Major Depressive Disorder (MDD) is a psychiatric disorder characterized by high comorbidity with cardiovascular disease. Furthermore, a combination of high heart rate (HR) and low heart rate variability (HRV) has been frequently reported in depressed patients. The present review proposes a frontal-vagal (brain-heart) network that overlaps with functional nodes of the depression network. Moreover, we summarize neuromodulation studies that have targeted key nodes in this depression network, with subsequent impact on heart rate (HR) or heart-rate-variability (HRV), such as the dorsolateral prefrontal cortex (DLPFC), subgenual anterior cingulate cortex (sgACC), and the vagus nerve (VN). Based on the interplay of this frontal-vagal network, we emphasize the importance of including HR and HRV measurements in human depression studies, in particular those that conduct neuromodulation, in order to obtain a better understanding of the pathways that are affected, and we explore the possibilities of using this frontal-vagal interplay as a method for target engagement in neuromodulation treatments. This frontal-vagal network theory opens-up the possibility for individualizing neuromodulation treatments such as rTMS. A recent development called Neuro-Cardiac-Guided TMS (NCG-TMS), was developed based on this theory, and an individual-participant meta-analysis is presented. Four studies provide consistent and replicable support for NCG-TMS as a target engagement method, with consistent HR deceleration during frontal TMS and HR acceleration during motor strip TMS.

Previous research has highlighted the role of the excitation/inhibition (E/I) ratio for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency transcranial random noise stimulation (tRNS)—an excitatory form of neurostimulation—on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n = 102) received either sham stimulation or 20-minute tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous magnetic resonance spectroscopy (MRS)-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG versus MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

Previous work has shown relations between domain-general processes, domain-specific processes, and mathematical ability. However, the underlying neurophysiological effects of mathematical ability are less clear. Recent evidence highlighted the potential role of beta oscillations in mathematical ability. Here we investigate whether domain-general (working memory) and domain-specific (number sense) processes mediate the relation between resting-state beta oscillations and mathematical ability, and how this may differ as a function of development (children vs. adults). We compared a traditional analysis method normally used in EEG studies with a more recently developed parameterization method that separates periodic from aperiodic activity. Regardless of methods chosen, we found no support for mediation of working memory and number sense, neither for children nor for adults. However, we found subtle differences between the methods. Additionally, we showed that the traditional EEG analysis method conflates periodic activity with aperiodic activity; in addition, the latter is strongly related to mathematical ability and this relation differs between children and adults. At the cognitive level, our findings do not support previous suggestions of a mediation of working memory and number sense. At the neurophysiological level our findings suggest that aperiodic, rather than periodic, activity is linked to mathematical ability as a function of development.

Previous research has highlighted the role of the excitation/inhibition ratio (E/I) for typical and atypical development, mental health, cognition, and learning. Other research has highlighted the benefits of high-frequency random noise stimulation (tRNS)—an excitatory form of neurostimulation—on learning. We examined the E/I as a potential mechanism and studied whether tRNS effect on learning depends on E/I as measured by the aperiodic exponent as its putative marker. In addition to manipulating E/I using tRNS, we also manipulated the level of learning (learning/overlearning) that has been shown to influence E/I. Participants (n=102) received either sham stimulation or 20 min tRNS over the dorsolateral prefrontal cortex (DLPFC) during a mathematical learning task. We showed that tRNS increased E/I, as reflected by the aperiodic exponent, and that lower E/I predicted greater benefit from tRNS specifically for the learning task. In contrast to previous Magnetic Resonance Spectroscopy-based E/I studies, we found no effect of the level of learning on E/I. A further analysis using a different data set suggest that both measures of E/I (EEG vs MRS) may reflect, at least partly, different biological mechanisms. Our results highlight the role of E/I as a marker for neurostimulation efficacy and learning. This mechanistic understanding provides better opportunities for augmented learning and personalized interventions.

Electroencephalography (EEG) provides valuable insights into brain development, but collecting high-quality data can be tedious, limiting its usability with children. This study evaluates the feasibility and reliability of EEG data acquisition in children with a wireless consumer-grade EEG headset (EMOTIV EPOC X), by comparing it to a research-grade system (BioSemi ActiveTwo), with a focus on aperiodic brain activity. The portability of the EMOTIV headset allows for EEG data collection in ecologically valid, real-world settings such as schools, enabling novel insights into brain activity during learning. We recorded EEG from 93 children (aged 9–10 years) using the EMOTIV headset, beginning with a 4-min resting-state measurement, followed by assessments of mathematical ability, visuospatial working memory, and verbal working memory, in a classroom environment. Aperiodic activity, thought to reflect fundamental aspects of neural excitability and cognitive processing, was extracted and its reliability compared across the two EEG systems. We further tested whether aperiodic activity recorded with EMOTIV predicts mathematical ability, replicating earlier research using research-grade EEG equipment. Our findings reveal that, similar to earlier findings, lower aperiodic activity was associated with higher math performance, supporting its role as a neural marker of cognitive ability. These results demonstrate the feasibility and reliability of using a consumer-grade mobile EEG headset to investigate individual differences in cognitive development in naturalistic contexts. This work opens up new opportunities for large-scale, school-based neurocognitive assessments and paves the way for personalized educational approaches based on neural profiles.