic of action potentials facilitates increased traffic, suggests the hypothesis that the specific pattern of myelination (myeloarchitecture) in each cortical area reveals the principal cortical microcircuits required for the function of that area. If this idea is correct, the observable sequential maturation of specific brain areas can provide evidence for models of the stages of cognitive development.
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Affiliation(s)
- Robert Turner
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Spinoza Centre for Neuroimaging, University of Amsterdam, Amsterdam, Netherlands
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13
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Mulder MJ, Keuken MC, Bazin PL, Alkemade A, Forstmann BU. Size and shape matter: The impact of voxel geometry on the identification of small nuclei. PLoS One 2019; 14:e0215382. [PMID: 30978242 PMCID: PMC6461289 DOI: 10.1371/journal.pone.0215382] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 04/01/2019] [Indexed: 12/28/2022] Open
Abstract
How, and to what extent do size and shape of a voxel measured with magnetic resonance imaging (MRI) affect the ability to visualize small brain nuclei? Despite general consensus that voxel geometry affects volumetric properties of regions of interest, particularly those of small brain nuclei, no quantitative data on the influence of voxel size and shape on labeling accuracy is available. Using simulations, we investigated the selective influence of voxel geometry by reconstructing simulated ellipsoid structures with voxels varying in shape and size. For each reconstructed ellipsoid, we calculated differences in volume and similarity between the labeled volume and the predefined dimensions of the ellipsoid. Probability functions were derived from one or two individual raters and a simulated ground truth for reference. As expected, larger voxels (i.e., coarser resolution) and increasing anisotropy results in increased deviations of both volume and shape measures, which is of particular relevance for small brain structures. Our findings clearly illustrate the anatomical inaccuracies introduced by the application of large and/or anisotropic voxels. To ensure deviations occur within the acceptable range (Dice coefficient scores; DCS > 0.75, corresponding to < 57% volume deviation), the volume of isotropic voxels should not exceed 5% of the total volume of the region of interest. When high accuracy is required (DCS > 0.90, corresponding to a < 19% volume deviation), the volumes of isotropic voxels should not exceed 0.08%, of the total volume. Finally, when large anisotropic factors (>3) are used, and the ellipsoid is orthogonal to the slice axes, having its long axis in the imaging plane, the voxel volume should not exceed 0.005% of the total volume. This allows sufficient compensation of anisotropy effects, in order to reach accuracy in the acceptable range (DCS > 0.75, corresponding to >57% volume deviation).
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Affiliation(s)
- Martijn J Mulder
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands.,Experimental Psychology, Utrecht University, Utrecht, the Netherlands
| | - Max C Keuken
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Pierre-Louis Bazin
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Anneke Alkemade
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Birte U Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
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14
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Aquino KM, Sokoliuk R, Pakenham DO, Sanchez-Panchuelo RM, Hanslmayr S, Mayhew SD, Mullinger KJ, Francis ST. Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher-order cognitive tasks: An inter-sensory task directing attention between visual and somatosensory domains. Hum Brain Mapp 2018; 40:1298-1316. [PMID: 30430706 PMCID: PMC6865556 DOI: 10.1002/hbm.24450] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 10/05/2018] [Accepted: 10/12/2018] [Indexed: 01/20/2023] Open
Abstract
Functional MRI at ultra‐high field (UHF, ≥7 T) provides significant increases in BOLD contrast‐to‐noise ratio (CNR) compared with conventional field strength (3 T), and has been exploited for reduced field‐of‐view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher‐order cognitive tasks leads to a number of challenges, in particular how to perform robust group‐level statistical analyses. This study addresses these challenges using an inter‐sensory cognitive task which modulates top‐down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalisation and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal‐lateral‐prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second‐level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multivariate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.
