Journal of Integrative Neuroscience

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain–computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.

Due to the recent explosion in various forms of 3D content, the evaluation of such content from a neuroscience perspective is quite interesting. However, existing investigations of cortical oscillatory responses in stereoscopic depth perception are quite rare. Therefore, we investigated spatiotemporal and spatio-temporo-spectral features at four different stereoscopic depths within the comfort zone. We adopted a simultaneous EEG/MEG acquisition technique to collect the oscillatory responses of eight participants. We defined subject-specific retinal disparities and designed a single trial-based stereoscopic viewing experimental paradigm. In the group analysis, we observed that, as the depth increased from Level 1 to Level 3, there was a time-locked increase in the N200 component in MEG and the P300 component in EEG in the occipital and parietal areas, respectively. In addition, initial alpha and beta event-related desynchronizations (ERD) were observed at approximately 500 to 1000 msec, while theta, alpha, and beta event-related synchronizations (ERS) appeared at approximately 1000 to 2000 ms. Interestingly, there was a saturation point in the increase in cognitive responses, including N200, P300, and alpha ERD, even when the depth increased only within the comfort zone. Meanwhile, the magnitude of low beta ERD decreased in the dorsal pathway as depth increased. From these findings, we concluded that cognitive responses are likely to become saturated in the visual comfort zone, while perceptual load may increase with depth.

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of the brain for different images with limited data sets. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using a t-test. MVPA uses machine learning algorithms to classify different brain states and helps with prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and a classification is done using a multi-class support vector machine (SVM). A proposed MVPA-CNN based algorithm is compared with a region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

The wiring diagram of the mouse brain presents an indispensable foundation for the research on basic and applied neurobiology. It is also essential as a structural foundation for computational simulation of the brain. Different scales of the connectome give us different hints and clues to understand the functions of the nervous system and how they process information. However, compared to the macroscale and most recent mesoscale mouse brain connectome studies, there is no complete whole brain microscale connectome available because of the scalability and accuracy of automatic recognition techniques. Different scales of the connectivity data are comprehensive descriptions of the whole brain at different levels of details. Hence connectivity results from a neighborhood scale may help to predict each other. Here we report a computational approach to bring the mesoscale connectome a step forward towards the microscale from the perspective of neuron, synapse and network motifs distribution by the connectivity data at the mesoscale and some facts from the anatomical experiments at the microscale. These attempts make a step forward towards the efforts of microscale mouse brain connectome given the fact that the detailed microscale connectome results are still far to be produced due to the limitation of current nano-scale 3-D reconstruction techniques. The generated microscale mouse brain will play a key role on the understanding of the behavioral and cognitive processes of the mouse brain. In this paper, the conversion method which could get the approximate number of neurons and synapses in microscale is proposed and tested in sub-regions of Hippocampal Formation (HF), and is generalized to the whole brain. As a step forward towards understanding the microscale connectome, we propose a microscale motif prediction model to generate understanding on the microscale structure of different brain region from network motif perspective. Correlation analysis shows that the predicted motif distribution is very relevant to the real anatomical brain data at microscale.

Interpersonal coordination during joint action depends on the perception of the partner's movements. In many such situations – for example, while moving furniture together or dancing a tango – there are kinesthetic interactions between the partners due to the forces shared between them that allow them to directly perceive one another's movements. Joint action of this type often involves a contrast between the roles of leader and follower, where the leader imparts forces onto the follower, and the follower has to be responsive to these force-cues during movement. We carried out a novel 2-person functional MRI study with trained couple dancers engaged in bimanual contact with an experimenter standing next to the bore of the magnet, where the two alternated between being the leader and follower of joint improvised movements, all with the eyes closed. One brain area that was unexpectedly more active during following than leading was the region of MT+/V5. While classically described as an area for processing visual motion, it has more recently been shown to be responsive to tactile motion as well. We suggest that MT+/V5 responds to motion based on force-cues during joint haptic interaction, most especially when a follower responds to force-cues coming from a leader's movements.

