Altered Functional Connectivity Strength in Abstinent Chronic Cocaine Smokers Compared to Healthy Controls

Ray, Suchismita; Gohel, Suril; Biswal, Bharat B.

doi:10.1089/brain.2014.0240

Cited by 26 publications

(23 citation statements)

References 47 publications

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“…Increased FC was observed in the right angular gyrus and right SPG of BQ dependent individuals. Our results are similar to other cocaine studies that have demonstrated increased FC between frontal-temporal and frontal-parietal brain regions of abstinent chronic cocaine users (89). Similarly, compared to people who have never smoked and former nicotine smokers, current smokers had greater connectivity in the right superior parietal lobe located in the dorsal attention network (90).…”

Section: Differences Of Functional Connectivity In Chronic Bq Users Asupporting

confidence: 90%

Acute and Chronic Effects of Betel Quid Chewing on Brain Functional Connectivity

et al. 2020

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Background: The active alkaloid in Betel quid is arecoline. Consumption of betel quid is associated with both acute effects and longer-term addictive effects. Despite growing evidence that betel quid use is linked with altered brain function and connectivity, the neurobiology of this psychoactive substance in initial acute chewing, and long-term dependence, is not clear.Methods: In this observational study, functional magnetic resonance imaging in a restingstate was performed in 24 male betel quid-dependent chewers and 28 male controls prior to and promptly after betel quid chewing. Network-based statistics were employed to determine significant differences in functional connectivity between brain networks for both acute effects and in long-term betel users versus controls. A support vector machine was employed for pattern classification analysis.Results: Before chewing betel quid, higher functional connectivity in betel quid-dependent chewers than in controls was found between the temporal, parietal and frontal brain regions (right medial orbitofrontal cortex, right lateral orbital frontal cortex, right angular gyrus, bilateral inferior temporal gyrus, superior parietal gyrus, and right medial superior frontal gyrus). In controls, the effect of betel quid chewing was significantly increased functional connectivity between the subcortical regions (caudate, putamen, pallidum, and thalamus), and the visual cortex (superior occipital gyrus and right middle occipital gyrus).Conclusion: These findings show that individuals who chronically use betel quid have higher functional connectivity than controls of the orbitofrontal cortex, and inferior temporal and angular gyri. Acute effects of betel quid are to increase the functional connectivity of some visual cortical areas (which may relate to the acute symptoms) and the basal ganglia and thalamus.

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Section: Differences Of Functional Connectivity In Chronic Bq Users Asupporting

confidence: 90%

Acute and Chronic Effects of Betel Quid Chewing on Brain Functional Connectivity

et al. 2020

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“…The duration of cocaine use was associated with lower rsFC between ventral tegmental area and thalamus/lentiform nucleus/nucleus accumbens [26], greater short-range and long-range functional connectivity density in the regions of the default mode network (DMN) [27], lower intra-network connectivity strength of sensory motor cortex [28], and greater inter-network connectivity strength between occipital-limbic brain regions [28]. Greater peak cocaine level was associated with greater rsFC between caudate and thalamus and between amygdala and insula, and with lower rsFC between amygdala and cerebellum [22].…”

Section: Relationship Between Brain Connectivity and Behavior Drug Umentioning

confidence: 99%

“…Greater recent cocaine use was associated with greater rsFC between striatum and DLPFC [23]. Greater cocaine use frequency and money spent on cocaine per week in abstinent CDs was associated with lower connectivity strength within occipital brain regions [28]. …”

Section: Relationship Between Brain Connectivity and Behavior Drug Umentioning

confidence: 99%

Effect of cocaine dependence on brain connections: clinical implications

Steinberg

Moeller

et al. 2015

Expert Review of Neurotherapeutics

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Cocaine dependence (CD) is associated with several cognitive deficits. Accumulating evidence, based on human and animal studies, has led to models for interpreting the neural basis of cognitive functions as interactions between functionally related brain regions. In this review, we focus on the magnetic resonance imaging (MRI) studies using brain connectivity techniques as related to CD. The majority of these brain connectivity studies indicated that cocaine use is associated with altered brain connectivity between different structures, including cortical-striatal regions and default mode network. In cocaine users, some of the altered brain connectivity measures are associated with behavioral performance, history of drug use, and treatment outcome. The implications of these brain connectivity findings to the treatment of CD and the pros and cons of the major brain connectivity techniques are discussed. Finally potential future directions in cocaine use disorder research using brain connectivity techniques are briefly described.

