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

Wu, Yi‐Long; Ma, Xiaofen; Zhou, Zhihua; Yan, Ji; Xu, Shoujun; Li, Meng; Fang, Jing; Li, Guoming; Zeng, Shaoqing; Lin, Chun‐Cheng; Li, Chunlong; Huang, Shiming; Jiang, Guihua

doi:10.1016/j.jpsychires.2020.08.001

Cited by 3 publications

(3 citation statements)

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“…Global efficiency reflects effective information transformation across remote regions, which is deemed to form the basis of many cognitive processes ( 35 ). Normalized Lp (lambda, λ) λ = Lpreal/Lprand, Lprand is the mean shortest path ( 36 ). In line with the results of previous studies on codeine addiction, chronic codeine abuse showed significantly higher Lp values, which indicates degradation of the small-world networks ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

“…Normalized Lp (lambda, λ) λ = Lpreal/Lprand, Lprand is the mean shortest path ( 36 ). In line with the results of previous studies on codeine addiction, chronic codeine abuse showed significantly higher Lp values, which indicates degradation of the small-world networks ( 36 ). The BQD group exhibited a decrease in global network efficiency coupled with an increased Lp in their brain network suggesting a decrease in transmission capacity/efficiency of information for the global network, the same as the result derived from a previous structural study in the BQD population ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…However, the causal relationship between error processing deficits and cocaine usage was unclear 37 . Additionally, LR machine afforded a novel method to identify codeine‐containing cough syrup dependence by handling multivariate characteristics consisting of clinical and brain functional connectomes data 38 . As for neuroimaging data, functional network analysis of the entire brain noticed abnormality in diffuse nodes (e.g., bilateral PFC) and global network properties, which were closely associated with clinical manifestations as well as degradation and interrupted connectivity of neuro, respectively 38,85 …”

Section: Supervised Machine Learning In Addicted Neuroimaging Data An...mentioning

confidence: 99%

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

Yang

et al. 2023

Addiction Biology

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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.

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

Ranjbar

Soti

Banazadeh

et al. 2021

Neuroscience & Biobehavioral Reviews

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

Cited by 3 publications

References 43 publications

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

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

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

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

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