The present study investigates the temperature dependence of electrotonic potentials in mathematically-simulated myelinated axons with one of three increasingly-severe type of amyotrophic lateral sclerosis (ALS) pathology, termed as ALS1, ALS2 and ALS3, respectively, in the physiological range ( 30 – 37 ∘ C ). These potentials were elicited by long-lasting (100 ms) subthreshold polarizing current stimuli ( ± 40 % of the threshold). Numerical solutions were computed using our temperature-dependent multi-layered model. The results showed the following trends: (i) in ALS1, polarizing electrotonic potentials were normal; (ii) in ALS2 and ALS3, action potentials were elicited in the early parts of the depolarizing electrotonic potentials, and (iii) in ALS3, spontaneous discharges were elicited after the termination of applied hyperpolarizing stimuli (i.e., post-anodal excitation). The ionic currents underlying electrotonic potentials in the ALS1 case were attributable to the activation of potassium fast ( K f + ) and slow ( K s + ) channels in the nodal and internodal axolemma beneath the myelin sheath. By contrast, in ALS2 and ALS3, the depolarizing stimuli activated the classical “transient” Na + channels in the nodal and internodal axolemma beneath the myelin sheath eliciting action potential generation. These results obtained were closer to those observed in hypothermia ( ≥ 25 ∘ C ) than in hyperthermia ( ≥ 40 ∘ C ).

Aim of the present study is to investigate the effects of medication with donepezil (acetylcholinesterase inhibitor) on the liver and kidney function in Alzheimer's disease (AD) and to compare the effects of donepezil medication during short (one month) and long term (six years) follow-ups. We evaluated female and male patients from Cukurova [42 AD patients; short term (5 mg/day)] and Dokuz Eylul [68 AD patients; long term (10 mg/day)] University Hospital. The results compared with the geriatric population without dementia in other words who are not in medication with donepezil. For short term evaluation all subjects underwent periodic examination with tests regarding hepatic and renal functions; firstly, before starting treatment and then repeated one month later. For long term evaluation all subjects underwent periodic examination with tests regarding hepatic and renal functions; three times at the end of each two consecutive years of treatment with donepezil. AD patients’ results were also compared with 79 neurologically healthy geriatric patients without dementia who were over 65 years of age and were not receiving medication with donepezil. For this task, serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels are used to predict possible liver damage, while the blood urea nitrogen (BUN) and creatinine (CRE) levels for kidney damage. No significant difference between the groups regarding the routine control of biochemical parameters was observed in short term drug medication. In long term patients’ group; the effects of two years use of donepezil on renal and hepatic function were also evaluated and levels of AST, ALT, BUN and CRE were found to be increased significantly compared to pretreatment levels. But, they remained in the reference intervals. However, levels of AST and ALT at the end of the fourth year of therapy were similar to those measured at the end of the second year, levels of BUN and CRE continuing to increase with staying below the reference limits. Functional markers obtained at the end of the sixth year of therapy were not differing from those of the fourth year. No significant difference was found during comparisons within the results of the neurologically healthy geriatric patient group. During comparisons between the two groups, measurements obtained at all-time points were significantly high in donepezil treated AD patients. We concluded that customized dosage according to hepatic and renal functions is necessary for using acetylcholinesterase inhibitor in AD patients.

The main aim of the present study was to determine whether adult dyslexic readers demonstrate the “Asynchrony Theory” (Breznitz [Reading Fluency: Synchronization of Processes, Lawrence Erlbaum and Associates, Mahwah, NJ, USA, 2006]) when selective attention is studied. Event-related potentials (ERPs) and behavioral parameters were collected from nonimpaired readers group and dyslexic readers group performing alphabetic and nonalphabetic tasks. The dyslexic readers group was found to demonstrate asynchrony between the auditory and the visual modalities when it came to processing alphabetic stimuli. These findings were found both for behavioral and ERPs parameters. Unlike the dyslexic readers, the nonimpaired readers showed synchronized speed of processing in the auditory and the visual modalities while processing alphabetic stimuli. The current study suggests that established reading is dependent on a synchronization between the auditory and the visual modalities even when it comes to selective attention.

Dorsal premotor cortex (PMd) is considered to play a crucial role in motor preparation, yet how the variation of neuronal activity affects the generation of different circumstances dependent movements remains unclear. Here we trained two monkeys to perform a delayed reaching task instructed by two sets of cues, one for indicating the target locations and another for indicating a conditionally presented virtual obstacle in the reaching path, which required the monkey to make a bypassing instead of straight reaching. We recorded the activity of PMd neurons and investigated how they responded to the switching of intended hand path induced by obstacle bypassing. Comparing the neuronal activity between hand bypassing trials and straight reaching trials, we found 30% of the total 687 set-related neurons showed different overall discharging level, and another 24% showed different onset time during the delay period. We also found 16% of the neurons were modulated only by target location and 14% were modulated by both target location and path switching. Our results demonstrate PMd neurons not only represent the planning of reaching to different target locations, as many previous studies have shown, but also represent the switching of intended reaching path induced by hand bypassing, suggesting how PMd neurons coordinate for such circumstances dependent motor planning.