A Neural Signature of Drug Craving in Methamphetamine Users

Post by Christopher Chen

The takeaway

By measuring brain activity and applying machine learning to detect patterns, researchers have identified a neurobiological signature of drug craving in methamphetamine use disorder (MUD). These findings represent a significant advancement toward developing personalized and effective therapeutic interventions for MUD and other substance use disorders. 

What's the science?

Whether a person becomes addicted to a drug or suffers from drug relapse is strongly grounded in the neurobiological mechanisms of craving and drug cue reactivity. Therefore, researchers have focused on developing treatments that target craving to reduce drug use.

Machine learning tools can be used to develop predictive models correlating brain activity and drug craving. In combination with EEG and behavioral data, machine learning has previously helped classify individuals with MUD and healthy controls with over 90% accuracy. However, while these studies demonstrated promising results, they used conventional low-density EEG. Conventional EEGs at the 32- and 64-channel scale lack the specificity to localize the source of certain brain signals. High-density (128 channels or higher) EEG can better assess the regional and global brain activity related to craving in MUD.

In a recent article in Cell Reports Medicine, Tian and colleagues leveraged high-density resting-state EEG and machine learning to investigate the neurophysiological signatures of MUD. Ultimately, this study aimed to identify individual-level functional connectivity in individuals with and without MUD in the hopes of generating reliable biomarkers that could be used to predict MUD.

How did they do it?

Researchers generated brain functional connectivity networks (FCNs), essentially visualizations of synchrony between brain regions, using data from resting-state high-density EEG from individuals with MUD and healthy controls (HC) who watched a 5-minute video depicting various scenarios of methamphetamine use. The data included 465 region of interest (ROI) pairs, spanning different frequency bands (delta, theta, alpha, beta, and gamma) and two resting conditions (eyes closed and eyes open).

The researchers quantified FCNs using a measurement called imaginary coherence (iCoh), a measurement of the synchrony between EEG signals from two channels. After characterizing FCNs in each subject, researchers used a machine learning technique called a relevance vector machine (RVM) to build models to predict craving scores for individuals with MUD. To further validate these predictive models, researchers applied them to another resting-state EEG dataset of 44 different individuals with MUD.

Additionally, the researchers were interested in seeing whether their models could distinguish between individuals with MUD and HC. They applied the RVM model to classify individuals with MUD and HC using FCNs from various brain signaling frequency bands and resting conditions. Finally, they compared the predictive capacity of their models with models derived from another well-known marker of brain activity called EEG spectral power. 

What did they find?

EEG-tailored machine learning models pinpointed crucial brain regions like the medial prefrontal cortex (mPFC), angular gyrus, orbital gyrus, and insula, along with their connections, as critical in mediating craving — findings that align with previous MUD studies. Interestingly, there were also unique connections at certain frequencies in the brain — delta and beta bands — that correlated with craving, suggesting these may be potential therapeutic targets. 

The researchers found that the most robust biomarker for MUD came from activity networks in the beta frequency, while participants had their eyes open (REO beta). Using data from REO beta conditions, the models exhibited the strongest predictive capabilities for cue-induced craving, correlating well with craving in individuals with MUD. Importantly, the prediction was prospectively replicated in an independent EEG dataset. The model also effectively identified abnormalities in MUD individuals, bettering previous methods by associating brain activities and interactions with MUD using source localization and iCoh. REO beta models were also best at classification performance across all frequency bands and resting conditions, expressing over 80% accuracy in determining whether an individual had MUD.  

What's the impact?

The results illustrate the effectiveness of integrating advanced brain imaging techniques with machine learning tools in identifying robust neurobiological biomarkers for drug addiction. Furthermore, the study successfully developed replicable predictive models for craving and showed that FCNs are potent measurement tools for characterizing brain activity related to drug craving. Looking ahead, these insights underscore the potential of leveraging similar combinations of imaging and AI-driven techniques to create more personalized and effective therapeutics for alleviating MUD and other drug-use disorders. 

The Development of Consciousness in Infants

Post by Laura Maile

What is consciousness?

Scientists have long attempted to understand where consciousness resides, whether it involves a network of brain areas, and when in development it emerges. Current theories of human consciousness state that consciousness develops as the brain becomes capable of integrating information, making us aware of ourselves and our environment. There are many different theories, however, on how and where consciousness is represented. Higher-order theories, for example, require that one be able to represent an external experience in the mind, and place importance on the prefrontal cortex. In contrast, integrated information theory places more emphasis on posterior cortical areas, and rests on the ability of the brain to integrate different stimuli to generate information by the whole. In general, it is agreed that consciousness is represented in the brain, likely as an integration of signals across multiple brain areas.  

How do you measure consciousness?

It is of critical importance to develop and agree upon measures of consciousness, specifically for infants, as they are unable to follow directions or communicate verbally. Infants do possess the ability to respond to stimuli such as the sound of their mother’s voice, different facial expressions, and noxious stimuli. Their responses can be measured both behaviorally, through limb withdrawal, facial grimacing, eye movement, vocal and sucking activity, and physiologically, with changes in heart rate and neural activity in brain areas that respond to environmental stimuli. Most current theories of consciousness are based on physical processes that can be measured via electroencephalogram (EEG) recordings and fMRI, and behavioral indicators of consciousness, such as the capacity to respond to environmental stimuli. fMRI studies, which record high-resolution hemodynamic activity in the brain that respresents neural activity, have identified cortical “hubs” and networks of brain regions that are active during different activities and states. By studying brain activity across different states of development, scientists have determined how functional networks change over time. Conceptually similar to EEG, which uses a net of electrodes fitted to the skull to record brain activity, magnetoencephalography (MEG) is a less invasive alternative that can be used to measure fetal brain activity. 

