Real-Time fMRI Brain Mapping in Animals

Sangcheon Choi; Kengo Takahashi; Yuanyuan Jiang; Sascha K&#246;hler; Hang Zeng; Qi Wang; Yan Ma; Xin Yu

doi:10.3791/61463

JoVE Journal > Neuroscience

Neuroscience

Real-Time fMRI Brain Mapping in Animals

Published: September 24, 2020

doi:

10.3791/61463

Sangcheon Choi^1,2, Kengo Takahashi^1,2, Yuanyuan Jiang^1,3, Sascha Köhler⁴, Hang Zeng^1,2, Qi Wang^1,2, Yan Ma^1,2, Xin Yu^1,3

¹Max Planck Institute for Biological Cybernetics, ²Graduate Training Centre of Neuroscience, ³MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School,Massachusetts General Hospital, ⁴Bruker BioSpin

Summary

Animal brain functional mapping can benefit from the real-time functional magnetic resonance imaging (fMRI) experimental set-up. Using the latest software implemented in the animal MRI system, we established a real-time monitoring platform for small animal fMRI.

Abstract

Dynamic fMRI responses vary largely according to the physiological conditions of animals either under anesthesia or in awake states. We developed a real-time fMRI platform to guide experimenters to monitor fMRI responses instantaneously during acquisition, which can be used to modify the physiology of animals to achieve desired hemodynamic responses in animal brains. The real-time fMRI set-up is based on a 14.1T preclinical MRI system, enabling the real-time mapping of dynamic fMRI responses in the primary forepaw somatosensory cortex (FP-S1) of anesthetized rats. Instead of a retrospective analysis to investigate confounding sources leading to the variability of fMRI signals, the real-time fMRI platform provides a more effective scheme to identify dynamic fMRI responses using customized macro-functions and a common neuroimage analysis software in the MRI system. Also, it provides immediate troubleshooting feasibility and a real-time biofeedback stimulation paradigm for brain functional studies in animals.

Introduction

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive method to measure the hemodynamic responses¹^,²^,³^,⁴^,⁵^,⁶^,⁷^,⁸^,⁹, e.g., the blood-oxygen-level-dependent (BOLD), cerebral blood volume and flow signal, associated with neural activity in the brain. In animal studies, hemodynamic signals can be affected by anesthesia¹⁰, the stress level of awake animals¹¹, as well as the potential non-physiological artifacts, e.g., cardiac pulsation and respiratory motions¹²^,¹³^,¹⁴^,¹⁵. Although many post-processing methods have been developed to provide a retrospective analysis of the fMRI signal for the task-related and resting-state functional dynamics and connectivity mapping¹⁶^,¹⁷^,¹⁸^,¹⁹, there are few techniques to provide a real-time brain function mapping solution and instantaneous readouts in the animal brain²⁰ (most of which are mainly used for human brain mapping²¹^,²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷). In particular, this kind of real-time fMRI mapping method is lacking in animal studies. It is necessary to set up an fMRI platform to enable the investigation of real-time brain state-dependent physiological stages and to provides real-time biofeedback stimulation paradigm for animal brain functional studies.

In the present work, we illustrate a real-time fMRI experimental set-up with the customized macro-functions of the MRI console software, demonstrating real-time monitoring of the evoked BOLD-fMRI responses in the primary forepaw somatosensory cortex (FP-S1) of the anesthetized rats. This real-time set-up allows for the visualization of the ongoing brain activation in functional maps, as well as individual time courses in a voxel-wise manner, using the existing neuroimage analysis software, Analysis of Functional NeuroImages (AFNI)²⁸. The preparation of the real-time fMRI experimental set-up for the animal study is described in the protocol. Besides the animal set-up, we provide detailed procedures to set up the visualization and analysis of the real-time fMRI signals using the latest console software in parallel with the image processing scripts. In summary, the proposed real-time fMRI set-up for animal studies is a powerful tool for monitoring the dynamic fMRI signals in the animal brain using the MRI console system.

Protocol

This study was performed in accordance with the German Animal Welfare Act (TierSchG) and Animal Welfare Laboratory Animal Ordinance (TierSchVersV). The experimental protocol described here was reviewed by the ethics commission (§15 TierSchG) and approved by the state authority (Regierungspräsidium, Tübingen, Baden-Württemberg, Germany).

