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Age-Dependent Microstructural Changes in Major Depressive Disorder: A Fixel-Based Analysis Study

Poster No:

502

Submission Type:

Abstract Submission

Authors:

Shi-Ming Wang¹^,2, Fan Huang²^,3^,4, Chih-Mao Huang²^,3^,4, Shwu-Hua Lee⁵^,6, Chemin Lin⁷^,6^,8

Institutions:

¹Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ²Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ³Center for intelligent Drug Systems and Smart Bio-devices, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ⁴Interdisciplinary neuroscience PhD program, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ⁵Community Medicine Research Center, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan, ⁶College of Medicine, Chang Gung University, Taoyuan, Taiwan, ⁷Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan, ⁸Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan

First Author:

Shi-Ming Wang
Department of Computer Science, National Yang Ming Chiao Tung University|Department of Biological Science and Technology, National Yang Ming Chiao Tung University
Hsinchu, Taiwan|Hsinchu, Taiwan

Co-Author(s):

Fan Huang
Department of Biological Science and Technology, National Yang Ming Chiao Tung University|Center for intelligent Drug Systems and Smart Bio-devices, National Yang Ming Chiao Tung University|Interdisciplinary neuroscience PhD program, National Yang Ming Chiao Tung University
Hsinchu, Taiwan|Hsinchu, Taiwan|Hsinchu, Taiwan

Chih-Mao Huang
Department of Biological Science and Technology, National Yang Ming Chiao Tung University|Center for intelligent Drug Systems and Smart Bio-devices, National Yang Ming Chiao Tung University|Interdisciplinary neuroscience PhD program, National Yang Ming Chiao Tung University
Hsinchu, Taiwan|Hsinchu, Taiwan|Hsinchu, Taiwan

Shwu-Hua Lee
Community Medicine Research Center, Linkou Chang Gung Memorial Hospital|College of Medicine, Chang Gung University
Taoyuan, Taiwan|Taoyuan, Taiwan

Chemin Lin
Department of Psychiatry, Keelung Chang Gung Memorial Hospital|College of Medicine, Chang Gung University|Community Medicine Research Center, Chang Gung Memorial Hospital
Keelung, Taiwan|Taoyuan, Taiwan|Keelung, Taiwan

Introduction:

Late-life depression (LLD) is one of the most common psychiatric illnesses affecting the growing population of older adults, associated with an increased risk of age-related cognitive declines [1] and progression of dementia [2]. It has been firmly established that Major Depressive Disorder (MDD) is associated with structural brain abnormalities [3-5]. Prior studies using Diffusion Tensor Imaging (DTI) have revealed a wide range of white matter abnormalities in adults with MDD [6-8]. In this study, we employed Fixel-Based Analysis (FBA), a novel diffusion model based on Constrained Spherical Deconvolution (CSD), to examine the specificity of LLD brain microstructure [9]. By utilizing FBA to conduct a fiber-specific investigation of white matter alterations, we aim to further unveil the interplay between the progression of MDD in the aging brain.

Methods:

We included 85 patients with major depressive disorder (MDD) and 67 healthy controls (HC). The MDD group consisted of 26 middle-aged (mean age 51.5 years; men/women = 6/20) and 59 elderly individuals (mean age 66.8 years; men/women = 20/39). The HC group comprised 26 middle-aged (mean age 49.1 years; men/women = 7/19) and 41 elderly participants (mean age 68.4 years; men/women = 16/25). High-resolution images were obtained using a GE HC 3T Discovery MR750 scanner. T1-weighted images: TR/TE=8.24/3.2 ms, resolution voxel size = 0.5 x 0.5 x 1 mm³, 160 slices, inversion time=450 ms, flip angle=12°, matrix size = 512 x 512. DWI utilized a spin-echo EPI sequence with: TR/TE=4500/84 ms, voxel size = 0.98 x 0.98 x 4 mm³, matrix size = 256 x 256, 29 slices, b-values of 0 and 1000 s/mm² across 32 directions. Intracranial volume calculations (ICV) from T1-weighted images were performed using FSL's Brain Extraction Tool. We implemented fixel-based analysis adhering to protocols outlined by MRtrix3 [9]. For subsequent analysis, the three main FBA metrics-fiber density (FD), fiber cross-section (FC), and the combined measure of fiber density and cross-section (FDC)-were computed. For tract-based analysis, we delineated 72 fiber tracts using the automated TractSeg approach [10]. We conducted a statistical comparison of FBA metrics between MDD patients and healthy controls, with ICV, age and gender as covariates. For each participant, mean FD, FC, and FDC values within each tract of interest (TOI) were computed. To assess the interplay between disease presence and age, a two-factor ANOVA was employed. This analysis delineated the effects and interactions between these two crucial factors (p-value < 0.05).

