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Detecting neuronal connectivity using diffusion tensor imaging in a Parkinson`s disease mouse basal ganglia using 9.4T MRI

Sang-Jin Im¹ and Hyeon-Man Baek¹
¹Gachon university, Incheon, Korea, Republic of

Synopsis

Parkinson's disease (PD) is a neurodegenerative disorder that affects motor and cognition, resulting from dopaminergic cell death in substantia nigra (SN). Basal ganglia, a neural circuit involved in executive functions such as motor control and have been studied extensively in PD mouse models. Mouse brain tractography using MRI have seen increasing use to study neural networks but more comprehensive analysis is needed to establish a stronger consensus. The purpose of this study is to provide a comprehensive analysis of basal ganglia neuronal connectivity in control and PD mouse models.

Introduction

Parkinson's disease (PD) is one of the most common neurological disorders and is a degenerative neuropathy that affects both motor function and cognition¹. The pathology of PD is the gradual loss of dopaminergic (DAergic) neurons in the substantia nigra (SN), significantly reducing extracellular DA levels in basal ganglia structures like the striatum². As a result of lowered DA levels, increased frequency of neuronal discharges in GPi, SNr, STN and decreased frequency in GPe can occur, inhibiting the motor nucleus of the thalamus³. Basal ganglia are neuronal circuits involved in motor control and executive functions such as motor learning, behavioral control and emotion⁴. Studies of Basal ganglia neuronal connectivity in Parkinson's disease (PD) using MRI have shown promising results but remains incomplete⁵. In addition, pathological data of PD mouse models are frequently used to document the pathology of Parkinson's but there is a lack of information regarding the neuronal interconnectivity of basal ganglia of PD mouse models. This study provides a comprehensive analysis of PD-related changes in mouse basal ganglia neuronal connectivity between control group and disease group by comparing tractography generated from each basal ganglia structure.

Method

Experiments were performed in four month old female mice (control group of TetP-AIMP2 (n= 2) and disease group 3X-Tg (n= 2)). Mouse transcardially were perfused and fixed with 4% paraformaldehyde and 0.1% Magnevist® in phosphate buffer (PB). Brains were extracted and incubated in 0.1% Magnevist/phosphate buffer for 4 days, placed in Fomblin and imaged. Image acquisition was conducted on a 9.4 T Bruker BioSpec horizontal bore, dedicated animal scanner (Bruker Biospin, Ettlingen, Germany), equipped with a gradient system of (440mT/m). For signal reception, a quadrature mouse brain surface coil (Bruker Biospin) was applied. MRI data was acquired using Paravision 5.1 software. The pulse sequence used for this acquisition was 3D TurboRARE T2 (Spin echo sequence with a repetition time = 1800 ms, echo time = 33.6 ms, flip angle = 90°, Bandwidth = 100kHz, field of view = 1.2 × 1.2 × 15.6 cm, matrix = 240 × 240 × 156, resolution = 50 × 50 × 100 µm, 1 averages and resulting in a total acquisition time of 1h 43m) and 2D EPI-Diffusion tensor (Spin echo sequence with a repetition time = 7000 ms, echo time = 30 ms, flip angle = 90°, bandwidth = 170kHz, b-value = 3003 s/mm², diffusion gradient pulse duration (δ) = 4.5 ms, diffusion gradient separation (Δ) = 10.6 ms, field of view = 1.8 × 1.8 cm, slice thickness = 0.12 mm, matrix = 150 × 150, slice = 70, resolution = 120 x 120 x 120 µm, 8 averages and resulting in a total acquisition time of 8h 10m). Diffusion tensor image data were preprocessed by denoising and biasfield correction using MRtrix3. We acquired brain extracted images from whole-head input 3D T2 image data and created masks based on Allen Mouse Brain Atlas anatomical regions using ANTx^6,7,8. Subsequently, we registered the basal ganglia masks obtained using ANTx to our native mouse brain using FSL's FLIRT tool⁹. We preformed eddy correction using FSL and then acquired fiber reconstruction data using FSL's BEDPOSTX¹⁰ and probabilistic tractography data using FSL's PROBTRACKX¹¹.

Result

The acquired T2 and DTI of a mouse as well as the FSL generated FA map are shown in Figure 1. The basal ganglia segmentation and 3D rendering of mouse brain are presented in Figure 2. 3D rendering was done by partitioning the STR, PAL, SNr, SNc and STN structures in relation to anatomical landmarks. Table 1 displays the volume, apparent diffusion coefficient (ADC) and fractional anisotropy (FA) value of our segmented structures. Probabilistic tractography between basal ganglia are represented as connectivity matrices to compare connectivity between each basal ganglia structure as shown in Figure 3. Basal ganglia connectivity map was estimated between 52 anatomic regions with a log10 scale color map using waypoints connectivity. Figure 4 shows fiber track tracts between PD related structures (e.g., GPi, GPe, SNr, SNc, STN) of control and disease group.

Discussion and conclusion

In this study, we were able to use the Allen Mouse Brain Atlas for the accurate segmentation of the basal ganglia, then use the segmentations for generating neuronal connectivity of basal ganglia structures in PD mice using high resolution 9.4T MRI. We were also able to generate a connectivity map of the entire brain for identifying connectivity differences in control and PD mouse models. In Table 1, quantitative analysis of the basal ganglia shows decrease in FA and increase in MD, which were found to be in part consistent with previous studies on Parkinson's disease¹. In addition, the connectivity matrix results in Figure 3 shows that the Parkinson group had a smaller overall signal intensity range than the control group, and that the mouse basal ganglia's interconnectivity is almost consistent with previous studies on human basal ganglia interconnectivity¹². In Figure 4, we were able to visualize the neural connectivity of Parkinson's disease-related biomarkers in the control and disease groups, and observe the reconstruction of connectivity between the structures. The results of this study can serve as a reference database for further studies using Parkinson's disease model.

Acknowledgements

This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science andICT (NRF-2017M3C7A1044367).

References

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Figures

Fig 1. Displayed Image of control group and Parkinson group with T2WI and DTI (FA color overlay).

Fig 2. Segmentation of the basal ganglia on representative T2/DTI minimum deformation images (top), 3D volume rendering with basal ganglia(bottom).

Table 1. Comparison of segmented basal ganglia structure volume, intensity, FA value and ADC value between control and disease group.

Fig 3. Probabilistic tractography connectivity matrix of control and PD mouse basal ganglia. Connectivity matrix of experimental control group and disease group (left), average matrix of each experimental group (right). Relative connectivity was estimated between 52 anatomic regions with a log10 scale color map. The tractography seeds (top) and targets (left) are displayed as 3D volume renderings.

Fig 4. Visualization of neuronal pathways in key areas of Parkinson's disease in control and disease groups.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)

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