1. Preparing the BOLD-fMRI experimental set-up for small animal study

Turn on the console software to control imaging parameters and acquire MRI data.
NOTE: The proposed real-time fMRI set-up is implemented utilizing macro-functions of the console software (version 6) in parallel with the image processing functions of AFNI.
Find MR sequences (i.e., Position, Localizer, Rapid Acquisition with Relaxation Enhancement (RARE), and 3D echo-planar imaging (EPI) with the workspace explorer, and then drag and append them in the scan list.
NOTE: Position and Localizer sequences are used to identify a region of interest (ROI) in a brain. A RARE sequence is used for an anatomy scan. A 3D EPI sequence is used to measure dynamic BOLD responses.
Place the predefined macro scripts, “Setup_rt3DEPI” and “Feed2AFNI_rt3DEPI” in the macro script path (e.g., “/opt/(PV version)/prog/curdir/(user name)/ParaVision/macros”). Activate the 3D EPI reconstruction options, “Pre Image Series Activities” and “Execute Macro” in the “Data Reconstruction” user interface menu, and then link the predefined macro script, “Setup_rt3DEPI”, before clicking the “Scan” button.
NOTE: The macro scripts are included in the Supplementary files.
Install the AFNI software for the real-time BOLD-fMRI analysis and visualization.

2. Catheterization and ventilation surgery

Set up a ventilator and physiological status monitoring systems such as thermometer, blood pressure and respiration recording as shown in Figure 1. Set a constant frequency of 60 ± 1 breath/min with the ventilator and a temperature of 37 °C using an MR-compatible heating pad with a feedback control set.
Anesthetize an adult male Sprague-Dawley rat (300-600 g) in a chamber with 5% isoflurane for induction and deliver 2-2.5% isoflurane for surgery from a vaporizer. Check the depth of anesthesia by pinching the hindpaw and confirming the lack of a withdrawal response.
Intubate the animal with a 14 G plastic cannula for ventilation (60 ± 1 breath/min with a mixture of 70% air and 30% oxygen). Adjust end-tidal carbon dioxide (CO₂) to be in the range of 25 ± 5 mmHg²⁹.
NOTE: The intubation is critical for maintaining proper CO₂ levels through fMRI experiments.
Place the animal in a supine position on a surgery table and shave a thigh with an electric razor. And then, make an incision on the shaved skin with surgical scissors.
NOTE: The length of the incision is around 1-2 cm in a longitudinal direction.
Find a femoral artery and vein under the incised region for catheterization and separate the individual femoral artery and vein from the surrounding tissues.
Fasten one side of the separated femoral artery with a surgical suture and hold the other side with micro bulldog forceps. Then, make a small incision between the tied regions on the femoral artery.
Insert a catheter into the femoral artery through the small incision and tie the catheter and the artery together with surgical sutures. Monitor the arterial blood pressure constantly with the physiological monitoring system to be in the range of 80-120 mmHg and measure the arterial blood gas regularly to maintain pO₂ of minimum 90 mmHg and pCO₂ of 30-45 mmHg during scanning.
NOTE: This catheterization is critical for monitoring the arterial blood pressure during fMRI experiments.
Fasten both ends of the femoral vein with silk braided surgical sutures. Then, make a small incision between the tied regions on the femoral vein. Use forceps to perform the suturing.
NOTE: The size of the suture is around 1-2 cm.
Insert a catheter into the femoral vein. Tie the catheter and the vein together with surgical sutures.
NOTE: This catheterization is critical for administrating alpha-chloralose through the vein and adjusting the anesthetic levels during fMRI experiments. If the animal is not well anesthetized, it will start to breathe spontaneously. In this case, more alpha-chloralose must be administrated to avoid respiratory motion artifacts.
Suture the surgical incision on the shaved skin. Once the surgical procedures are completed, keep the animal anesthetized by infusing a bolus of alpha-chloralose with the dosage of ~80 mg/kg through the catheter connected to the femoral vein and stop isoflurane administration at the same time.