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“…As seen from Table 2, the t value of node ORBinf.L, CAU.L, MTG.L, PUT, PAL and PCG.R are less than 0 in patients, which indicates that activation level of these regions are less than those in normal group, and the functional connection strength of patient is relatively small. The value of node PoCG.R, IPL.L, PCUN.R, PreCG and PCG.L are greater than 0, this shows that the activation of this part of the patient's [26][27][28][29][30] region is higher than the normal, and the functional connection strength is relatively large. As can be seen from Table 2, the Precuneus's statistical value is large, which shows that the degree of activation in the region has a greater degree of weakening of patient.…”

Section: Discussionmentioning

confidence: 99%

Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

Zhuqing¹,

Huan²,

Zhao³

2017

J Health Med Inform

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Aiming to study the local functional structure of brain function network in resting state, the Fuzzy C-means (FCM) algorithm is adapted to modular the brain functional network. Then the nodes in module are connected into a network using correlation between the time series extracted from Functional Magnetic Resonance Imaging (fMRI) data. Afterwards node degree, clustering coefficient and shortest path length are used to analyse the functional characteristics of networks. Finally, the differences in activation between patients and normal controls' brain regions are compared through Amplitude of Low Frequency Fluctuation (ALFF). Experimental results demonstrate that, the shortest path length of the patient is smaller than that of the normal human, so the information transmission rate increases. Clustering coefficient is higher than the normal, and the degree of grouping of network is enhanced. The correlation between the patient nodes is generally greater than the normal, and there is a weakened situation in the local area. It also found that the proportion of region for the activation level higher than the average of the whole brain in normal with more than the patient. In particular, the activation level of the Precentral gyrus (PreCG) and other regions in patient has a large degree decline. And the activation level in left Caudate nucleus (CAU.L), the lenticular nucleus, Putamen (PUT) and the lenticular nucleus, Palladium (PAL) and other regions is increased for patient. The research results verify the feasibility of modularization analysis of brain functional network using algorithm and correlation in resting state. The main idea of the k-means is the minimization of an objective function, which is normally chosen to be the total distance between all patterns from their respective cluster centres. Its solution relies on an iterative scheme, which starts with arbitrarily chosen initial cluster memberships or centres. The distribution of objects among clusters and the updating of cluster centres are the two main steps of the k-means algorithm [15]. The algorithm alternates between these two steps until the value of the objective function cannot be reduced anymore. Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting StateZhuqingFCM Clustering is a soft version of k-means, where each data point has a fuzzy degree of belonging to each cluster [16]. The FCM algorithm for vector set is a clustering technique that aims to partitioning a set of measured vector xi (i=1,2,...,n) into Gi (i=1,2,...,c) clusters, the main result is the minimization of an objective function J(U,G) with respect to a fuzzy partition matrix U and a set of prototypes G through cluster centre of each cluster.Where d (xk-Gi) represents a universal distance function. Corresponding to the fuzzy partition, elements value of U= [uij]c×n is allowed 0 to 1. However, with the normalization rule, the membership of cluster is equal to oneWhen the Euclidean distance is chosen as the non-similar...

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Altered Functional Connectivity Strength in Abstinent Chronic Cocaine Smokers Compared to Healthy Controls

Cited by 26 publications

References 47 publications

Acute and Chronic Effects of Betel Quid Chewing on Brain Functional Connectivity

Acute and Chronic Effects of Betel Quid Chewing on Brain Functional Connectivity

Effect of cocaine dependence on brain connections: clinical implications

Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

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