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Comparison of differences in brain structure area and differences through tractography analysis: patients with subclinical depression

Sang Jin Im¹, Jeong hwan Lee², and Sie kyeong Kim³
¹Gacheon University, Incheon, Korea, Republic of, ²2Department of Psychiatry, Chungbuk National University College of Medicine, Cheongju, Korea, Republic of, ³Department of Psychiatry, Chungbuk National University College of Medicine, Cheongju, Korea, Republic of

Synopsis

Structural and functional changes in the subcortical area affect cognitive function and can cause increased vulnerability to mood symptoms such as anxiety and depression. However, studies on changes in subcortical structures show inconsistencies. This research shows the structural differences in segmented subcortical regions between control and depression groups and visualizes the pathway between structures according to the connection strength through tractography analysis to confirm functional differences.

Introduction

Subclinical depression(SD), a condition that does not meet the criteria for depressive disorder despite having depressive symptoms is known to have a significant effect on the quality of life, such disorders can persist over a serious combination of serious symptoms, as well as mild grief and numbness. In addition, a previous study confirmed that the risk of developing minor depression from subclinical depression is increased in subjects with subclinical depression¹. Tractography analysis using DTI allows the mapping of neural connectivity by reconstructing each voxel containing diffusion information into a fiber path^2-4. Such analyses can show how many various related pathologies of the brain may be involved in changes in behavior, learning, and aging. However, further investigation is needed because relatively little is known about connectivity patterns between subcortical areas. This study provides a comprehensive cross-sectional analysis of changes in the brain structure related to depression in two groups of elderly populations using 3T MRI. The results of this study can be served as a reference database for the study of depression-related brain connectivity.

Method

The recruited participants, a total of 39, were divided into two groups: 20 in the controls and 19 in the diseased group. Participants who agreed to the test were given a mini-mental status examination in the Korean version of the CERAD assessment packet(MMSE-KC)⁵ and the Korean version of the short geriatric depression scale(GDS-K)⁶. Image data acquisition was conducted on a 3 Tesla Philips Achieve MRI scanner(Philips Medical System, Netherlands). For signal reception, a Sensitivity encoding(SENSE) 8-channel head coil was applied. The pulse sequence used for this acquisition was High-resolution T1-weighted three-dimensional magnetization-prepared rapid gradient echo(T1-MPRAGE) (Gradient echo sequence with a Repetition Time(TR) = 6.8 ms, Echo Time(TE) = 3.2 ms, Flip Angle(FA) = 9°, Bandwidth = 241.1Hz, Field Of View(FOV) = 256x240 mm, Slice Thickness = 1.2 mm, Matrix size = 256x240, Voxel size = 1×1×1.2 mm³, Number of Slice = 170, Scan time = 5 m 34 s) and 2D EPI-Diffusion tensor (Spin echo sequence with a Repetition Time(TR) = 6033 ms, Echo Time(TE) = 70 ms, Flip Angle(FA) = 90°, Bandwidth = 29.8 Hz, Field Of View(FOV) = 224x224 mm, Matrix size = 112x109, Slice Thickness = 3 mm, Voxel size = 2×2.04× 3 mm³, Number of Slice = 50, Diffusion gradient pulse duration(δ) = 34.4 ms, Diffusion gradient separation(Δ) = 12.3 ms, B-value = 1000 s/mm², Scan time = 3 m 31 s). Pipelines used for image data analysis were processed using FMRIB software library version 5.0.1(FSL, Created by the Analysis Group, Oxford, UK)⁷ and MRtrix3 (Brain Research Institute, Melbourne, Australia)⁸. First, all DICOM data were converted to NIFTI format, and brain images were extracted from the input data. After registering each extracted brain images to Montreal Neurological Institute(MNI) 152 space, the segmentation of volume estimation was performed to calculate the total brain tissue volume⁹. Second, using the FIRST tool, we acquired extracted subcortical regions(Nucleus Accumbens, Amygdala, Caudate, Hippocampus, Globus Pallidus, Putamen, and Thalamus) of the brain¹⁰. Third, the volume data of 7 subcortical structures were measured. Lastly, DTI data was preprocessed through noise removal, and neural connectivity analyzed and quantified through diffusion analysis¹¹. The image Data processing pipeline is shown in Fig 1.

