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Luo Q, Sun K, Dan G, Zhou XJ. Fast 3D fMRI acquisition with high spatial resolutions over a reduced FOV. Magn Reson Med 2024. [PMID: 38888135 DOI: 10.1002/mrm.30191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/29/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE To develop and demonstrate a fast 3D fMRI acquisition technique with high spatial resolution over a reduced FOV, named k-t 3D reduced FOV imaging (3D-rFOVI). METHODS Based on 3D gradient-echo EPI, k-t 3D-rFOVI used a 2D RF pulse to reduce the FOV in the in-plane phase-encoding direction, boosting spatial resolution without increasing echo train length. For image acceleration, full sampling was applied in the central k-space region along the through-slab direction (kz) for all time frames, while randomized undersampling was used in outer kz regions at different time frames. Images were acquired at 3T and reconstructed using a method based on partial separability. fMRI detection sensitivity of k-t 3D-rFOVI was quantitively analyzed with simulation data. Human visual fMRI experiments were performed to evaluate k-t 3D-rFOVI and compare it with a commercial multiband EPI sequence. RESULTS The simulation data showed that k-t 3D-rFOVI can detect 100% of fMRI activations with an acceleration factor (R) of 2 and ˜80% with R = 6. In the human fMRI data acquired with 1.5-mm spatial resolution and 800-ms volume TR (TRvol), k-t 3D-rFOVI with R = 4 detected 46% more activated voxels in the visual cortex than the multiband EPI. Additional fMRI experiments showed that k-t 3D-rFOVI can achieve TRvol of 480 ms with R = 6, while reliably detecting visual activation. CONCLUSIONS k-t 3D-rFOVI can simultaneously achieve a high spatial resolution (1.5-mm isotropically) and short TRvol (480-ms) at 3T. It offers a robust acquisition technique for fast fMRI studies over a focused brain volume.
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Affiliation(s)
- Qingfei Luo
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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Jia K, Goebel R, Kourtzi Z. Ultra-High Field Imaging of Human Visual Cognition. Annu Rev Vis Sci 2023; 9:479-500. [PMID: 37137282 DOI: 10.1146/annurev-vision-111022-123830] [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] [Indexed: 05/05/2023]
Abstract
Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition.
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Affiliation(s)
- Ke Jia
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom;
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom;
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Dowdle LT, Vizioli L, Moeller S, Akçakaya M, Olman C, Ghose G, Yacoub E, Uğurbil K. Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies. Neuroimage 2023; 270:119949. [PMID: 36804422 DOI: 10.1016/j.neuroimage.2023.119949] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 01/27/2023] [Accepted: 02/15/2023] [Indexed: 02/19/2023] Open
Abstract
As the neuroimaging field moves towards detecting smaller effects at higher spatial resolutions, and faster sampling rates, there is increased attention given to the deleterious contribution of unstructured, thermal noise. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, for suppressing thermal noise using datasets acquired with various field strengths, voxel sizes, sampling rates, and task designs. Following minimal preprocessing, statistical activation (t-values) of NORDIC processed data was compared to the results obtained with alternative denoising methods. Additionally, we examined the consistency of the estimates of task responses at the single-voxel, single run level, using a finite impulse response (FIR) model. To examine the potential impact on effective image resolution, the overall smoothness of the data processed with different methods was estimated. Finally, to determine if NORDIC alters or removes temporal information important for modeling responses, we employed an exhaustive leave-p-out cross validation approach, using FIR task responses to predict held out timeseries, quantified using R2. After NORDIC, the t-values are increased, an improvement comparable to what could be achieved by 1.5 voxels smoothing, and task events are clearly visible and have less cross-run error. These advantages are achieved with smoothness estimates increasing by less than 4%, while 1.5 voxel smoothing is associated with increases of over 140%. Cross-validated R2s based on the FIR models show that NORDIC is not measurably distorting the temporal structure of the data under this approach and is the best predictor of non-denoised time courses. The results demonstrate that analyzing 1 run of data after NORDIC produces results equivalent to using 2 to 3 original runs and that NORDIC performs equally well across a diverse array of functional imaging protocols. Significance Statement: For functional neuroimaging, the increasing availability of higher field strengths and ever higher spatiotemporal resolutions has led to concomitant increase in concerns about the deleterious effects of thermal noise. Historically this noise source was suppressed using methods that reduce spatial precision such as image blurring or averaging over a large number of trials or sessions, which necessitates large data collection efforts. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, which suppresses thermal noise. Across datasets varying in field strength, voxel sizes, sampling rates, and task designs, NORDIC produces substantial gains in data quality. Both conventional t-statistics derived from general linear models and coefficients of determination for predicting unseen data are improved. These gains match or even exceed those associated with 1 voxel Full Width Half Max image smoothing, however, even such small amounts of smoothing are associated with a 52% reduction in estimates of spatial precision, whereas the measurable difference in spatial precision is less than 4% following NORDIC.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States.
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
| | - Steen Moeller
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
| | - Mehmet Akçakaya
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Cheryl Olman
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
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Li Q, Steeg GV, Yu S, Malo J. Functional Connectome of the Human Brain with Total Correlation. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1725. [PMID: 36554129 PMCID: PMC9777567 DOI: 10.3390/e24121725] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/14/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pairwise measures such as correlation or mutual information. In this work, we build on this idea to infer a large-scale (whole-brain) connectivity network based on Total Correlation and show the possibility of using this kind of network as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of Total Correlation and clustering results are trustable compared to ground truth values. Second, the inferred large-scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies, but, interestingly, can estimate additional relations beyond pairwise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.
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Affiliation(s)
- Qiang Li
- Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Shujian Yu
- Machine Learning Group, UiT—The Arctic University of Norway, 9037 Tromsø, Norway
| | - Jesus Malo
- Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain
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