Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent

Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction

Drug abuse is a serious problem worldwide. Owing to intermittent intake of certain substances and the early inconspicuous clinical symptoms, this brings huge challenges for timely diagnosing addiction status and preventing substance use disorders (SUDs). As a non-invasive technique, neuroimaging can capture neurobiological signatures of abnormality in multiple brain regions caused by drug consumption in each clinical stage, like parenchymal morphology alteration as well as aberrant functional activity and connectivity of cerebral areas, making it realizable to diagnosis, prediction and even preemptive therapy of addiction. Machine learning (ML) algorithms primarily used for classification have been extensively applied in analysing medical imaging datasets. Significant neurobiological characteristics employed and revealed by classifiers were used to diagnose addictive states and predict initiation and vulnerability to drug usage, treatment abstinence, relapse and resilience of addicts and the risk of SUD. In this review, we summarize application of ML methods in neuroimaging focusing on addicts' diagnosis of clinical status and risk prediction and elucidate the discriminative neurobiological features from brain electrophysiological, morphological and functional perspectives that contribute most to the classifier, finally highlighting the auxiliary role of ML in addiction treatment.

Mapping white matter structural and network alterations in betel quid-dependent chewers using high angular resolution diffusion imaging

BACKGROUND

To evaluate brain white matter diffusion characteristics and anatomical network alterations in betel quid dependence (BQD) chewers using high angular resolution diffusion imaging (HARDI).

METHODS

The current study recruited 53 BQD chewers and 37 healthy controls (HC) in two groups. We explored regional diffusion metrics alternations in the BQD group compared with the HC group using automated fiber quantification (AFQ). We further employed the white matter (WM) anatomical network of HARDI to explore connectivity alterations in BQD chewers using graph theory.

RESULTS

BQD chewers presented significantly lower FA values in the left and right cingulum cingulate, the left and right thalamic radiation, and the right uncinate. The BQD has a significantly higher RD value in the right uncinate fasciculus than the HC group. At the global WM anatomical network level, global network efficiency (p = 0.008) was poorer and Lp (p = 0.016) was greater in the BQD group. At the nodal WM anatomical network level, nodal efficiency (p < 0.05) was lower in the BQD group.

CONCLUSION

Our findings provide novel morphometric evidence that brain structural changes in BQD are characterized by white matter diffusivity and anatomical network connectivity among regions of the brain, potentially leading to the enhanced reward system and impaired inhibitory control.

Addiction and the cerebellum with a focus on actions of opioid receptors

Increasing evidence suggests that the cerebellum could play a role in the higher cognitive processes involved in addiction as the cerebellum contains anatomical and functional pathways to circuitry controlling motivation and saliency. In addition, the cerebellum exhibits a widespread presence of receptors, including opioid receptors which are known to play a prominent role in synaptic and circuit mechanisms of plasticity associated with drug use and development of addiction to opioids and other drugs of abuse. Further, the presence of perineural nets (PNNs) in the cerebellum which contain proteins known to alter synaptic plasticity could contribute to addiction. The role the cerebellum plays in processes of addiction is likely complex, and could depend on the particular drug of abuse, the pattern of use, and the stage of the user within the addiction cycle. In this review, we discuss functional and structural modifications shown to be produced in the cerebellum by opioids that exhibit dependency-inducing properties which provide support for the conclusion that the cerebellum plays a role in addiction.