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Affiliation(s)
- Kevin M Aquino
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.,Brain and Mental Health Laboratory, Monash University, Clayton, Australia.,School of Physics, University of Sydney, Sydney, Australia
| | - Rodika Sokoliuk
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Daisie O Pakenham
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Rosa Maria Sanchez-Panchuelo
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Simon Hanslmayr
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stephen D Mayhew
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.,Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
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15
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Lohmann G, Stelzer J, Lacosse E, Kumar VJ, Mueller K, Kuehn E, Grodd W, Scheffler K. LISA improves statistical analysis for fMRI. Nat Commun 2018; 9:4014. [PMID: 30275541 PMCID: PMC6167367 DOI: 10.1038/s41467-018-06304-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 08/21/2018] [Indexed: 01/11/2023] Open
Abstract
One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).
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Affiliation(s)
- Gabriele Lohmann
- Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, 72076, Tübingen, Germany.
- Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany.
| | - Johannes Stelzer
- Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, 72076, Tübingen, Germany
- Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany
| | - Eric Lacosse
- Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Max-Planck-Institute for Intelligent Systems, Max-Planck-Ring 4, 72076, Tübingen, Germany
| | - Vinod J Kumar
- Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany
| | - Karsten Mueller
- Methods & Development Group Nuclear Magnetic Resonance, Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
| | - Esther Kuehn
- German Center for Neurodegenerative Diseases (DZNE), Leipziger Strasse 44, 39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), 30120, Magdeburg, Germany
- Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
| | - Wolfgang Grodd
- Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany
| | - Klaus Scheffler
- Department of Biomedical Magnetic Resonance Imaging, University Hospital Tübingen, Hoppe-Seyler-Strasse 3, 72076, Tübingen, Germany
- Magnetic Resonance Centre, Max-Planck-Institute for Biological Cybernetics, Max-Planck-Ring 11, 72076, Tübingen, Germany
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16
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Keuken MC, Isaacs BR, Trampel R, van der Zwaag W, Forstmann BU. Visualizing the Human Subcortex Using Ultra-high Field Magnetic Resonance Imaging. Brain Topogr 2018; 31:513-545. [PMID: 29497874 PMCID: PMC5999196 DOI: 10.1007/s10548-018-0638-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 01/28/2018] [Indexed: 12/15/2022]
Abstract
With the recent increased availability of ultra-high field (UHF) magnetic resonance imaging (MRI), substantial progress has been made in visualizing the human brain, which can now be done in extraordinary detail. This review provides an extensive overview of the use of UHF MRI in visualizing the human subcortex for both healthy and patient populations. The high inter-subject variability in size and location of subcortical structures limits the usability of atlases in the midbrain. Fortunately, the combined results of this review indicate that a large number of subcortical areas can be visualized in individual space using UHF MRI. Current limitations and potential solutions of UHF MRI for visualizing the subcortex are also discussed.
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Affiliation(s)
- M C Keuken
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Postbus 15926, 1001NK, Amsterdam, The Netherlands.
- Cognitive Psychology Unit, Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.
| | - B R Isaacs
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Postbus 15926, 1001NK, Amsterdam, The Netherlands
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - R Trampel
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - B U Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Postbus 15926, 1001NK, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
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17
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The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc Natl Acad Sci U S A 2018; 115:E6356-E6365. [PMID: 29925602 DOI: 10.1073/pnas.1801582115] [Citation(s) in RCA: 174] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Localizing human brain functions is a long-standing goal in systems neuroscience. Toward this goal, neuroimaging studies have traditionally used volume-based smoothing, registered data to volume-based standard spaces, and reported results relative to volume-based parcellations. A novel 360-area surface-based cortical parcellation was recently generated using multimodal data from the Human Connectome Project, and a volume-based version of this parcellation has frequently been requested for use with traditional volume-based analyses. However, given the major methodological differences between traditional volumetric and Human Connectome Project-style processing, the utility and interpretability of such an altered parcellation must first be established. By starting from automatically generated individual-subject parcellations and processing them with different methodological approaches, we show that traditional processing steps, especially volume-based smoothing and registration, substantially degrade cortical area localization compared with surface-based approaches. We also show that surface-based registration using features closely tied to cortical areas, rather than to folding patterns alone, improves the alignment of areas, and that the benefits of high-resolution acquisitions are largely unexploited by traditional volume-based methods. Quantitatively, we show that the most common version of the traditional approach has spatial localization that is only 35% as good as the best surface-based method as assessed using two objective measures (peak areal probabilities and "captured area fraction" for maximum probability maps). Finally, we demonstrate that substantial challenges exist when attempting to accurately represent volume-based group analysis results on the surface, which has important implications for the interpretability of studies, both past and future, that use these volume-based methods.