3. Placing the animal inside the MRI scanner

Transfer the anesthetized animal to the MRI scanner as soon as 2.10 step is done and secure it on a custom-made cradle.
Insert a real-time feedback rectal thermometer on the animal to monitor the animal’s temperature. Place a heating pad under the animal’s torso to control the temperature. Maintain the body temperature at 37.0 ± 0.5 °C during MRI scans.
Deliver alpha-chloralose with ~25 mg/kg/h solution in a mixture of pancuronium (~2 mg/kg/h), a muscle relaxer, continuously while keeping the animal anesthetized and reducing motion artifacts in fMRI images. Monitor the blood pressure and respiration by adjusting the amount of drug and the rate of ventilation according to the physiological status.
Administer ophthalmic ointment on the eyes of the animal to prevent dryness during fMRI experiments. Fix the animal’s head safely with two ear bars to avoid head motion artifacts.
Fix a transceiver surface coil on the head. Tune and match the coil to the Larmor frequency (e.g., 599 MHz on 14.1 T) on the head before MRI measurements.
NOTE: Here, 22 mm diameter coil is used to cover the whole brain of a rat.
Insert a pair of needle electrodes into the skin of the forepaw between digits 1 and 4 and fix them with surgical tape. And then, confirm that the stimulation works properly after connecting a stimulation input cable to these electrodes³⁰.
Insert the animal into the MRI bore and place it at the iso-center approximately.

4. Measuring anatomical MR images

Click the calibration menu button in the main user interface. Perform the calibrations of the MRI system clicking the following items in the Adjustment Platform user interface (see Help menu in the console software): Find the basic resonance frequency, Calibrate the RF pulse power, Set the optimal receiver gain, Measure the B0 map in the animal for shimming, Run global linear shims based on non-localized free induction decay (FID) integral.
NOTE: This step takes less than 2 min.
Run a Position sequence by clicking the “Scan” button to find the head location of the animal inside the MRI bore. If the head is not located at the iso-center, adjust the head location while moving the cradle back and forth until the head is located at the iso-center.
Run a Localizer sequence by clicking the “Scan” button to identify an ROI in the head. Select Map Shim and define the ROI of the shim volume to cover the whole brain in the localizer image and then, run a high order (e.g., 2^nd or 3^rd order) shimming using the “Shim up to” option to reduce the main magnetic field (B0) inhomogeneities at the ROI.
NOTE: The high order shimming is a critical step to improve the quality of BOLD-fMRI data when EPI sequences are used.
Run a T2-weighted RARE sequence by clicking the “Scan” button to acquire anatomical images covering the whole brain in a coronal view (e.g., the following sequence parameters are used: repetition time (TR) 4000 ms, effective echo time (TE) 36.1 ms, matrix 128 x 128, field of view (FOV) 19.2×19.2 mm², number of slices 32, slice thickness 0.3 mm, RARE factor 8).
NOTE: In the following real-time fMRI visualization step, the anatomical images are used to register 3D EPI images as a template.

