White Matter Functional Alterations Precede Gray Matter Functional Aberrations in Cocaine Addiction
Poster No:
368
Submission Type:
Abstract Submission
Authors:
Chuan Fan1, Xiaochu Zhang2
Institutions:
1The First Affiliated Hospital of AnHui Medical University, HeFei, AnHui Province, 2University of Science & Technology of China, HeFei, AnHui Province
First Author:
Co-Author:
Introduction:
White matter (WM) alterations are crucial pathological changes in cocaine addiction and serve as an important structural basis for maintaining addictive behaviors (Liu X et al.,2023; Yalçın B et al.,2024). Although traditional fMRI studies mainly focused on gray matter (GM), recent studies highlight the physiological importance of WM function in neuropsychiatric disorders (Fan C et al.,2023; Ji Gj et al.,2024). Moreover, WM functional networks may reveal additional aberrations compared to GM in early disease stages (Jiang Y et al.,2022). WM functional networks are more constrained by morphological structure and exhibit greater randomness compared to GM, suggesting their distinct roles in brain diseases (Pang JC et al.,2023; Li J et al.,2019). Despite the increasing recognition of WM function role in neuropsychiatric disorders, systematic studies comparing its function with that of GM in these conditions remain scarce. We hypothesize that WM function and GM function play distinct roles in cocaine addiction. A systematic investigation of their differential contributions through graph theory approaches may provide novel insights into the mechanisms of addiction.
Methods:
This study included two publicly available datasets. The first dataset consisted of 62 cocaine users (mean age 31.50±7.47, 53 males/9 females, mean education level 11.15±3.05) and 48 matched healthy controls (mean age 30.27±8.12, 38 males/10 females, mean education level 12.27±3.01). The second dataset comprised an independent sample of 28 cocaine users (mean age 36.61±9.48, 25 males/3 females). We constructed both gray matter (GM) and white matter (WM) functional networks for all participants using identical methods and criteria, and employed graph theoretical analysis to examine whether both WM functional and GM functional networks exhibited topological differences between cocaine users and healthy controls. We trained two distinct machine learning models (XGBoost): one to evaluate whether abnormal topological indices could effectively discriminate cocaine users from healthy controls, and another to assess whether the model could differentiate the severity of impulsivity among cocaine users and validate impulsivity severity identification in an independent cocaine sample. Additionally, Neurite Orientation Dispersion and Density Imaging (NODDI) analysis was employed to investigate microstructural alterations in both GM and WM among cocaine users. A series of validation analyses were also conducted to test the robustness of the functional topological differences between WM and GM.
·Fig 1 Flowchart of Imaging Data Preprocessing and Extraction.
Results:
For the WM functional networks, cocaine users demonstrated significantly increased Eloc at the AUC value or across each sparsity threshold compared to healthy controls (Fig 2a). While no significant differences were observed in GM functional topological properties at the AUC values or across each sparsity threshold (Fig 2b). Abnormal WM functional topological indices effectively distinguished cocaine users from healthy controls (accuracy 0.6909, p = 0.0016) (Fig 2c). WM functional topological alterations also successfully identify impulsivity levels among cocaine users (accuracy 0.6810, p = 0.0131), with further validation in an independent cocaine sample (accuracy 0.7467, p = 0.0170) (Fig 2c).While GM functional topology could neither distinguish cocaine addicts from healthy controls nor identify impulsivity severity in cocaine users (Fig 2d). Confounding factor analysis showed the abnormal WM functional topology failed to identify age or education level (Fig 2e). NODDI analysis showed microstructural abnormalities in both WM and GM in cocaine users (Fig 2f).
·Fig 2 Main Results
Conclusions:
Despite microstructural changes exhibited in both WM and GM, abnormalities in WM functional topological indices precede those in GM. These WM indices can serve as potential neurobiological markers for disease classification and severity differentiation, suggesting that WM function may provide earlier warning signals for the disease.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Diffusion MRI
Keywords:
Addictions
FUNCTIONAL MRI
Psychiatric Disorders
White Matter
Other - Cocaine Addiction; Graph theory; Functional connectivity; NODDI analysis
1|2Indicates the priority used for review
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Provide references using APA citation style.
Ji, G. J. (2023). White matter dysfunction in psychiatric disorders is associated with neurotransmitter and genetic profiles. Nature Mental Health, 1(9), 655-666.
Jiang, Y. (2022). Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia. Psychological Medicine, 52(7), 1333-1343.
Li, J. (2019). Exploring the functional connectome in white matter. Human brain mapping, 40(15), 4331-4344.
Liu, X. (2023). Cellular and molecular basis of drug addiction: The role of neuronal ensembles in addiction. Current Opinion in Neurobiology, 83, 102813.
Pang, J. C. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566-574.
Yalçın, B. (2024). Myelin plasticity in the ventral tegmental area is required for opioid reward. Nature, 1-9.