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18
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Torrisi S, Chen G, Glen D, Bandettini PA, Baker CI, Reynolds R, Yen-Ting Liu J, Leshin J, Balderston N, Grillon C, Ernst M. Statistical power comparisons at 3T and 7T with a GO / NOGO task. Neuroimage 2018; 175:100-110. [PMID: 29621615 DOI: 10.1016/j.neuroimage.2018.03.071] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 03/13/2018] [Accepted: 03/29/2018] [Indexed: 10/17/2022] Open
Abstract
The field of cognitive neuroscience is weighing evidence about whether to move from standard field strength to ultra-high field (UHF). The present study contributes to the evidence by comparing a cognitive neuroscience paradigm at 3 Tesla (3T) and 7 Tesla (7T). The goal was to test and demonstrate the practical effects of field strength on a standard GO/NOGO task using accessible preprocessing and analysis tools. Two independent matched healthy samples (N = 31 each) were analyzed at 3T and 7T. Results show gains at 7T in statistical strength, the detection of smaller effects and group-level power. With an increased availability of UHF scanners, these gains may be exploited by cognitive neuroscientists and other neuroimaging researchers to develop more efficient or comprehensive experimental designs and, given the same sample size, achieve greater statistical power at 7T.
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Affiliation(s)
- Salvatore Torrisi
- Section on Neurobiology of Fear and Anxiety, NIMH, Bethesda MD, United States.
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, Bethesda MD, United States
| | - Daniel Glen
- Scientific and Statistical Computing Core, NIMH, Bethesda MD, United States
| | | | - Chris I Baker
- Section on Learning and Plasticity, NIMH, Bethesda MD, United States
| | - Richard Reynolds
- Scientific and Statistical Computing Core, NIMH, Bethesda MD, United States
| | | | - Joseph Leshin
- Section on Neurobiology of Fear and Anxiety, NIMH, Bethesda MD, United States
| | - Nicholas Balderston
- Section on Neurobiology of Fear and Anxiety, NIMH, Bethesda MD, United States
| | - Christian Grillon
- Section on Neurobiology of Fear and Anxiety, NIMH, Bethesda MD, United States
| | - Monique Ernst
- Section on Neurobiology of Fear and Anxiety, NIMH, Bethesda MD, United States
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19
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Tillman RM, Stockbridge MD, Nacewicz BM, Torrisi S, Fox AS, Smith JF, Shackman AJ. Intrinsic functional connectivity of the central extended amygdala. Hum Brain Mapp 2018; 39:1291-1312. [PMID: 29235190 PMCID: PMC5807241 DOI: 10.1002/hbm.23917] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 12/03/2017] [Accepted: 12/04/2017] [Indexed: 12/16/2022] Open
Abstract
The central extended amygdala (EAc)-including the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce)-plays a critical role in triggering fear and anxiety and is implicated in the development of a range of debilitating neuropsychiatric disorders. Although it is widely believed that these disorders reflect the coordinated activity of distributed neural circuits, the functional architecture of the EAc network and the degree to which the BST and the Ce show distinct patterns of functional connectivity is unclear. Here, we used a novel combination of imaging approaches to trace the connectivity of the BST and the Ce in 130 healthy, racially diverse, community-dwelling adults. Multiband imaging, high-precision registration techniques, and spatially unsmoothed data maximized anatomical specificity. Using newly developed seed regions, whole-brain regression analyses revealed robust functional connectivity between the BST and Ce via the sublenticular extended amygdala, the ribbon of subcortical gray matter encompassing the ventral amygdalofugal pathway. Both regions displayed coupling with the ventromedial prefrontal cortex (vmPFC), midcingulate cortex (MCC), insula, and anterior hippocampus. The BST showed stronger connectivity with the thalamus, striatum, periaqueductal gray, and several prefrontal territories. The only regions showing stronger functional connectivity with the Ce were neighboring regions of the dorsal amygdala, amygdalohippocampal area, and anterior hippocampus. These observations provide a baseline against which to compare a range of special populations, inform our understanding of the role of the EAc in normal and pathological fear and anxiety, and showcase image registration techniques that are likely to be useful for researchers working with "deidentified" neuroimaging data.
