Over the last 15 years, functional magnetic resonance imaging (fMRI) of the brain's "resting state" (rsfMRI) and its measures of functional connectivity have been major topics of interest.
It is assumed that low-frequency fluctuations seen in the BOLD signal reflect sudden neural activity and that synchronized fluctuations in separate brain regions thus point to a functional link between them, as illustrated in figure 1.
Figure 1. Principle of Seed based Functional Connectivity Analysis.
Initially demonstrated for the human motorcortex (Biswal et al., 1995), a number of connectivity networks have been discovered in the human brain and have been shown to vary in different psychiatric and neurological diseases. This makes functional connectivity MRI (fcMRI) a highly interesting method to improve our understanding of brain function with regards to health and disease.
The establishment of fcMRI in animal models was started only a few years ago. The technique is very attractive, especially in rodents, as it is anticipated to offer a highly interesting functional readout for disease progression, treatment and repair in a wide range of existing animal models.
When compared to fMRI, fcMRI does not depend on stimulation and can explore brain networks that cannot be accessed via external stimuli. It also leverages the major benefits of MRI with regard to non-invasiveness and suitability for longitudinal studies.
This technical brief assesses the feasibility and potential of fcMRI applied to imaging of rat brain through a high field MRI system.
117/16 Bruker BioSpec ('H @ = 500 MHz); BGA9s gradient system (Gmax=750 mT/m; min. ramp time of 125 µs); quadrature volume resonator (inner diameter 72 mm) for transmission; Avance II electronics; ParaVision; rat brain quadrature surface coil (-30x30 mm2) for reception.
Monitoring system (SA Instruments): fiber optic temperature probe, pulse oxymeter, display and recording (synced to MRI acquisition) of respiration, respiratory pad, pulse and temperature utilizing a custom-made data acquisition software (DasyLab).
FieldMap acquisition and local MapShim; TurboRARE for T-weighted anatomical reference (RARE-factor 8, 28 slices a 0.5 mm, 2562 matrix, 125 x 125 µm2, TR 4.0 s, TEoff 32.5 ms, 2 averages, acq. time 4m16s); single-shot gradient echo EPI for functional image acquisition.
Initial anaesthesia utilizing 1.5 % Iso-flurane; conversation to Medetomidine sedation: subcutaneous 0.5 ml bolus and ensuing infusion of 1 ml/h of Domitor solution (Pfizer; 0.1 ml/kg ad 10 ml saline solution).
Processing and Analysis
Motion correction and coregistration to rodent brain template utilizing FSL tools; connectivity analysis with custom-made ImageJ Software and FSL tools; removal of physiological noise utilizing regression of motion and physiological parameters (Kalthoff et al., 2011).
Results and Discussion
High field BOLD functional MRI information of the resting rat brain consistently attains a quality expedient for functional connectivity analysis.
Using the given EPI imaging protocol, voxel time courses generally have signal variations on the order of 1.5% that occur from a number of sources, as shown in figure 2.
Figure 2. Contributions to Resting State BOLD Signal Fluctuations.
One third of the fluctuations are attributed to the system’s intrinsic raw noise, mainly thermal noise from coil, sample and electronics. Most (~40%) of the fluctuations occur as a result of physiological noise that begin from the cardio-respiratory cycle and related pseudo-motion.
To reduce false-positive correlations in ensuing analyses, physiological noise should be rectified by motion correction and regression, which can enhance tSNR by ~30%. The remaining ~25% of fluctuations may be due to actual neurovascular fluctuations at which the succeeding functional connectivity analysis is aimed.
Seed-based connectivity analysis (SCA) can produce connectivity maps that are able to visualize the temporal correlation of voxels with seed regions defined by priori. Rodent brain functional connectivity maps from seed regions in striatum and cortex reveal clear bilateral regions, suggesting powerful interhemispheric connectivity between homologous regions, as illustrated in figure 3.
It should be noted that observation of reliable, robust and particular connectivity networks may not be possible through traditional Isoflurane anaesthesia, and needs highly sensitive experimental protocols like Medetomidine sedation (Weber 2006, Pawela 2008, Williams 2010).
Figure 3. Seed based Functional Connectivity Maps (group average).
In addition, the topology of functional connectivity networks can be studied utilizing data-driven methods such as independent component analysis (ICA). This analysis consistently detects separated brain functional networks in both cortical and subcortical structures on a per-subject basis, as indicated in figure 4. Such methods are especially interesting to compare network topologies without a priori hypotheses between species or in pathologies.
Figure 4. Topography of Functional Connectivity Networks identified via ICA.
It has been identified from human studies that functional connectivity is susceptible to various neurological disorders. Initial studies demonstrate that this holds true for a number of animal models, for instance in the longitudinal evaluation of network remodelling after stroke, as demonstrated in figure 5.
Figure 5. Loss of Functional Connectivity after Stroke.
Functional connectivity magnetic resonance imaging of the rat brain can deliver a functional readout for disease progression, treatment and repair in a number of existing animal models.
Through technical progression and extensive availability of high field systems, the challenge of fcMRI has moved towards novel data analysis strategies and maintenance of a suitable physiological state.
Functional connectivity, especially in tandem with other modalities, will be a key concept for neuroscientific research in the coming years. One of the crucial steps to manipulate this potential is to set up standard protocols for mouse fcMRI to access the wide range of transgenic animal models.
Produced from articles authored by Daniel Kalthoff and Mathias Hoehn.
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