5. Real-Time fMRI software set-up and fMRI response visualization

Open a terminal window and go to the real-time AFNI plugin path using the following command:
cd /home/(user name)/rt_afni
NOTE: The AFNI plugin script, “afni_rt” is included in the Supplementary files.
Execute AFNI software with the real-time plugin using the command and options below.
afni -rt
-yestplugouts
-DAFNI_REALTIME_MP_HOST_PORT=localhost:(port number)
-DAFNI_REALTIME_Graph=Realtime
-DAFNI_FIM_IDEAL=(Paradigm)
NOTE: In the first case, the code allows external programs to exchange data with AFNI while in the second case the real-time plugin will attempt to open a TCP socket to the user-defined localhost and port. In the third and fourth cases, the codes will plot the time course of fMRI data in real-time and plot the time course of the user-defined paradigm in the fMRI time course respectively when real-time fMRI data are acquired. For further details, check https://afni.nimh.nih.gov/pub/dist/doc/program_help/README.environment.html.
Monitor upcoming AFNI BRIK files defined by using the command “Dimon” as shown in Figure 2 with the following options:
Dimon -tr (TR of EPI) -nt (NRepetitions of EPI)
-rt -quit
-infile_pattern realtime*.BRIK
-file_type AFNI
NOTE: “Dimon” is a command to monitor the real-time acquisition of AFNI image files using the following options: “-rt” which executes the real-time plugin and “-infile_pattern (data name).BRIK -file_type AFNI” which allows the plugin to read the specific BRIK files and to send them into AFNI for display and formatting. For further details, check https://afni.nimh.nih.gov/pub/dist/doc/program_help/Dimon.html.
Use “pvcmd” command with the following options:
pvcmd -a JMacroManager JMMExecuteMacro -category $USER -macro Feed2AFNI_rt3DEPI
NOTE: This code exists in the macro script, “Setup_rt3DEPI”, to run the background macro script, “Feed2AFNI_rt3DEPI”, right after clicking “Scan” button for EPI acquisition.
Use “exec pvcmd” command with the following options to get EPI acquisition parameters.
exec pvcmd -a ParxServer -r ParamGetValue -psid $ParSpaceId -param (PVM parameters of EPI) -id 10 -args $AcqKey $ParSpaceId $ProcnoPath
Use “exec to3d” command with the following options to convert EPI raw data to AFNI files in real-time in the background macro script, “Feed2AFNI_rt3DEPI”.
exec to3d -omri -xFOV $FOV_X -yFOV $FOV_Y -zFOV $FOV_Z -prefix $LastVolName $ImgFormat$Path2dseq
Make sure that EPI geometrical information is consistent with the anatomy orientation.
NOTE: The “to3d” AFNI command will run automatically with the geometrical information such as the field of view (FOV) and matrix size to convert the fMRI raw data into one AFNI BRIK data whenever each 3D volume data is stored after every single TR as shown in Figure 2. The image orientation can be changed with the geometrical information parameters of “to3d”. For further details, check https://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html.
Turn on an electrical stimulus isolator and perform electrical forepaw stimulation for one evoked fMRI study (e.g., 3Hz, 4s pulse width 300us, 2.5mA) using stimulation blocks.
NOTE: Here, the block-design paradigm consists of 10 pre-stimulation scans, 3 stimulation scans and 12 inter-stimulation scans (15 scans per epoch).
Run a T2*-weighted 3D EPI sequence by clicking the “Scan” button for the BOLD-fMRI study (e.g., the following parameters are used: TR/TE 1500/14 ms, matrix 64 x 64 x 32, FOV 19.2 x 19.2 x 9.6 mm³, and resolution 300 x 300 x 300 µm³).
NOTE: As soon as clicking the “Scan” button, monitoring, and processing raw data will be done by using the predefined macro scripts in real-time. Once one AFNI BRIK dataset is converted, voxel-wise time course graphs for 3D EPI images are displayed in the AFNI software and automatically updated for every single TR.
To overlay the EPI images on top of the anatomical RARE images, convert the RARE images to an AFNI BRIK dataset using the command “to3d” as in step 5.6, then register the EPI images to the anatomical images using the “align_epi_anat.py” AFNI script with the following options:
align_epi_anat.py -anat anatomy_template_al+orig -epi epi.$(epi data number)+orig -epi_base 1 -suffix _volreg -rat_align -cost lpa -epi2anat
NOTE: For further details, check https://afni.nimh.nih.gov/pub/dist/doc/program_help/ align_epi_anat.py.html.
To process functional maps of the BOLD responses, calculate the deconvolution of 3D+time dataset with a specific stimulus time series using the “3dDeconvolve” command with the following options:
3dDeconvolve -input (input file name)+orig. -nfirst 0 -polort 3 -num_stimts 1 -stim_times 1 (stimulation paradigm file name) 'BLOCK(4,1)' -stim_label 1 forepaw -tout -fout -rout
NOTE: Image processing steps such as spatial smoothing or temporal filtering have been incorporated into a customized AFNI data processing script. For further details, check https://afni.nimh.nih.gov/afni/doc/help/3dDeconvolve.html.
To visualize functional maps of the BOLD signals, use an interactive clustering in the AFNI software. Open the “Define Overlay” option and use the “Clusters” function from the AFNI user interface menu.
After the last fMRI scan, take the animal out of the MRI scanner and euthanize it according to the approved protocols.
NOTE: Image processing functions of AFNI and macro-functions in the latest console software were used to process the real-time fMRI data. Detailed information and descriptions of macro-functions can be found from the help menu in the console software. The AFNI software is a freeware, that can be directly downloaded through the NIMH-AFNI website. The related scripts to build the linkage between AFNI and the console system are attached.