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Affiliation(s)
| | - Melissa D. Stockbridge
- Department of Hearing and Speech SciencesUniversity of MarylandCollege ParkMaryland20742
| | - Brendon M. Nacewicz
- Department of PsychiatryUniversity of Wisconsin—Madison, 6001 Research Park BoulevardMadisonWisconsin53719
| | - Salvatore Torrisi
- Section on the Neurobiology of Fear and AnxietyNational Institute of Mental HealthBethesdaMaryland20892
| | - Andrew S. Fox
- Department of PsychologyUniversity of CaliforniaDavisCalifornia95616
- California National Primate Research CenterUniversity of CaliforniaDavisCalifornia95616
| | - Jason F. Smith
- Department of PsychologyUniversity of MarylandCollege ParkMaryland20742
| | - Alexander J. Shackman
- Department of PsychologyUniversity of MarylandCollege ParkMaryland20742
- Neuroscience and Cognitive Science ProgramUniversity of MarylandCollege ParkMaryland20742
- Maryland Neuroimaging CenterUniversity of MarylandCollege ParkMaryland20742
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20
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Tittgemeyer M, Rigoux L, Knösche TR. Cortical parcellation based on structural connectivity: A case for generative models. Neuroimage 2018; 173:592-603. [PMID: 29407457 DOI: 10.1016/j.neuroimage.2018.01.077] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 01/26/2018] [Accepted: 01/29/2018] [Indexed: 12/14/2022] Open
Abstract
One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function -this has led to the concept of the 'connectome'. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements. Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas' functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation. In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding. In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness -the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions.
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Affiliation(s)
| | - Lionel Rigoux
- Max-Planck-Institute for Metabolism Research, Cologne, Germany
| | - Thomas R Knösche
- Max-Planck-Institute for Cognitive and Brain Sciences, Leipzig, Germany
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21
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de Hollander G, Keuken MC, van der Zwaag W, Forstmann BU, Trampel R. Comparing functional MRI protocols for small, iron-rich basal ganglia nuclei such as the subthalamic nucleus at 7 T and 3 T. Hum Brain Mapp 2017; 38:3226-3248. [PMID: 28345164 PMCID: PMC6867009 DOI: 10.1002/hbm.23586] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 03/09/2017] [Accepted: 03/15/2017] [Indexed: 11/05/2022] Open
Abstract
The basal ganglia (BG) form a network of subcortical nuclei. Functional magnetic resonance imaging (fMRI) in the BG could provide insight in its functioning and the underlying mechanisms of Deep Brain Stimulation (DBS). However, fMRI of the BG with high specificity is challenging, because the nuclei are small and variable in their anatomical location. High resolution fMRI at field strengths of 7 Tesla (T) could help resolve these challenges to some extent. A set of MR protocols was developed for functional imaging of the BG nuclei at 3 T and 7 T. The protocols were validated using a stop-signal reaction task (Logan et al. []: J Exp Psychol: Human Percept Perform 10:276-291). Compared with sub-millimeter 7 T fMRI protocols aimed at cortex, a reduction of echo time and spatial resolution was strictly necessary to obtain robust Blood Oxygen Level Dependent (BOLD) sensitivity in the BG. An fMRI protocol at 3 T with identical resolution to the 7 T showed no robust BOLD sensitivity in any of the BG nuclei. The results suggest that the subthalamic nucleus, as well as the substantia nigra, red nucleus, and the internal and external parts of the globus pallidus show increased activation in failed stop trials compared with successful stop and go trials. Hum Brain Mapp 38:3226-3248, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Gilles de Hollander
- University of Amsterdam, Amsterdam Brain & Cognition CenterAmsterdamThe Netherlands
- Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and SciencesAmsterdamThe Netherlands
| | - Max C. Keuken
- University of Amsterdam, Amsterdam Brain & Cognition CenterAmsterdamThe Netherlands
- Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and SciencesAmsterdamThe Netherlands
| | | | - Birte U. Forstmann
- University of Amsterdam, Amsterdam Brain & Cognition CenterAmsterdamThe Netherlands
- Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and SciencesAmsterdamThe Netherlands
- Department of PsychologyUniversiteit LeidenLeidenThe Netherlands
| | - Robert Trampel
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
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22
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Degryse J, Seurinck R, Durnez J, Gonzalez-Castillo J, Bandettini PA, Moerkerke B. Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI. Front Neurosci 2017; 11:222. [PMID: 28484367 PMCID: PMC5399022 DOI: 10.3389/fnins.2017.00222] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 04/04/2017] [Indexed: 01/06/2023] Open
Abstract
In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null.
