Lab Talk

Opiate Addiction in the EEG

Opiate addiction has escalated to crisis levels, however there are few EEG studies that provide insight into the impact on the human brain and the results are inconsistent.

The Addicted Brain.

Addiction is characterized by lack of control over a particular behavior, where seeking out the addicted substance or activity becomes compulsive, taking control of you rather than the other way around. Some are borne of habits that start off very innocently, but subsequently develop into an unstoppable force. Others are faster to take hold, emerging simply from the powerful effect of a substance on the circuitry that goes beyond normal reward response to induce cravings that lead to self destructive behaviors.  Opiates for example, are powerfully addictive leading to a modern day crisis and raise many questions.

For example:  What is different about the brain activity of addicted people and of those more prone to addiction compared to the normal non-addicted folk?  And are all ‘addictions’ the same? For example, can you be just more addiction prone in general, or can they be substance or activity specific?

Surprisingly, however, the literature on opiates with respect to the brain is profoundly dominated by studies of how they impact molecular and cellular process in brain tissue derived from rats and mice. When it comes to human studies of the brain, it is exceptionally sparse.

Nonetheless, we summarize here what we could find, focusing on the resting-state EEG studies.

A Summary of Resting State EEG Studies

Various studies have looked at spectral characteristics of the resting state EEG in substance addicted people versus controls (others who are not addicted to a substance),  typically focused on opioids, nicotine or alcohol. An early study in 1976 by Lenn et al., looked at the effect of the synthetic opiate methadone on the resting eyes closed EEG and reported a shift in the alpha peak – those taking methadone had lower alpha peak frequencies on average.

More recently researchers Wang et al at the University of Auckland in New Zealand looked at segments or ranges of the power spectral density or PSD  (defined by the names delta, theta, alpha, beta and gamma) in opiate users (of a variety of opiates). Their study along with another follow on publication reported higher power in all frequency ranges in opiate users compared to healthy controls, particularly for the higher frequency beta and gamma activity. The difference was pronounced across all regions of the brain, though more prominent in the parieto-occipital and frontal regions (though the statistics were a little sparse – no error bars on some of the graphs for example – so the overlap between the two groups was not evident.  See the control issue). There were also a number of confounds since the drug users had a significantly lower education and employment compared to the healthy controls which could account in part for the differences observed.

A more interesting result however, was that users of the synthetic opiate Methadone, often used as a milder replacement to wean addicts, was more similar to the opiate group but in between the healthy controls and opiate users. This pattern suggests that at least some of the differences between the groups may be opiate induced and calls for a more in depth analytical investigation of the EEG in opiate users relative to non addicts.

Confounding Results

Other studies have also used resting-state EEG to explore the shifts in power in opiate users across the different  frequency bands, as well exploring changes in the pattern of functional connectivity. However the relatively small sample sizes and heterogeneity across study groups means that they present an inconsistent picture with opposite results in some cases. A summary of recent papers is shown below (taken from Leong & Yuan 2017).

 

In general the studies were conducted with small sample sizes of 15 to 30 people and heterogeneous study populations, for example including varied profiles of opiate users some with multiple substance abuse issues and other stressors. Thus these results could in large part reflect large individual variability or confounds created by differences in the methodology or addiction profiles. These limitations, conceded by the authors of these studies, reinforces the need for further research.

Moving Forward in Addiction Research

Addiction is an enormously complex phenomenon, substances vary, individual profiles vary and trajectories are different. Consequently larger sample sizes and carefully captured profiles of addiction and other life factors are essential to begin to tease out the differences and understand the phenomenon.

What’s more, being able to separate the brain’s propensity for addiction from the actual effects of the addicted substance itself, requires longitudinal studies which can track at-risk individuals over time. The use of mobile EEG in real-time, outside the lab environment, could also enable more accurate tracking of the different phases of addiction – from craving to withdrawal to recovery (or relapse) – as well as helping to determine the targeting and effectiveness of different treatment interventions.

Finally, although looking at regional shifts in the major bands of the power spectrum can provide some directional pointers, this is has limitations as spectral changes of this nature are observed under a wide variety of cognitive an behavioral circumstances unrelated to addiction.  Bringing to bear a wider array of analytical approaches to extract a broader range of signal and network features could provide could deliver deeper insights.  Such efforts could also be a path to developing diagnostic and treatment monitoring tools.

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