Aberrant White Matter Microstructure in youth with a familial risk for bipolar disorder
Poster No:
416
Submission Type:
Abstract Submission
Authors:
Haoran Xu1,2,3, Lu Lu1, Lisha Zhang1,2, Xiao Li4, Qiyong Gong1, Manpreet Singh5, Melissa DelBello2
Institutions:
1Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, OH, 3Department of Interventional Therapy,Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 4Department of Interventional Therapy,Chinese Academy of Medical Sciences and Peking Union Medical Co, Beijing, China, 5Psychiatry and Behavioral Sciences, University of California Davis, Sacramento, CA
First Author:
Haoran Xu
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University|Department of Psychiatry, College of Medicine, University of Cincinnati|Department of Interventional Therapy,Chinese Academy of Medical Sciences and Peking Union Medical College
Chengdu, China|Cincinnati, OH|Beijing, China
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University|Department of Psychiatry, College of Medicine, University of Cincinnati|Department of Interventional Therapy,Chinese Academy of Medical Sciences and Peking Union Medical College
Chengdu, China|Cincinnati, OH|Beijing, China
Co-Author(s):
Lu Lu
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Lisha Zhang
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University|Department of Psychiatry, College of Medicine, University of Cincinnati
Chengdu, China|Cincinnati, OH
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University|Department of Psychiatry, College of Medicine, University of Cincinnati
Chengdu, China|Cincinnati, OH
Xiao Li
Department of Interventional Therapy,Chinese Academy of Medical Sciences and Peking Union Medical Co
Beijing, China
Department of Interventional Therapy,Chinese Academy of Medical Sciences and Peking Union Medical Co
Beijing, China
Qiyong Gong
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Melissa DelBello
Department of Psychiatry, College of Medicine, University of Cincinnati
Cincinnati, OH
Department of Psychiatry, College of Medicine, University of Cincinnati
Cincinnati, OH
Introduction:
Bipolar disorder (BD) affects 1-3% of the global population, with onset typically in adolescence or early adulthood(Grande et al.,2016). BD is highly familial, and offspring of BD patients may show mood dysregulation as a precursor to BD(Perich et al., 2017). Neurobiological models emphasize prefrontal-limbic white matter (WM) abnormalities, which play a central role in BD's pathophysiology(Phillips et al., 2008). Studies have found WM disruptions in BD, especially in the anterior thalamic radiation (ATR), corpus callosum, and uncinate fasciculus(Favre et al., 2019). Given BD's hereditary nature, high-risk youth may exhibit similar WM changes(Linke et al., 2020), highlighting the potential of WM microstructure as an early risk marker for BD.
Methods:
A total of 121 symptomatic youth aged 12-17 years, with a first-degree relative diagnosed with BD, and 55 matched healthy controls (HC) were recruited at the University of Cincinnati and Stanford University(Honeycutt et al., 2022). High-risk youth were selected based on moderate to severe depressive and/or anxiety symptoms, assessed by the Childhood Depression Rating Scale-Revised and Pediatric Anxiety Rating Scale (PARS).
MRI data were acquired using 3T scanners and 8-channel phased-array head coils at both sites. The DTI and T1w data were preprocessed using MRtrix3, FSL and ANTs. The data preprocessing involved denoising, correct the Gibb's ringing, eddy current correction, bias field correction and skull removal. The AFQ procedure steps were summarized as follow: performance of the whole-brain tractography, segmentation of a whole brain fiber group into 20 fascicles groups, definition of the tract core and filtering out of stray fibers, and quantification of the diffusion measures at 100 equidistant nodes along each fiber tract(Winkler et al., 2012).
The analysis was performed using a non-parametric permutation-based multiple-comparison correction approach(Nichols et al., 2002), applied separately across four different outcome measures: fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) along each fiber tract for equally spaced 1–100 nodes (p < 0.05, FEW-corrected). Only findings that included three or more adjacent nodes along a tract were presented(Banfi et al., 2019).
MRI data were acquired using 3T scanners and 8-channel phased-array head coils at both sites. The DTI and T1w data were preprocessed using MRtrix3, FSL and ANTs. The data preprocessing involved denoising, correct the Gibb's ringing, eddy current correction, bias field correction and skull removal. The AFQ procedure steps were summarized as follow: performance of the whole-brain tractography, segmentation of a whole brain fiber group into 20 fascicles groups, definition of the tract core and filtering out of stray fibers, and quantification of the diffusion measures at 100 equidistant nodes along each fiber tract(Winkler et al., 2012).