Result

The visualized subcortical areas are presented in Fig. 2, and detailed positions and shapes of them are identified on each slide. Also, the segmentation results are 3D rendered in Fig. 3. Table 1 shows the statistical difference of all average volume, FA, and ADC values between the controls and the depressions. Although there were no significant differences in volume and the FA values between the control group and the depression group, ADC value on the Left Hippocampus area was significantly different (p: 4.69E-02). However, after FDR correction was done, the significant difference disappeared (p>0.05). The probabilistic tractography analysis is presented in the form of a linked matrix (Fig 4-A,B), with the T-test comparison each group's connectivity strength are compared (Fig 4-C,D). The connectivities between structures that show the significant difference from the T-test statistical analysis are 3D rendered and visible in Figure 5. The aforementioned results are consistent with the connectivity matrix shown in Figure 4, the control group showing stronger connectivity strength than the disease group's connectivity.

Discussion and conclusion

This study aims to investigate structural and functional differences in subcortical regions of aged patients with depression using 3Tesla MRI. Structural analysis showed that in the majority of measured subcortical volumes, FA, and ADC were smaller in the depression group volumes, with measured values on the left hemisphere larger than that of the right hemisphere in all groups. Functional analysis using probabilistic tractography indicated that the connection intensity between each subcortical area was higher in the control group than the depression group, especially between left accumbense-left hippocampus, left accumbense-right hippocampus, and right thalamus-right caudate. Such differences of connection strength showed that emotional and cognitive disorders, such as anxiety and sadness, can affect the subcortical connectivity of depression groups, and the analysis of cross-regional network measures can help assess cognitive impairment across clinical conditions¹². The results of this study can be used as a reference for other brain connection related research in depression.

Acknowledgements

No acknowledgement found.

References

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[3] Abhinav K, Yeh FC, Pathak S et al. Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: a review. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease. 2014;1842(11):2286-2297.

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[5] Lee JH, Lee KU, Lee DY et al. Development of the Korean Version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-K) clinical and neuropsychological assessment batteries. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2002;57(1):P47-P53.

[6] Bae JN, Cho MJ et al. Development of the Korean version of the Geriatric Depression Scale and its short form among elderly psychiatric patients. Journal of psychosomatic research. 2004;57(3):297-305.

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[11] Shen X, Reus LM, Cox SR et al. Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank imaging data. Scientific reports. 2017;7(1):1-10.

Figures

Figure 1. Full analysis pipeline of the image data.

Figure 2. The segmentation result of subcortical structures from the entire brain. 3D rendered brain showing the slice number to view the location of the subcortical structures (Top left), subcortical structure label (Bottom left), segmentation information of the subcortical structures overlaid on MPRAGE image data (Right 1-6).

Figure 3. Segmented subcortical structures rendered in 3D. Model Plot of the position of subcortical areas (Left), location indicator and the label information of each subcortical structures (Right a-f).

Figure 4. Connectivity matrix map using DTI analysis results. Control Group Connectivity Matrix Map (A), Depression Group Connectivity Matrix Map (B), Subcortical Area Connectivity Matrix between two groups (C), Connectivity matrix with significant difference (D). The link matrix was estimated between 14 anatomical areas on a logarithmic 10 scale color map. Tractography seed is shown above, target shown in the left column, along with rendered volumes of each structural area.

Figure 5. The significant difference of connection between subcortical areas is 3D rendered. Pathway connection strength is expressed in accordance with a logarithmic 10 scale gray map. The rendered structure and connection strength were presented in Axial, Sagittal, and Coronal (Top), and the exact location of the structural area was rendered on the left.

Table 1. Volume, FA and ADC of Subcortical Structures.

Volume is measured in cubic units (1 voxel = 1mm3). ADC is measured in millimeter squared per seconds (mm2/s). Displays the Significant P-value in bold type.

Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)

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