The Society of Applied Neuroscience (SAN, http://www.applied-neuroscience.org/) in cooperation with the Medical School of the Aristotle University of Thessaloniki and the Department of Neurology of the Max Planck Institute for Human Cognitive and Brain Sciences, have the great pleasure to invite you to the biennial meeting of the Society, SAN2016 (http://applied-neuroscience.org/san2016/), which will be held in Corfu Island, Greece between 6-9 of October, 2016.
New Article: State of the Art and Future Prospects of Nanotechnologies in the Field of Brain-Computer Interfaces
Neuroprosthetic control by individuals suffering from tetraplegia has already been demonstrated using implanted microelectrode arrays over the patients’ motor cortex. Based on the state of the art of such micro & nano-scale technologies, we review current trends and future prospects for the implementation of nanotechnologies in the field of Brain-Computer Interfaces (BCIs), with brief mention of current clinical applications.
Micro- and Nano-Electromechanical Systems (MEMS, NEMS) and micro-Electrocorticography now belong to the mainstay of neurophysiology, producing promising results in BCI applications, neurophysiological recordings and research. The miniaturization of recording and stimulation systems and the improvement of reliability and durability, decrease of neural tissue reactivity to implants, as well as increased fidelity of said systems are the current foci of this technology. Novel concepts have also begun to emerge such as nanoscale integrated circuits that communicate with the macroscopic environment, neuronal pattern nano-promotion, multiple biosensors that have been “wired” with piezoelectric nanomechanical resonators, or even “neural dust” consisting of 10-100μm scale independent floating low-powered sensors. Problems that such technologies have to bypass include a minimum size threshold and the increase in power to maintain a high signal-to-noise-ratio. Physiological matters such as immunological reactions, neurogloia or neuronal population loss should also be taken into consideration. Progress in scaling down of injectable interfaces to the muscles and peripheral nerves is expected to result in less invasive BCI-controlled actuators (neuroprosthetics in the micro and nano scale).
The state-of-the-art of current microtechnologies demonstrate a maturing level of clinical relevance and promising results in terms of neural recording and stimulation. New MEMS and NEMS fabrication techniques and novel design and application concepts hold promise to address current problems with these technologies and lead to less invasive, longer lasting and more reliable BCI systems in the near future.
New Article: Beta-band functional connectivity is reorganized in mild cognitive impairment after combined computerized physical and cognitive training
Physical and cognitive idleness constitute significant risk factors for the clinical manifestation of age-related neurodegenerative diseases. In contrast, a physically and cognitively active lifestyle may restructure age-declined neuronal networks enhancing neuroplasticity. The present study, investigated the changes of brain’s functional network in a group of elderly individuals at risk for dementia that were induced by a combined cognitive and physical intervention scheme. Fifty seniors meeting Petersen’s criteria of Mild Cognitive Impairment were equally divided into an experimental (LLM), and an active control (AC) group. Resting state electroencephalogram (EEG) was measured before and after the intervention. Functional networks were estimated by computing the magnitude square coherence between the time series of all available cortical sources as computed by standardized low resolution brain electromagnetic tomography (sLORETA). A statistical model was used to form groups’ characteristic weighted graphs. The introduced modulation was assessed by networks' density and nodes’ strength. Results focused on the beta band (12-30 Hz) in which the difference of the two networks' density is maximum, indicating that the structure of the LLM cortical network changes significantly due to the intervention, in contrast to the network of AC. The node strength of LLM participants in the beta band presents a higher number of bilateral connections in the occipital, parietal, temporal and prefrontal regions after the intervention. Our results show that the combined training scheme reorganizes the beta-band functional connectivity of MCI patients.
By Micah AllenThis post marks the first of my new interview series “Connecting the Dots: Big Thinkers in Cognitive Neuroscience”.
Last month marked the 10th anniversary of the landmark paper that launched “connectomics”, overthrowing the predominant approach to localizing individual functions in the brain in favor of mapping the entirety of the brain’s connections. In the decade since, connectomics has redefined how we collect, analyzing, and interpret our data. Along the way numerous international endeavors like the Human Connectome Project have sprung up, spurring hundreds of institutions to amass never before seen volumes of brain data from thousands of individuals. This revolution has moved cognitive neuroimaging from a small scale endeavor, governed by many isolated labs conducting small scale studies in closed settings, to a massive open science bonanza of data sharing. Today many brain science institutes find themselves engaged in large scale data collection, whether to establish normative samples of particular patient groups or to bolster ongoing connectomic and computational approaches. This movement has not been without its detractors however, with some raising concerns about the cost and long-term payoff of these massive scale projects, arguing that they come at the cost of more flexible, smaller, hypothesis-driven research.
To get a feeling for how far we’ve come and how far we’ve yet to go, I met with PLOS ONE Section Editor and PLOS Computational Biology Deputy Editor-in-Chief, Olaf Sporns to discuss the first “decade of connectomics”.
In this video you can see a high resolution chronectome for the human brain. Each graph coresponds to one sample point and not to a time window. This gives us the opportunity to have more detailed chronographs as well as to investigate the graphs' parameters like density (lower left fig) or clustering coefficient (lower right fig)
New article: ERP Measures of Math Anxiety: How Math Anxiety Affects Working Memory and Mental Calculation Tasks?
There have been several attempts to account for the impact of Mathematical Anxiety (MA) on brain activity with variable results. The present study examines the effects of MA on ERP amplitude during performance of simple arithmetic calculations and working memory tasks. Data were obtained from 32 university students as they solved four types of arithmetic problems (one- and two-digit addition and multiplication) and a working memory task comprised of three levels of difficulty (1,2,and 3-back task). Compared to the Low-MA group, High-MA individuals demonstrated reduced ERP amplitude at frontocentral (between 180-320 ms) and centroparietal locations (between 380-420 ms). These effects were independent of task difficulty/complexity, individual performance, and general state/trait anxiety levels. Results support the hypothesis that higher levels of self-reported MA are associated with lower cortical activation during the early stages of the processing of numeric stimuli in the context of cognitive tasks.
Citation: Klados MA, Simos PG, Micheloyannis S, Margulies DS and Bamidis PD (2015). ERP Measures of Math Anxiety: How Math Anxiety Affects Working Memory and Mental Calculation Tasks?. Front. Behav. Neurosci. 9:282.