Representative Results

Figure 3 and Figure 4 show a representative real-time voxel-wise BOLD-fMRI time course and functional maps with electrical forepaw stimulation (3 Hz, 4 s, pulse width 300 us, 2.5 mA). The fMRI design paradigm comprises 10 pre-stimulation scans, 3 stimulation scans, and 12 inter-stimulation scans with a total of 8 epochs (130 scans). The total scan time is 3 min 15 sec (195 sec). Figure 3 shows the voxel-wise time course (black line) of the contralateral FP-S1 corresponding to the block-design paradigm (red line) in the real-time acquisition format. Figure 4 shows the activated BOLD maps corresponding to the electrical forepaw stimulation. The activated regions are detected and displayed as the colored clusters (red and yellow colors). Experimenters can use the “Clusters” function in the AFNI software to interactively explore clustered volumes and display them as an overlaid color-coded image.

Figure 1: Real-time fMRI experimental set-up for forepaw stimulation. A simplified schematic of the real-time fMRI set-up and the flow (dashed lines) of the control parameters are shown. One computer (left) is used as a console for pulse sequence execution, stimulus isolator control, and data analysis with AFNI. The other computer (right) is used for monitoring physiological information (e.g., blood pressure, respiration, and chest movement, etc.). Please click here to view a larger version of this figure.

Figure 2: Diagram of the data processing during fMRI scanning. A simplified flow chart of data processing with the representative macro and AFNI functions in the real-time fMRI set-up is shown. Before starting fMRI scans, the “Pre Image Series Activities” and “Execute Macro” options are selected among the reconstruction options. The “Setup_rt3DEPI” script is executed by using those options when clicking the “Scan” button. With the “Dimon” command, the real-time AFNI files are monitored and sent into the AFNI plugin to display dynamic BOLD responses when the background macro script, “Feed2AFNI_rt3DEPI” converts the fMRI raw data to the AFNI files. Please click here to view a larger version of this figure.

Figure 3: Real-time voxel-wise fMRI responses. An activated single voxel time course graph (black line) from the primary forepaw somatosensory (FP-S1) cortex is shown during the block-design stimulation paradigm. The repetitive fMRI design paradigm (red line) was defined by the “afni -rt -DAFNI_FIM_IDEAL=(Paradigm)”. The graph demonstrates that clear and stable BOLD responses follow electrical stimulation in real-time. Please click here to view a larger version of this figure.

Figure 4: Functional maps of BOLD responses to electrical stimulation in contralateral FP-S1 regions. The voxel clusters activated in the FP-S1 regions (yellow and red colors) were identified and significantly synchronized with the repetitive stimulation paradigm, overlaid on the T2-weighted anatomical images. Please click here to view a larger version of this figure.

Supplementary Files. Please click here to download these files.

Discussion

Real-time monitoring of the fMRI signal helps experimenters adjust the physiology of animals to optimize functional mapping. Motion artifacts in awake animals, as well as the anesthetic effect, are major factors that mediate the variability of fMRI signals, confounding the biological interpretation of the signal by itself³¹^,³²^,³³^,³⁴^,³⁵^,³⁶^,³⁷^,³⁸. The real-time fMRI platform offers instantaneous information to assist the optimization of scanning parameters and anesthetic administration schemes. Also, real-time brain hemodynamic responses can be used to provide fMRI-based biofeedback controlling signals for novel stimulation paradigms in multi-modal brain functional studies.

A remaining concern about the proposed real-time fMRI set-up is the technical dependency on the vendor-specific console software. In this protocol, the real-time fMRI analysis scripts implement a series of macro-functions using a console software (see Table of Materials) version 6 or higher. The workflow of the MR scan in the previous console software (e.g., PV version 5 or lower) is different from the latest version due to the upgraded user interface and new parameter definition. Using the previous version of the console system (PV version 3), Lu et al. (2008) have shown that the real-time fMRI set-up enabled the monitoring of the drug-induced hemodynamic signal changes in the rat brain to study the cocaine’s effect on the central nervous system²⁰. However, those set-ups cannot be readily applied to the new console software with state-of-the-art electronic devices. In the latest console software, it is a critical step to run the predefined macro scripts and monitor fMRI raw data right after starting to scan by selecting the “Pre Image Series Activities” and “Execute Macro” options of the “Data Reconstruction”.

For further image processing, customized AFNI functions can be readily incorporated into the real-time image processing scripts. In particular, it will be valuable to provide real-time analysis using motion-related traces, e.g., electromyography (EMG) signal for awake animal fMRI³⁸, and incorporate multi-modal dynamic brain signal, e.g., GCaMP-mediated Ca²⁺, to specify whole-brain hemodynamic correlation³⁷. Furthermore, this real-time fMRI set-up can be extended to animal neurofeedback studies to investigate self-regulating brain and behavior similar to previous human studies²⁷.

