Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

Khajehpour, Hassan; Makkiabadi, Bahador; Ekhtiari, Hamed; Bakht, Sepideh; Noroozi, Alireza; Mohagheghian, Fahimeh

doi:10.1371/journal.pone.0226249

Cited by 23 publications

(20 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When these regions are impaired, the executive control systems may be affected as demonstrated by Volkow et al ( 2015 ) by specific impairments within the executive brain networks in MA addicts during the exposure to drug-associated cues. Khajehpour et al ( 2019 ) also reported that MA abusers differed in the gamma band in the paracentral lobule. It may be speculated that the substance-dependent individuals are unable to control their addiction-related behaviors (Khajehpour et al, 2019 ).…”

Section: Discussionmentioning

confidence: 96%

See 1 more Smart Citation

Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform

et al. 2021

View full text Add to dashboard Cite

Few studies have investigated the functional patterns of methamphetamine abstainers. A better understanding of the underlying neurobiological mechanism in the brains of methamphetamine abstainers will help to explain their abnormal behaviors. Forty-two male methamphetamine abstainers, currently in a long-term abstinence status (for at least 14 months), and 32 male healthy controls were recruited. All subjects underwent functional MRI while responding to drug-associated cues. This study proposes to combine a convolutional neural network with a short-time Fourier transform to identify different brain patterns between methamphetamine abstainers and controls. The short-time Fourier transformation provides time-localized frequency information, while the convolutional neural network extracts the structural features of the time–frequency spectrograms. The results showed that the classifier achieved a satisfactory performance (98.9% accuracy) and could extract robust brain voxel information. The highly discriminative power voxels were mainly concentrated in the left inferior orbital frontal gyrus, the bilateral postcentral gyri, and the bilateral paracentral lobules. This study provides a novel insight into the different functional patterns between methamphetamine abstainers and healthy controls. It also elucidates the pathological mechanism of methamphetamine abstainers from the view of time–frequency spectrograms.

show abstract

Section: Discussionmentioning

confidence: 96%

“…Khajehpour et al ( 2019 ) also reported that MA abusers differed in the gamma band in the paracentral lobule. It may be speculated that the substance-dependent individuals are unable to control their addiction-related behaviors (Khajehpour et al, 2019 ).…”

Section: Discussionmentioning

confidence: 96%

Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform

et al. 2021

View full text Add to dashboard Cite

show abstract

“…MDMA acts via serotonergic receptors and may cause disturbances of serotonergic pathways (Benningfield and Cowan, 2013) and electrophysiological changes that reflect recruitment of additional resources to perform cognitive tasks (Roberts et al, 2018). The other psychoactive substances used among PU, such as cocaine, amphetamine, and others may interact in a complex way and cumulative effect may impact the further increased brain activity (Zilverstand et al, 2018;Khajehpour et al, 2019). Still, little is known about the interactive impact of various illicit drugs on the human brain (Gouzoulis-Mayfrank and Daumann, 2006;Van Dam et al, 2008;Shevlin et al, 2017), regardless of the observed high frequency of polydrug use pattern.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, hair sample analysis was not available for eight participants from the control group. However, it is important to note that biological measures of drug use (illicit substances or psychotropic medication) is rarely used in nonusing controls in the research field on cannabis (or other illicit drugs) and self-reported measures dominate for CU (Smith et al, 2017;Khajehpour et al, 2019;Rangel-Pacheco et al, 2020) or the urine/hair sample analyses detect cannabis use only (no other illicit drugs nor psychotropic medication) (Yücel et al, 2016;Prashad et al, 2018). That is why we would consider it as an additional value of our study and exclusion of this participants form analyses would make CG too small to obtain reliable results.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Not Just a Pot: Visual Episodic Memory in Cannabis Users and Polydrug Cannabis Users: ROC and ERP Preliminary Investigation