When does consciousness emerge?

There are conflicting theories about when exactly consciousness appears during development. Some theories, for example, require that individuals have a sense of self and an understanding of their own mental state in order to be qualified as “conscious,” which would mean that humans are not conscious until some time after their first birthday. Other recent evidence indicates that consciousness may appear in early infancy or even before birth, as soon as thalamocortical activity appears in the brain at about 24-26 weeks gestation. Rather than base our understanding of the emergence of consciousness on a specific theory or set of conflicting theories, some scientists suggest that the field measure markers of consciousness in adults and observe when they first emerge in infants. 

Activity in the brain shows that the primary cortical areas that process vision, auditory, and sensorimotor information are active in response to external stimuli at birth, indicating that newborn babies can process many sensory inputs. Some of these areas are also present and active prior to birth in the developing fetus. Those brain areas involved in more complex processes such as attention, executive function, and memory appear less complex at birth and develop over the first two years of life. Some specific functional activity networks have been linked to the capacity for or recovery of consciousness after injury in adults. Three of these networks, the default mode network, dorsal activity network, and executive control network, have recently been identified as distinct and functional networks in newborn babies.  

Behavioral data shows that shortly after birth, infants can process auditory and visual inputs that allow them to recognize their mother’s voice, show sensitivity to music, and even show preference for their native language. Visual acuity is low at birth, but brain imaging data shows responses in the visual pathway to distinct visual inputs at two months of age. While most perceived senses expand and develop as the infant ages, there is also data indicating that young infants aged 4-6 months can perceive more distinct sounds and faces than older infants and adults. Additionally, multisensory integration that requires conscious perception of individual stimuli has been demonstrated in 4-5 month old infants. 

What's next?

The study of infant consciousness has become an increasingly important and studied topic in the field of consciousness research. Continued improvement in methods to measure brain activity and other markers of consciousness in fetuses and infants is needed. A more complete understanding of the neural correlates and functional basis of consciousness will also require a tightening of the many theories of consciousness into one universally accepted theory. 

The takeaway

A definitive answer on when human consciousness begins has yet to be identified, but the increase in studies on conscious experience in infants and preterm fetuses is bringing us closer to one. Recent evidence points to an earlier onset of consciousness than was previously described in human infants, indicating some level of consciousness is present at birth, and potentially even in the late stages of gestation when brain activity and behavioral responses to external stimuli can be measured. 

References +

Bayne, T et al., Consciousness in the cradle: on the emergence of infant experience. Trends in Cognitive Sciences. 2023.

Padilla, N et al., Making of the mind. Acta Paediatrica. 2020.

Seth, AK et al., Theories of consciousness. Nature Reviews Neuroscience. 2022.

Alzheimer’s Disease Creates a Positive Feedback Loop of Excitatory Signaling in Brain Networks

Post by Rebecca Hill

The takeaway

Beta-amyloid and tau are proteins that aggregate in two distinct networks in the brain in Alzheimer’s disease. When there are higher beta-amyloid and tau protein levels, the neuronal networks switch from inhibitory to excitatory signaling, leading to a positive feedback loop of excitation and further protein build-up.

What's the science?

In individuals affected by Alzheimer’s disease, the disruption of normal brain functioning is linked to a build-up of b-amyloid and tau proteins. Early in the progression of Alzheimer’s disease, beta-amyloid proteins build up in the association cortex (the default mode network, also known as the DMN), which is usually active when a person is daydreaming or at rest. Tau proteins on the other hand tend to appear in the entorhinal cortex (in the medial temporal lobe, also known as the MTL), which mainly facilitates memory processing. It’s still unclear why this protein aggregation occurs in two different networks in the brain. This week in Neuron, Giorgio and colleagues investigated this difference in protein aggregation by examining how these two different networks in the brain interact using brain imaging.

How did they do it?

The authors recruited 66 individuals (45 older adults without symptoms of Alzheimer’s and 21 young adults) for this study. A subset of these individuals had evidence of beta-amyloid and tau protein build-up reminiscent of early-stage Alzheimer’s disease. The authors asked participants whether an image presented to them was new or repeated while undergoing brain imaging to measure activity in the association and entorhinal cortices. Typically, neuronal activity is suppressed when viewing an image that has already been seen before. However, previous research has found that Alzheimer’s patients cannot suppress neuronal activity well during these tasks. The authors used functional MRI (fMRI) to measure brain activity and PET scans to measure the amount of beta-amyloid and tau proteins present in the two brain regions.

What did they find?

For participants with low levels of b-amyloid and tau proteins, there was inhibitory signaling between the DMN and the MTL for repeated images, indicating normal brain functioning with an ability to suppress neuronal activity. However, for participants whose protein levels were higher in the two brain regions, there was excitatory signaling instead. This indicates that Alzheimer’s disease alters normal brain functioning by causing neurons to fire excitatory signals from each brain region to the other. As more beta-amyloid builds up in these networks, there is more excitatory signaling to other networks, further increasing the amount of tau building up in the other networks. This creates a positive feedback loop of excitation between these networks, leading to further build-up of beta-amyloid and tau proteins.

What's the impact?

This study is the first to show how Alzheimer’s disease develops in the association and entorhinal cortices by creating a positive feedback loop of excitatory neuronal signaling. This causes abnormal brain functioning in individuals with Alzheimer’s disease by preventing their ability to suppress brain activity in response to new stimuli. Understanding how Alzheimer’s disease develops may help with our ability to diagnose and treat individuals affected by it. 

Access the original scientific publication here.