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Affiliation(s)
- Jasper Degryse
- Department of Data-Analysis, Ghent UniversityGent, Belgium
| | - Ruth Seurinck
- Department of Data-Analysis, Ghent UniversityGent, Belgium
| | - Joke Durnez
- Department of Psychology, Stanford UniversityPalo Alto, CA, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain Cognition, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain Cognition, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA
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23
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Turner R, De Haan D. Bridging the gap between system and cell: The role of ultra-high field MRI in human neuroscience. PROGRESS IN BRAIN RESEARCH 2017; 233:179-220. [DOI: 10.1016/bs.pbr.2017.05.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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24
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25
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Turner R. Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science. Philos Trans R Soc Lond B Biol Sci 2016; 371:20150349. [PMID: 27574303 PMCID: PMC5003851 DOI: 10.1098/rstb.2015.0349] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2016] [Indexed: 11/29/2022] Open
Abstract
When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.
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Affiliation(s)
- Robert Turner
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany
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26
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Webb AG, Van de Moortele PF. The technological future of 7 T MRI hardware. NMR IN BIOMEDICINE 2016; 29:1305-1315. [PMID: 25974894 DOI: 10.1002/nbm.3315] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 02/07/2015] [Accepted: 04/07/2015] [Indexed: 06/04/2023]
Abstract
In this article we present our projections of future hardware developments on 7 T human MRI systems. These include compact cryogen-light magnets, improved gradient performance, integrated RF-receive and direct current shimming coil arrays, new RF technology with adaptive impedance matching, patient-specific specific absorption rate estimation and monitoring, and increased integration of physiological monitoring systems. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- A G Webb
- C. J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - P F Van de Moortele
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
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27
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van der Zwaag W, Schäfer A, Marques JP, Turner R, Trampel R. Recent applications of UHF-MRI in the study of human brain function and structure: a review. NMR IN BIOMEDICINE 2016; 29:1274-1288. [PMID: 25762497 DOI: 10.1002/nbm.3275] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/19/2014] [Accepted: 01/22/2015] [Indexed: 06/04/2023]
Abstract
The increased availability of ultra-high-field (UHF) MRI has led to its application in a wide range of neuroimaging studies, which are showing promise in transforming fundamental approaches to human neuroscience. This review presents recent work on structural and functional brain imaging, at 7 T and higher field strengths. After a short outline of the effects of high field strength on MR images, the rapidly expanding literature on UHF applications of blood-oxygenation-level-dependent-based functional MRI is reviewed. Structural imaging is then discussed, divided into sections on imaging weighted by relaxation time, including quantitative relaxation time mapping, phase imaging and quantitative susceptibility mapping, angiography, diffusion-weighted imaging, and finally magnetization-transfer imaging. The final section discusses studies using the high spatial resolution available at UHF to identify explicit links between structure and function. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Wietske van der Zwaag
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Switzerland
| | - Andreas Schäfer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - José P Marques
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Robert Turner
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Spinoza Centre, University of Amsterdam, The Netherlands
- SPMMRC, School of Physics and Astronomy, University of Nottingham, UK
| | - Robert Trampel
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Glasser MF, Smith SM, Marcus DS, Andersson J, Auerbach EJ, Behrens TEJ, Coalson TS, Harms MP, Jenkinson M, Moeller S, Robinson EC, Sotiropoulos SN, Xu J, Yacoub E, Ugurbil K, Van Essen DC. The Human Connectome Project's neuroimaging approach. Nat Neurosci 2016; 19:1175-87. [PMID: 27571196 PMCID: PMC6172654 DOI: 10.1038/nn.4361] [Citation(s) in RCA: 615] [Impact Index Per Article: 76.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 07/18/2016] [Indexed: 12/20/2022]
Abstract
Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.