The analysis was performed using a non-parametric permutation-based multiple-comparison correction approach(Nichols et al., 2002), applied separately across four different outcome measures: fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) along each fiber tract for equally spaced 1–100 nodes (p < 0.05, FEW-corrected). Only findings that included three or more adjacent nodes along a tract were presented(Banfi et al., 2019).
Results:
A total of 153 (high-risk, n=108 and HC, n= 45) youth (mean age: 14.9±1.6 years, 43% male) were included in the analysis.
We found that HR youth had significantly increased FA in nodes 1-7 of left ATR, nodes 1-9 of right ATR, nodes 1-5 of right corticospinal, and nodes 1-4 of left corticospinal comparing to HC group. HR patients also showed increased AD in nodes 50-52 of right ATR comparing to HC group. Moreover, HR patients showed decreased RD in nodes 2-7 of left corticospinal, nodes 1-11 of right corticospinal and nodes 1-11, of callosum forceps minor, as well as decreased MD in nodes 3-13 of right corticospinal.
Within the HR group, the MD of the right corticospinal tract showed a negative correlation with both Treatment-emergent activation and suicidality assessment profile (TEASAP) -akathisia and TEASAP_suicide subscale. In contrast, the RD of the callosum forceps minor was positively correlated with PARS scores. Additionally, the AD of the left corticospinal tract was positively correlated with both PARS and Children's Global Assessment Score, and negatively correlated with TEASAP_AKA.
We found that HR youth had significantly increased FA in nodes 1-7 of left ATR, nodes 1-9 of right ATR, nodes 1-5 of right corticospinal, and nodes 1-4 of left corticospinal comparing to HC group. HR patients also showed increased AD in nodes 50-52 of right ATR comparing to HC group. Moreover, HR patients showed decreased RD in nodes 2-7 of left corticospinal, nodes 1-11 of right corticospinal and nodes 1-11, of callosum forceps minor, as well as decreased MD in nodes 3-13 of right corticospinal.
Within the HR group, the MD of the right corticospinal tract showed a negative correlation with both Treatment-emergent activation and suicidality assessment profile (TEASAP) -akathisia and TEASAP_suicide subscale. In contrast, the RD of the callosum forceps minor was positively correlated with PARS scores. Additionally, the AD of the left corticospinal tract was positively correlated with both PARS and Children's Global Assessment Score, and negatively correlated with TEASAP_AKA.
·Fig. 1 The significantly altered fiber tracts between HR and HC groups (FWE correction, P < 0.05, solid lines for means and dashed lines for 95% confidence interval).
·Fig. 2 Scatter plots for the mean diffusion parameters values of the significantly correlated nodes of each fiber and the clinical scale within HR.
Conclusions:
The results of the current DTI study confirm and expand our understanding of the critical role that alterations in the Fronto-limbic regions and corticospinal tracts play in the pathophysiology of BD. These findings suggest that disrupted white matter connectivity in these regions may serve as a disease-related feature, reflecting the underlying vulnerability present in high-risk youth for BD. These white matter abnormalities may also serve as early biomarkers for identifying individuals at an increased risk of developing BD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
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:
MRI
PEDIATRIC
Psychiatric
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - bipolor disorder
1|2Indicates the priority used for review
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Favre, P., Pauling, M., Stout, J., Hozer, F., Sarrazin, S., Abe, C., . . . Group, E. B. D. W. (2019). Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals. Neuropsychopharmacology, 44(13), 2285-2293.
Grande, I., Berk, M., Birmaher, B., & Vieta, E. (2016). Bipolar disorder. Lancet, 387(10027), 1561-1572.
Honeycutt, D. C., DelBello, M. P., Strawn, J. R., Ramsey, L. B., Patino, L. R., Hinman, K., . . . Singh, M. K. (2022). A Double-Blind Randomized Trial to Investigate Mechanisms of Antidepressant-Related Dysfunctional Arousal in Depressed or Anxious Youth at Familial Risk for Bipolar Disorder. J Pers Med, 12(6).
Linke, J. O., Stavish, C., Adleman, N. E., Sarlls, J., Towbin, K. E., Leibenluft, E., & Brotman, M. A. (2020). White matter microstructure in youth with and at risk for bipolar disorder. Bipolar Disord, 22(2), 163-173.
Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp, 15(1), 1-25.
Perich, T., Frankland, A., Roberts, G., Levy, F., Lenroot, R., & Mitchell, P. B. (2017). Disruptive mood dysregulation disorder, severe mood dysregulation and chronic irritability in youth at high familial risk of bipolar disorder. Aust N Z J Psychiatry, 51(12), 1220-1226.
Phillips, M. L., Ladouceur, C. D., & Drevets, W. C. (2008). A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry, 13(9), 829, 833-857.
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92(100), 381-397.
Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One, 7(11), e49790.