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Testing HCN1 channel dysregulation in the prefrontal cortex using a novel piezoelectric silk neuromodulator

Mathew et al. | May 05, 2021

Testing HCN1 channel dysregulation in the prefrontal cortex using a novel piezoelectric silk neuromodulator

Although no comprehensive characterization of schizophrenia exists, there is a general consensus that patients have electrical dysfunction in the prefrontal cortex. The authors designed a novel piezoelectric silk-based implant and optimized electrical output through the addition of conductive materials zinc oxide (ZnO) and aluminum nitride (AlN). With further research and compatibility studies, this implant could rectify electrical misfiring in the infralimbic prefrontal cortex.

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Starts and Stops of Rhythmic and Discrete Movements: Modulation in the Excitability of the Corticomotor Tract During Transition to a Different Type of Movement

Lim et al. | Aug 27, 2018

Starts and Stops of Rhythmic and Discrete Movements: Modulation in the Excitability of the Corticomotor Tract During Transition to a Different Type of Movement

Control of voluntary and involuntary movements is one of the most important aspects of human neurological function, but the mechanisms of motor control are not completely understood. In this study, the authors use transcranial magnetic stimulation (TMS) to stimulate a portion of the motor cortex while subjects performed either discrete (e.g. throwing) or rhythmic (e.g. walking) movements. By recording electrical activity in the muscles during this process, the authors showed that motor evoked potentials (MEPs) measured in the muscles during TMS stimulation are larger in amplitude for discrete movements than for rhythmic movements. Interestingly, they also found that MEPs during transitions between rhythmic and discrete movements were nearly identical and larger in amplitude than those recorded during either rhythmic or discrete movements. This research provides important insights into the mechanisms of neurological control of movement and will serve as the foundation for future studies to learn more about temporal variability in neural activity during different movement types.

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Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Wang et al. | Feb 23, 2015

Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.

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Determining the Effects of Voice Pitch on Adolescent Perception, Subconscious Bias, and Marketing Success Using Electroencephalography

Guan et al. | Apr 28, 2021

Determining the Effects of Voice Pitch on Adolescent Perception, Subconscious Bias, and Marketing Success Using Electroencephalography

Voice pitch affects perceived authoritativeness, competency, and leadership capacity. In this study, the authors suggest that examining certain measures of brain activity collected using an affordable EEG could predict advertising effectiveness, which may be invaluable in future neuromarketing research. Understanding voice pitch and other factors that cause implicit bias may allow significant advances in marketing, facilitating business success.

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Open Source RNN designed for text generation is capable of composing music similar to Baroque composers

Goel et al. | May 05, 2021

Open Source RNN designed for text generation is capable of composing music similar to Baroque composers

Recurrent neural networks (RNNs) are useful for text generation since they can generate outputs in the context of previous ones. Baroque music and language are similar, as every word or note exists in context with others, and they both follow strict rules. The authors hypothesized that if we represent music in a text format, an RNN designed to generate language could train on it and create music structurally similar to Bach’s. They found that the music generated by our RNN shared a similar structure with Bach’s music in the input dataset, while Bachbot’s outputs are significantly different from this experiment’s outputs and thus are less similar to Bach’s repertoire compared to our algorithm.

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