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Affiliation(s)
- Matthew F. Glasser
- Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA
| | - Stephen M. Smith
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Daniel S. Marcus
- Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA
| | - Jesper Andersson
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Edward J. Auerbach
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Timothy E. J. Behrens
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Timothy S. Coalson
- Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA
| | - Michael P. Harms
- Department of Psychiatry, Washington University Medical School, Saint Louis, MO
| | - Mark Jenkinson
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Steen Moeller
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Emma C. Robinson
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Stamatios N. Sotiropoulos
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Junqian Xu
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - David C. Van Essen
- Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA
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Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain. PLoS One 2016; 11:e0158185. [PMID: 27341204 PMCID: PMC4920409 DOI: 10.1371/journal.pone.0158185] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 06/10/2016] [Indexed: 12/22/2022] Open
Abstract
The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.
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A map of the human neocortex showing the estimated overall myelin content of the individual architectonic areas based on the studies of Adolf Hopf. Brain Struct Funct 2016; 222:465-480. [PMID: 27138385 PMCID: PMC5225164 DOI: 10.1007/s00429-016-1228-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 04/21/2016] [Indexed: 12/01/2022]
Abstract
During the period extending from 1910 to 1970, Oscar and Cécile Vogt and their numerous collaborators published a large number of myeloarchitectonic studies on the cortex of the various lobes of the human cerebrum. In a previous publication [Nieuwenhuys et al (Brain Struct Funct 220:2551–2573, 2015; Erratum in Brain Struct Funct 220: 3753–3755, 2015)], we used the data provided by the Vogt–Vogt school for the composition of a myeloarchitectonic map of the entire human neocortex. Because these data were derived from many different brains, a standard brain had to be introduced to which all data available could be transferred. As such the Colin 27 structural scan, aligned to the MNI305 template was selected. The resultant map includes 180 myeloarchitectonic areas, 64 frontal, 30 parietal, 6 insular, 17 occipital and 63 temporal. Here we present a supplementary map in which the overall density of the myelinated fibers in the individual architectonic areas is indicated, based on a meta-analysis of data provided by Adolf Hopf, a prominent collaborator of the Vogts. This map shows that the primary sensory and motor regions are densely myelinated and that, in general, myelination decreases stepwise with the distance from these primary regions. The map also reveals the presence of a number of heavily myelinated formations, situated beyond the primary sensory and motor domains, each consisting of two or more myeloarchitectonic areas. These formations were provisionally designated as the orbitofrontal, intraparietal, posterolateral temporal, and basal temporal dark clusters. Recently published MRI-based in vivo myelin content mappings show, with regard to the primary sensory and motor regions, a striking concordance with our map. As regards the heavily myelinated clusters shown by our map, scrutiny of the current literature revealed that correlates of all of these clusters have been identified in in vivo structural MRI studies and appear to correspond either entirely or largely to known cytoarchitectonic entities. Moreover, functional neuroimaging studies indicate that all of these clusters are involved in vision-related cognitive functions.
