Search

Importance: Brain fog is associated with significant morbidity and reduced productivity and gained increasing attention after COVID-19. However, this subjective state has not been systematically characterised. Objective: To characterise self-reported brain fog. Design: We systematically studied the cross-sectional associations between 29 a priori variables with the presence of "brain fog." The variables were grouped into four categories: demographics, symptoms and functional impairments, comorbidities and potential risk factors (including lifestyle factors), and cognitive score. Univariate methods determined the correlates of brain fog, with long-COVID and non-long-COVID subgroups. XGBoost machine learning model retrospectively characterised subjective brain fog. Bonferroni-corrected statistical significance was set at 5%. Setting: Digital application for remote data collection. Participants: 25,796 individuals over the age of 18 who downloaded and completed the application. Results: 7,280 of 25,796 individuals (28.2%) reported experiencing brain fog, who were generally older (mean brain fog 35.7 ± 11.9 years vs. 32.8 ± 11.6 years, p < 0.0001) and more likely to be female (OR = 1.2, p < 0.001). Associated symptoms and functional impairments included difficulty focusing or concentrating (OR = 3.3), feeling irritable (OR = 1.6), difficulty relaxing (OR = 1.2, all p < 0.0001), difficulty following conversations (OR = 2.2), remembering appointments (OR = 1.9), completing paperwork and performing mental arithmetic (ORs = 1.8, all p < 0.0001). Comorbidities included long-COVID-19 (OR = 3.8, p < 0.0001), concussions (OR = 2.4, p < 0.0001), and higher migraine disability assessment scores (MIDAS) (+34.1%, all p < 0.0001). Cognitive scores were marginally lower with brain fog (-0.1 std., p < 0.001). XGBoost achieved a training accuracy of 85% with cross-validated accuracy of 74%, and the features most predictive of brain fog in the model were difficulty focusing and following conversations, long-COVID, and severity of migraines. Conclusions and relevance: This is the largest study characterising subjective brain fog as an impairment of concentration associated with functional impairments in activities of daily living. Brain fog was particularly associated with a history of long-COVID-19, migraines, concussion, and with 0.1 standard deviations lower cognitive scores, especially on modified Stroop testing, suggesting impairments in the ability to inhibit cognitive interference. Further prospective studies in unselected brain fog sufferers should explore the full spectrum of brain fog symptoms to differentiate it from its associated conditions. [ABSTRACT FROM AUTHOR]

Introduction: Our brain continuously maps our body in space. It has been suggested that at least two main frames of reference are used to process somatosensory stimuli presented on our own body: the anatomical frame of reference (based on the somatotopic representation of our body in the somatosensory cortex) and the spatial frame of reference (where body parts are mapped in external space). Interestingly, a mismatch between somatotopic and spatial information significantly affects the processing of bodily information, as demonstrated by the "crossing hand" effect. However, it is not clear if this impairment occurs not only when the conflict between these frames of reference is determined by a static change in the body position (e.g., by crossing the hands) but also when new associations between motor and sensory responses are artificially created (e.g., by presenting feedback stimuli on a side of the body that is not involved in the movement). Methods: In the present study, 16 participants performed a temporal order judgment task before and after a congruent or incongruent visual-tactilemotor-task in virtual reality. During the VR task, participants had to move a cube using a virtual stick. In the congruent condition, the haptic feedback during the interaction with the cube was provided on the right hand (the one used to control the stick). In the incongruent condition, the haptic feedback was provided to the contralateral hand, simulating a sort of 'active' crossed feedback during the interaction. Using a psychophysical approach, the point of subjective equality (or PSE, i.e., the probability of responding left or right to the first stimulus in the sequence in 50% of the cases) and the JND (accuracy) were calculated for both conditions, before and after the VR-task. Results: After the VR task, compared to the baseline condition, the PSE shifted toward the hand that received the haptic feedback during the interaction (toward the right hand for the congruent condition and toward the left hand for the incongruent condition). Dicussion: This study demonstrated the possibility of inducing spatial biases in the processing of bodily information by modulating the sensory-motor interaction between stimuli in virtual environments (while keeping constant the actual position of the body in space). [ABSTRACT FROM AUTHOR]

Introduction: The current study measured the frontal midline theta rhythm (Fm"), which appears in the frontal midline region during the attentional focus state, using the sheet-type wearable electroencephalograph (EEG) device HARU-1, and examined the modulation of frontal gamma band activity by cognitive tasks. Methods: We measured the frontal EEG of 20 healthy subjects using HARU-1 for 2 min during the rest eyes-closed condition and simple mental calculation task condition, respectively. Statistical analyses were conducted using permutation testing based on t-test and cluster analysis to compare the results between the resting state and the task condition. Results: Twelve of 20 subjects showed Fm" during the task condition. The 12 subjects with Fm" showed significantly higher activity of the theta and gamma bands, and significantly low activity of the alpha band during the task condition compared to the resting condition. In the eight subjects without Fm" were significantly low activity of the alpha and beta bands and no significant activity in the theta and gamma band activity during the task condition compared to the resting condition. Discussion: These results indicate that it is possible to measure Fm" using HARU-1. A novel finding was the gamma band activity appearing with Fm" in the left and right frontal forehead regions, suggesting that it reflects the function of the prefrontal cortex in working memory tasks. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. [ABSTRACT FROM AUTHOR]

