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Free keywords:
Structural covariance networks; Substance use disorders; Brain organization
Abstract:
Background
Previous research has demonstrated systematic structural alterations in neuropsychiatric disorders, linked to the human connectome's intrinsic organization. However, it remains unclear if similar coordinated co-alteration effects extend to substance use disorders (SUDs). Here, we examined substance use co-alteration networks (SUDcov) to uncover macroscale principles of illness and substance-use effects across the cortex.
Methods
We derived maps of case-control differences in cortical thickness from 2,847 patients with six substance use disorders (alcohol, methamphetamines, cocaine, opioids, cannabis, nicotine) and 1,951 non-affected individuals from the ENIGMA Addiction Working Group. We investigated substance use co-alteration networks via inter-regional SUD association with cortical thickness and performed probed systematic co-organisation with covariance and neuropsychiatric co-alterations using hub, epicenter and diffusion map embedding modeling.
Results
SUDcov hubs followed normative functional (r=0.527, pspin < 0.05) and structural (r=0.314, pspin < 0.05) connectivity patterns, linking to epicenters. SUDcov and cortical thickness covariance patterns overlapped, apart from inferior temporal areas and medial orbitofrontal cortex (mOFC). The primary gradient of SUDcov differentiated OFC/cingulate from the rest of the cortex and mirrored the second covariance and neuropsychiatric co-alteration gradient (pspin < 0.05). The second SUDcov gradient differentiated parietal and OFC areas from the rest of the cortex. Hierarchical clustering of SUD and neuropsychiatric co-alteration patterns further underlined this differentiation.
Conclusions
Substance use co-alteration networks are organized in a network-like fashion. These patterns underscore the differentiation between paralimbic and other regions of the cerebral cortex by SUDs co-alterations. We observed a differentiation between SUD and neuropsychiatric co-alteration networks, possibly related to co-morbidity and neurodevelopmental factors.