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Tardif CL, Gauthier CJ, Steele CJ, Bazin PL, Schäfer A, Schaefer A, Turner R, Villringer A. Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity. Neuroimage 2016; 131:55-72. [DOI: 10.1016/j.neuroimage.2015.08.047] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 08/18/2015] [Accepted: 08/20/2015] [Indexed: 12/13/2022] Open
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Ely BA, Xu J, Goodman WK, Lapidus KA, Gabbay V, Stern ER. Resting-state functional connectivity of the human habenula in healthy individuals: Associations with subclinical depression. Hum Brain Mapp 2016; 37:2369-84. [PMID: 26991474 DOI: 10.1002/hbm.23179] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Revised: 02/23/2016] [Accepted: 02/29/2016] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION The habenula (Hb) is postulated to play a critical role in reward and aversion processing across species, including humans, and has been increasingly implicated in depression. However, technical constraints have limited in vivo investigation of the human Hb, and its function remains poorly characterized. We sought to overcome these challenges by examining the whole-brain resting-state functional connectivity of the Hb and its possible relationship to depressive symptomatology using the high-resolution WU-Minn Human Connectome Project (HCP) dataset. METHODS Anatomical and resting-state functional MRI data from 50 healthy subjects with low or high subclinical depression scores (n = 25 each) were analyzed. Using novel semi-automated segmentation and optimization techniques, we generated individual-specific Hb seeds and calculated whole-brain functional connectivity for the entire cohort and the contrast of high vs. low depression groups. RESULTS In the entire cohort, the Hb exhibited significant connectivity with key brainstem structures (i.e., ventral tegmental area, substantia nigra, pons) as well as the anterior and posterior cingulate cortices, precuneus, thalamus, and sensorimotor cortex. Multiple regions showed differential Hb connectivity based on subclinical depression scores, including the amygdala, insula, and prefrontal, mid-cingulate, and entorhinal cortices. CONCLUSIONS Hb connectivity findings converged on areas associated with salience processing, sensorimotor systems, and the default mode network. We also detected substantial Hb-brainstem connectivity, consistent with prior histological and animal research. High and low subclinical depression groups exhibited differences in Hb connectivity with multiple regions previously linked to depression, suggesting the relationship between these structures as a potential target for future research and treatment. Hum Brain Mapp 37:2369-2384, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Benjamin A Ely
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Junqian Xu
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York.,Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, Translational and Molecular Imaging Institute, New York, New York.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Wayne K Goodman
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kyle A Lapidus
- Department of Psychiatry, Stony Brook University, Stony Brook, New York
| | - Vilma Gabbay
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.,Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Emily R Stern
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
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Lorio S, Kherif F, Ruef A, Melie-Garcia L, Frackowiak R, Ashburner J, Helms G, Lutti A, Draganski B. Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. Hum Brain Mapp 2016; 37:1801-15. [PMID: 26876452 PMCID: PMC4855623 DOI: 10.1002/hbm.23137] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 01/18/2016] [Accepted: 01/26/2016] [Indexed: 01/04/2023] Open
Abstract
The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R1 (=1/T1), R2*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Sara Lorio
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - Ferath Kherif
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - Anne Ruef
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - Lester Melie-Garcia
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - Richard Frackowiak
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, London, United Kingdom
| | - Gunther Helms
- Department of Clinical Sciences, Lund University, Medical Radiation Physics, Lund, Sweden
| | - Antoine Lutti
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - Bodgan Draganski
- LREN - Department of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland.,Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Open Science CBS Neuroimaging Repository: Sharing ultra-high-field MR images of the brain. Neuroimage 2016; 124:1143-1148. [DOI: 10.1016/j.neuroimage.2015.08.042] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Revised: 07/21/2015] [Accepted: 08/15/2015] [Indexed: 01/03/2023] Open
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Tardif CL, Schäfer A, Waehnert M, Dinse J, Turner R, Bazin PL. Multi-contrast multi-scale surface registration for improved alignment of cortical areas. Neuroimage 2015; 111:107-22. [DOI: 10.1016/j.neuroimage.2015.02.005] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 01/19/2015] [Accepted: 02/02/2015] [Indexed: 11/30/2022] Open
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