Binkowska

Jakubowska

Gaca

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Background While research has consistently identified an association between long-term cannabis use and memory impairments, few studies have examined this relationship in a polydrug context (i.e., when combining cannabis with other substances).Aims: In this preliminary study, we used event-related potentials to examine the recognition process in a visual episodic memory task in cannabis users (CU) and cannabis polydrug users (PU). We hypothesized that CU and PU will have both–behavioral and psychophysiological–indicators of memory processes affected, compared to matched non-using controls with the PU expressing more severe changes.Methods 29 non-using controls (CG), 24 CU and 27 PU were enrolled into the study. All participants completed a visual learning recognition task while brain electrical activity was recorded. Event-related potentials were calculated for familiar (old) and new images from a signal recorded during a subsequent recognition test. We used receiver operating characteristic curves for behavioral data analysis.Results The groups did not differ in memory performance based on receiver operating characteristic method in accuracy and discriminability indicators nor mean reaction times for old/new images. The frontal old/new effect expected from prior research was observed for all participants, while a parietal old/new effect was not observed. While, the significant differences in the late parietal component (LPC) amplitude was observed between CG and PU but not between CG and CU nor CU and PU. Linear regression analysis was used to examine the mean amplitude of the LPC component as a predictor of memory performance accuracy indicator. LPC amplitude predicts recognition accuracy only in the CG.Conclusion The results showed alterations in recognition memory processing in CU and PU groups compared to CG, which were not manifested on the behavioral level, and were the most prominent in cannabis polydrug users. We interpret it as a manifestation of the cumulative effect of multiple drug usage in the PU group.

show abstract

“…Newton et al (2003) showed 78 that the delta/δ and theta/θ bands, but not others, were elevated almost globally in the cortical subregions (Newton et al, 2003). The findings were partly supported by Khajehpour et al (2019), showing that delta/δ and gamma/γ powers were slightly, yet significantly, increased in a topographic analysis (Khajehpour et al, 2019). Alcohol is 82 believed to be inhibitory, mimicking GABA's effect on postsynaptic GABAA receptors (Olsen & Liang, 2017).…”

mentioning

confidence: 86%

Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine- and alcohol-use disorders

Minnerly

Shokry²,

To³

et al. 2021

Preprint

View full text Add to dashboard Cite

Electroencephalography (EEG) likely reflects activity of cortical neurocircuits, making it an insightful estimation for mental health in patients with substance use disorder (SUD). EEG signals are recorded as sinusoidal waves, containing spectral amplitudes across several frequency bands with high spatio-temporal resolution. Prior work on EEG signal analysis has been made mainly at individual electrodes. These signals can be evaluated from advanced aspects, including sub-regional and hemispheric analyses. Due to limitation of computational techniques, few studies in earlier work could conduct data analyses from these aspects. Therefore, EEG in patients with SUD is not fully understood. In the present retrospective study, spectral powers from a data house containing opioid (OUD), methamphetamine/stimulants (MUD), and alcohol use disorder (AUD) were extracted, and then converted into five distinct topographic data (i.e., electrode-based, cortical subregion-based, left-right hemispheric, anterior-posterior based, and total cortex-based analyses). We found that EEG spectral powers in patients with OUD were significantly different from those with MUD or AUD. Differential changes were observed from multiple perspectives, including individual electrodes, subregions, hemispheres, anterior-posterior cortices, and across the cortex as a whole. Understanding the differential changes in EEG signals may be useful for future work with machine learning and artificial intelligence (AI), not only for diagnostic but also for prognostic purposes in patients with SUD.

show abstract

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

Cited by 23 publications

References 77 publications

Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform

Identifying Methamphetamine Abstainers With Convolutional Neural Networks and Short-Time Fourier Transform

Not Just a Pot: Visual Episodic Memory in Cannabis Users and Polydrug Cannabis Users: ROC and ERP Preliminary Investigation

Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine- and alcohol-use disorders

Contact Info

Product

Resources

About