Disclosures

The authors have nothing to disclose.

Acknowledgements

We thank Dr. D. Chen and Dr. C. Yen for sharing the AFNI script to set up the real-time fMRI for PV 5 and the AFNI team for the software support. This research was supported by NIH Brain Initiative funding (RF1NS113278-01, R01 MH111438-01), and the S10 instrument grant (S10 RR023009-01) to Martinos Center, German Research Foundation (DFG) Yu215/3-1, BMBF 01GQ1702, and the internal funding from Max Planck Society.

Materials

14.1T Bruker MRI system	Bruker BioSpin MRI GmbH	N/A
A365 Stimulus Isolator	World Precision Instruments	N/A
AcqKnowledge Software	Biopac	RRID:SCR_014279, http://www.biopac.com/product/acqknowledge-software/
AFNI	Cox, 1996	RRID:SCR_005927, http://afni.nimh.nih.gov
CO2SMO (ETCO2/SpO2 Monitor), Model 7100	Novametrix Medical Systems Inc	N/A
Isoflurane	CP-Pharma	Cat# 1214
Master-9	A.M.P.I	N/A
Nanoliter Injector	World Precision Instruments	Cat# NANOFIL
Pancuronium Bromide	Inresa Arzneimittel	Cat# 34409.00.00
ParaVision 6	Bruker BioSpin MRI GmbH	RRID:SCR_001964
Phosphate Buffered Saline (PBS)	Gibco	Cat# 10010-023
Rat: Sprague Dawley rat	Charles River Laboratories	Crl:CD(SD)
SAR-830/AP Ventilator	CWE	N/A
α-chloralose	Sigma-Aldrich	Cat# C0128-25G;RRID

