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Review
. 2020 Feb 17:6:4.
doi: 10.1186/s42234-020-0040-0. eCollection 2020.

Biomarkers and neuromodulation techniques in substance use disorders

Bettina Habelt  1 , Mahnaz Arvaneh  2 , Nadine Bernhardt  1 , Ivan Minev  2
Affiliations
Review

Biomarkers and neuromodulation techniques in substance use disorders

Bettina Habelt et al. Bioelectron Med. .

Erratum in

Abstract

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.

Keywords: Addiction; Closed-loop systems; DBS; Deep brain stimulation; ERP; Event-related potentials; Flexible electronics; Multimodal neural interfaces; Neural activity; Neurotransmitters; Substance use disorders; Transcranial direct current stimulation; tDCS.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Application of neuroprosthetic devices in SUD. Drug-related stimuli can induce craving and subsequent relapse in drug addicted individuals such as a glass of beer in alcoholics. The loss of inhibitory control leading to alcohol consumption is accompanied by abnormally decreased ERP amplitudes like for N2 and P3. Neuroprosthetic systems could identify and normalise these pathological features through different brain stimulation methods leading to improved behaviour control and decreased relapse risk
See this image and copyright information in PMC

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