Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance.
New Article: Resting-state functional connectivity and network analysis of cerebellum with respect to crystallized IQ and gender.
During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum’s relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson’s correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e. nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend towards the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks
New Article: Automated individual-level parcellation of Broca's region based on functional connectivity
Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles.
New Article: A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques
Artifact rejection techniques are used to recover the brain signals underlying artifactual electroencephalographic (EEG) segments. Although over the last few years many different artifact rejection techniques have been proposed (http://dx.doi.org/10.1109/JSEN.2011.2115236, http://dx.doi.org/10.1016/j.clinph.2006.09.003, http://dx.doi.org/10.3390/e16126553), none has been established as a gold standard so far, because assessing their performance is difficult and subjective (http://dx.doi.org/10.1109/ITAB.2009.5394295, http://dx.doi.org/10.1016/j.bspc.2011.02.001, http://dx.doi.org/10.1007/978-3-540-89208-3_300. ). This limitation is mainly based on the fact that the underlying artifact-free brain signal is unknown, so there is no objective way to measure how close the retrieved signal is to the real one. This article solves the aforementioned problem by presenting a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts, using a realistic head model. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed.
It is recognized that lower electroencephalographic (EEG) frequencies correspond to distributed brain activity over larger spatial regions than higher frequencies and are associated with coordination. In motor processes it has been suggested that this is not always the case. Our objective was to explore this contradiction. In our study, seven healthy subjects performed four motor tasks (execution and imagery of right hand and foot) under EEG recording. Two cortical source models were defined, model «A» with 16 Regions of Interest (ROIs) and model «B» with 20 ROIs over the sensorimotor cortex. Functional connectivity was calculated by Directed Transfer Function for alpha and beta rhythm networks. Four graph properties were calculated for each network: characteristic path length (CPL), clustering coefficient (CC), density (D) and small-world-ness (SW). Different network modules and in-degrees of nodes were also calculated and depicted in connectivity maps. Analysis of variance was used to determine statistical significance of observed differences in the network properties between tasks, between rhythms and between ROI models. Consistently on both models, CPL and CC were lower and D was higher in beta rhythm networks. No statistically significant difference was observed for SW between rhythms or for any property between tasks on any model. Comparing the models we observed lower CPL for both rhythms, lower CC in alpha and higher CC in beta when the number of ROIs increased. Also, denser networks with higher SW were correlated with higher number of ROIs. We propose a non-exclusive model where alpha rhythm uses greater wiring costs to engage in local information progression while beta rhythm coordinates the neurophysiological processes in sensorimotor tasks.
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)