References

Ogawa, S., Lee, T. M., Kay, A. R., Tank, D. W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences U.S.A. 87 (24), 9868-9872 (1990).
Belliveau, J. W., et al. Functional mapping of the human visual cortex by magnetic resonance imaging. Science. 254 (5032), 716-719 (1991).
Stehling, M. K., Turner, R., Mansfield, P. Echo-planar imaging: magnetic resonance imaging in a fraction of a second. Science. 254 (5028), 43-50 (1991).
Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., Hyde, J. S. Time course EPI of human brain function during task activation. Magnetic Resonance in Medicine. 25 (2), 390-397 (1992).
Kwong, K. K., et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Science U. S. A. 89 (12), 5675-5679 (1992).
Ogawa, S., et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Science U. S. A. 89 (13), 5951-5955 (1992).
Biswal, B., Yetkin, F. Z., Haughton, V. M., Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine. 34 (4), 537-541 (1995).
Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature. 453 (7197), 869-878 (2008).
Kim, S. G., Ogawa, S. Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. Journal of Cerebral Blood Flow and Metabolism. 32 (7), 1188-1206 (2012).
Peltier, S. J., et al. Functional connectivity changes with concentration of sevoflurane anesthesia. Neuroreport. 16 (3), 285-288 (2005).
Dopfel, D., Zhang, N. Mapping stress networks using functional magnetic resonance imaging in awake animals. Neurobiology of Stress. 9, 251-263 (2018).
Hu, X. P., Le, T. H., Parrish, T., Erhard, P. Retrospective Estimation and Correction of Physiological Fluctuation in Functional Mri. Magnetic Resonance in Medicine. 34 (2), 201-212 (1995).
Birn, R. M. The role of physiological noise in resting-state functional connectivity. Neuroimage. 62 (2), 864-870 (2012).
Caballero-Gaudes, C., Reynolds, R. C. Methods for cleaning the BOLD fMRI signal. Neuroimage. 154, 128-149 (2017).
Pais-Roldan, P., Biswal, B., Scheffler, K., Yu, X. Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI. Frontiers in Neuroscience. 12, 00788 (2018).
Glover, G. H., Li, T. Q., Ress, D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine. 44 (1), 162-167 (2000).
Chang, C., Cunningham, J. P., Glover, G. H. Influence of heart rate on the BOLD signal: The cardiac response function. Neuroimage. 44 (3), 857-869 (2009).
Birn, R. M., Diamond, J. B., Smith, M. A., Bandettini, P. A. Separating respiratory-variation-related neuronal-activity-related fluctuations in fluctuations from fMRI. Neuroimage. 31 (4), 1536-1548 (2006).
Golestani, A. M., Chang, C., Kwinta, J. B., Khatamian, Y. B., Chen, J. J. Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test-retest reliability and effect of fMRI sampling rate. Neuroimage. 104, 266-277 (2015).
Lu, H. B., et al. Real-time animal functional magnetic resonance imaging and its application to neurophamacological studies. Magnetic Resonance Imaging. 26 (9), 1266-1272 (2008).
Cox, R. W., Jesmanowicz, A., Hyde, J. S. Real-time functional magnetic resonance imaging. Magnetic Resonance Medicine. 33 (2), 230-236 (1995).
Lee, C. C., Jack, C. R., Rossman, P. J., Riederer, S. J. Real-time reconstruction and high-speed processing in functional MR imaging. American Journal of Neuroradiology. 19 (7), 1297-1300 (1998).
Voyvodic, J. T. Real-time fMRI paradigm control, physiology, and behavior combined with near real-time statistical analysis. Neuroimage. 10 (2), 91-106 (1999).
Cohen, M. S. Real-time functional magnetic resonance imaging. Methods. 25 (2), 201-220 (2001).
Posse, S., et al. A new approach to measure single-event related brain activity using real-time fMRI: Feasibility of sensory, motor, and higher cognitive tasks. Human Brain Mapping. 12 (1), 25-41 (2001).
Decharms, R. C. Reading and controlling human brain activation using real-time functional magnetic resonance imaging. Trends in Cognitive Sciences. 11 (11), 473-481 (2007).
Bruhl, A. B. Making Sense of Real-Time Functional Magnetic Resonance Imaging (rtfMRI) and rtfMRI Neurofeedback. International Journal of Neuropsychopharmacology. 18 (6), (2015).
Cox, R. W. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research. 29 (3), 162-173 (1996).
Liou, W. W., Goshgarian, H. G. Quantitative assessment of the effect of chronic phrenicotomy on the induction of the crossed phrenic phenomenon. Experimental Neurology. 127 (1), 145-153 (1994).
Shih, Y. Y., et al. Ultra high-resolution fMRI and electrophysiology of the rat primary somatosensory cortex. Neuroimage. 73, 113-120 (2013).
Masamoto, K., Kim, T., Fukuda, M., Wang, P., Kim, S. G. Relationship between neural, vascular, and BOLD signals in isoflurane-anesthetized rat somatosensory cortex. Cerebral Cortex. 17 (4), 942-950 (2007).
van Alst, T. M., et al. Anesthesia differentially modulates neuronal and vascular contributions to the BOLD signal. Neuroimage. 195, 89-103 (2019).
Wu, T. L., et al. Effects of isoflurane anesthesia on resting-state fMRI signals and functional connectivity within primary somatosensory cortex of monkeys. Brain and Behavior. 6 (12), 00591 (2016).
Liu, X., Zhu, X. H., Zhang, Y., Chen, W. The change of functional connectivity specificity in rats under various anesthesia levels and its neural origin. Brain Topography. 26 (3), 363-377 (2013).
Liu, X. P., et al. Multiphasic modification of intrinsic functional connectivity of the rat brain during increasing levels of propofol. Neuroimage. 83, 581-592 (2013).
Hutchison, R. M., Hutchison, M., Manning, K. Y., Menon, R. S., Everling, S. Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain’s functional architecture. Human Brain Mapping. 35 (12), 5754-5775 (2014).
He, Y., et al. Ultra-Slow Single-Vessel BOLD and CBV-Based fMRI Spatiotemporal Dynamics and Their Correlation with Neuronal Intracellular Calcium Signals. Neuron. 97 (4), 925-939 (2018).
Yoshida, K., et al. Physiological effects of a habituation procedure for functional MRI in awake mice using a cryogenic radiofrequency probe. Journal of Neuroscience Methods. 274, 38-48 (2016).

Play Video

PDF

DOI

DOWNLOAD MATERIALS LIST

Cite This Article

Choi, S., Takahashi, K., Jiang, Y., Köhler, S., Zeng, H., Wang, Q., Ma, Y., Yu, X. Real-Time fMRI Brain Mapping in Animals. J. Vis. Exp. (163), e61463, doi:10.3791/61463 (2020).

Automatically Generated