Binaural beats utilize a phenomenon that occurs within the cortex when two different frequencies are presented separately to each ear. This procedure produces a third phantom binaural beat, whose frequency is equal to the difference of the two presented tones and which can be manipulated for non-invasive brain stimulation. The effects of binaural beats on working memory, the system in control of temporary retention and online organization of thoughts for successful goal directed behavior, have not been well studied. Furthermore, no studies have evaluated the effects of binaural beats on brain connectivity during working memory tasks. In this study, we determined the effects of different acoustic stimulation conditions on participant response accuracy and cortical network topology, as measured by EEG recordings, during a visuospatial working memory task. Three acoustic stimulation control conditions and three binaural beat stimulation conditions were used: None, Pure Tone, Classical Music, 5Hz binaural beats, 10Hz binaural beats, and 15Hz binaural beats. We found that listening to 15Hz binaural beats during a visuospatial working memory task not only increased the response accuracy, but also modified the strengths of the cortical networks during the task. The three auditory control conditions and the 5Hz and 10Hz binaural beats all decreased accuracy. Based on graphical network analyses, the cortical activity during 15Hz binaural beats produced networks characteristic of high information transfer with consistent connection strengths throughout the visuospatial working memory task.

Objective . Synchronization in activated regions of cortical networks affect the brain’s frequency response, which has been associated with a wide range of states and abilities, including memory. A non-invasive method for manipulating cortical synchronization is binaural beats. Binaural beats take advantage of the brain’s response to two pure tones, delivered independently to each ear, when those tones have a small frequency mismatch. The mismatch between the tones is interpreted as a beat frequency, which may act to synchronize cortical oscillations. Neural synchrony is particularly important for working memory processes, the system controlling online organization and retention of information for successful goal-directed behavior. Therefore, manipulation of synchrony via binaural beats provides a unique window into working memory and associated connectivity of cortical networks. Approach . In this study, we examined the effects of different acoustic stimulation conditions during an N-back working memory task, and we measured participant response accuracy and cortical network topology via EEG recordings. Six acoustic stimulation conditions were used: None, Pure Tone, Classical Music, 5 Hz binaural beats, 10 Hz binaural beats, and 15 Hz binaural beats. Main results . We determined that listening to 15 Hz binaural beats during an N-Back working memory task increased the individual participant’s accuracy, modulated the cortical frequency response, and changed the cortical network connection strengths during the task. Only the 15 Hz binaural beats produced significant change in relative accuracy compared to the None condition. Significance . Listening to 15 Hz binaural beats during the N-back task activated salient frequency bands and produced networks characterized by higher information transfer as compared to other auditory stimulation conditions.

Objective . The objective of this paper is to didactically compare resting state connectivity networks computed using two different methods called phase locking value (PLV) and convergent cross-mapping (CCM). PLV is a ubiquitous measure of connectivity in electrophysiological research but is less often applied to fMRI BOLD timeseries since this model-based metric assumes that oscillatory coupling is a sufficient condition for connectivity. Alternatively, CCM is a model-free method, which detects potentially nonlinear causal influences based on the ability to estimate one timeseries with another and does not assume an oscillatory structure. Approach . We use a toy dataset to test the PLV and CCM algorithms under different known synchronization conditions. Additionally, experimental resting state EEG and fMRI datasets are used for comparison. Main results . The results show that the resting state brain networks computed using both algorithms produce similar results for both resting state EEG and fMRI datasets. For both neuroimaging datasets, the network characteristics follow the same trends and the similarity between the computed networks, for both algorithms, is highly significant. Significance . CCM is able to identify low or one-way connection strengths better than PLV but takes exponentially longer to compute. Based on these results, PLV provides a good metric for on-line network identification because it is both computationally fast and an excellent approximation of the network computed with CCM.

Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP). CP is primarily treated with drugs that can have negative side effects (e.g., opioid addiction), and lose efficacy after long-term use. Electrical stimulation of the spinal cord or peripheral nerves is an alternative therapy that has great potential to reduce the need for drugs and has fewer negative side effects; but has been associated with suboptimal efficacy because its modulation mechanisms are unknown. Critical to advancing CP treatment is a deeper understanding of how pain is processed in the superficial and deep layers of the dorsal horn (DH), which is the first central relay station for pain processing in the spinal cord. Mechanistic models of the DH have been developed to investigate modulation mechanisms but are non-linear and high-dimensional and thus difficult to analyze. In this paper, we construct a tractable computational model of the DH in rats from LFP recordings of the superficial layer network and spiking activity of WDR neurons in the deep layer. By combining a deterministic linear time-invariant model with a stochastic point process model, we can accurately predict responses of the DH circuit to electrical stimulation of the peripheral nerve. The model is computationally efficient, low-dimensional, and able to capture the stochastic nature of neuronal dynamics in the DH; and is a first step in developing new therapies for CP.