Results:

Fig.1.A indicates that after adjusting for age and gender, patients with MDD exhibit significantly lower FD in the bilateral Fornix (FX) compared to healthy individuals. Fig.1.B and 1.C demonstrate widespread white matter alterations across three different FBA metrics as a function of age between individuals with MDD and healthy controls., Patients with MDD showed changes in inter-hemispheric and intra-hemispheric connectivity with age. Fig.2.A highlights significant disease effects on FD within the corpus callosum (CC) and the bilateral FX. Additionally, the FD result showed interaction between disease progression and age within the right optic radiation (OR), right parieto-occipital pontine (POPT), right striato-occipital (ST OCC), right striato-parietal (ST PAR), and right thalamo-occipital (T OCC) tracts. Fig.2.B reveals FC interactions between disease and age in the right anterior thalamic radiation (ATR), left OR, left ST OCC, and left T OCC. In Fig.2.C, FDC result showed interaction of disease and age in the left ATR, suggesting that these changes in the ATR may modulate by both disease progression and aging.

·Figure 1. Fixel-based analysis results visualized within glass brain, with colors marking fixel directions, thresholded by FWE-corrected p-values.

·Figure 2. This figure displays the distribution of mean FBA metrics for key fiber bundles from a two-way ANOVA analysis, overlaid on glass brain with color-coded fixel orientations.

Conclusions:

Our findings imply microstructural changes in the depressive brain are dynamic and age-dependent. The study underscores the need for considering age in neurological disease progression and white matter integrity, advocating for more targeted treatments.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Lifespan Development:

Aging

Modeling and Analysis Methods:

Diffusion MRI Modeling and Analysis ²

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

White Matter Anatomy, Fiber Pathways and Connectivity

Novel Imaging Acquisition Methods:

Diffusion MRI

Keywords:

Aging

MRI

Psychiatric

Psychiatric Disorders

STRUCTURAL MRI

White Matter

WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC

^1|2Indicates the priority used for review

Provide references using author date format

1. John, A., et al., Affective problems and decline in cognitive state in older adults: a systematic review and meta-analysis. Psychological medicine, 2019. 49(3): p. 353-365.
2. Asmer, M.S., et al., Meta-analysis of the prevalence of major depressive disorder among older adults with dementia. The Journal of clinical psychiatry, 2018. 79(5): p. 15460.
3. Lin, C., et al., Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI. Brain Imaging and Behavior, 2023. 17(1): p. 125-135.
4. Lin, C., et al., Greater white matter hyperintensities and the association with executive function in suicide attempters with late-life depression. Neurobiology of aging, 2021. 103: p. 60-67.
5. Patil, A.U., et al., Review of EEG-based neurofeedback as a therapeutic intervention to treat depression. Psychiatry Research: Neuroimaging, 2023: p. 111591.
6. Chen, G., et al., Disorganization of white matter architecture in major depressive disorder: a meta-analysis of diffusion tensor imaging with tract-based spatial statistics. Scientific reports, 2016. 6(1): p. 21825.
7. Liao, Y., et al., Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. Journal of Psychiatry and Neuroscience, 2013. 38(1): p. 49-56.
8. Murphy, M.L. and T. Frodl, Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biology of mood & anxiety disorders, 2011. 1: p. 1-12.
9. Raffelt, D.A., et al., Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage, 2017. 144: p. 58-73.
10. Wasserthal, J., P. Neher, and K.H. Maier-Hein, TractSeg-Fast and accurate white matter tract segmentation. NeuroImage, 2018. 183: p. 239-253.