Humans can quickly approximate how many objects are in a visual image, but no clear consensus has been achieved on the cognitive resources underlying this ability. Previous work has lent support to the notion that mechanisms which explicitly represent the locations of multiple objects in the visual scene within a mental map are critical for both visuo-spatial working memory and enumeration (at least for relatively small numbers of items). Regarding the cognitive underpinnings of large numerosity perception, an issue currently subject to much controversy is why numerosity estimates are often non-veridical (i.e., susceptible to biases from non-numerical quantities). Such biases have been found to be particularly pronounced in individuals with developmental dyscalculia (DD), a learning disability affecting the acquisition of arithmetic skills. Motivated by findings showing that DD individuals are also often impaired in visuo-spatial working memory, we hypothesized that resources supporting this type of working memory, which allow for the simultaneous identification of multiple objects, might also be critical for precise and unbiased perception of larger numerosities. We therefore tested whether loading working memory of healthy adult participants during discrimination of large numerosities would lead to increased interference from non-numerical quantities. Participants performed a numerosity discrimination task on multi-item arrays in which numerical and non-numerical stimulus dimensions varied congruently or incongruently relative to each other, either in isolation or in the context of a concurrent visuo-spatial or verbal working memory task. During performance of the visuo-spatial, but not verbal, working memory task, precision in numerosity discrimination decreased, participants' choices became strongly biased by item size, and the strength of this bias correlated with measures of arithmetical skills. Moreover, the interference between numerosity and working memory tasks was bidirectional, with number discrimination impacting visuo-spatial (but not verbal) performance. Overall, these results suggest that representing visual numerosity in a way that is unbiased by non-numerical quantities relies on processes which explicitly segregate/identify the locations of multiple objects that are shared with visuo-spatial (but not verbal) working memory. This shared resource may potentially be impaired in DD, explaining the observed co-occurrence of working memory and numerosity discrimination deficits in this clinical population. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain–computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network (CNN)-based approach for implementing a subject-independent fNIRS-based BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication (70%) as well as that obtained using conventional shrinkage linear discriminant analysis (65.74 ± 7.68%). To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials (of approximately 12 min) were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. [ABSTRACT FROM AUTHOR]

There is debate in the literature regarding how single-digit arithmetic fluency is achieved over development. While the Fact-retrieval hypothesis suggests that with practice, children shift from quantity-based procedures to verbally retrieving arithmetic problems from long-term memory, the Schema-based hypothesis claims that problems are solved through quantity-based procedures and that practice leads to these procedures becoming more automatic. To test these hypotheses, a sample of 46 typically developing children underwent functional magnetic resonance imaging (fMRI) when they were 11 years old (time 1), and 2 years later (time 2). We independently defined regions of interest (ROIs) involved in verbal and quantity processing using rhyming and numerosity judgment localizer tasks, respectively. The verbal ROIs consisted of left middle/superior temporal gyri (MTG/STG) and left inferior frontal gyrus (IFG), whereas the quantity ROIs consisted of bilateral inferior/superior parietal lobules (IPL/SPL) and bilateral middle frontal gyri (MFG)/right IFG. Participants also solved a single-digit subtraction task in the scanner. We defined the extent to which children relied on verbal vs. quantity mechanisms by selecting the 100 voxels showing maximal activation at time 1 from each ROI, separately for small and large subtractions. We studied the brain mechanisms at time 1 that predicted gains in subtraction fluency and how these mechanisms changed over time with improvement. When looking at brain activation at time 1, we found that improvers showed a larger neural problem size effect in bilateral parietal cortex, whereas no effects were found in verbal regions. Results also revealed that children who showed improvement in behavioral fluency for large subtraction problems showed decreased activation over time for large subtractions in both parietal and frontal regions implicated in quantity, whereas non-improvers maintained similar levels of activation. All children, regardless of improvement, showed decreased activation over time for large subtraction problems in verbal regions. The greater parietal problem size effect at time 1 and the reduction in activation over time for the improvers in parietal and frontal regions implicated in quantity processing is consistent with the Schema-based hypothesis arguing for more automatic procedures with increasing skill. The lack of a problem size effect at time 1 and the overall decrease in verbal regions, regardless of improvement, is inconsistent with the Fact-retrieval hypothesis. [ABSTRACT FROM AUTHOR]

Background : Abacus experts could mentally calculate fast some mathematical operations using multi-digit numbers. The temporal dynamics of abacus mental calculation are still unknown although some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor neural process during abacus mental calculation. Therefore, this contribution aims to clarify the significant similarities and the differences between experts and novices by investigating calculation-induced neuromagnetic responses based on cerebral oscillatory changes. Methods : Twelve to 13 healthy abacus experts and 17 non-experts participated in two experimental paradigms using non-invasive neuromagnetic measurements. In experiments 1 and 2, the spatial distribution of oscillatory changes presented mental calculations and temporal frequency profiles during addition while examining multiplication tasks. The MEG data were analyzed using synthetic aperture magnetometry (SAM) with an adaptive beamformer to calculate the group average of the spatial distribution of oscillatory changes and their temporal frequency profiles in source-level analyses. Results : Using a group average of the spatial distribution of oscillatory changes, we observed some common brain activities in both right-handed abacus experts and non-experts. In non-experts, we detected the right dorsolateral prefrontal cortex (DLPFC) and bilateral Intraparietal sulcus (IPS); whereas in experts, detected the bilateral parieto-occipital sulcus (POS), right inferior frontal gyrus (IFG), and left sensorimotor areas mainly. Based on the findings generated, we could propose calculation processing models for both abacus experts and non- experts conveniently. Conclusion : The proposed model of calculation processing in abacus experts and novices revealed that the novices could calculate logically depending on numerical processing in the left IPS. In contrast, abacus experts are utilizing spatial processing using a memorized imaginary abacus, which distributed over the bilateral hemispheres in the IFG and sensorimotor areas. [ABSTRACT FROM AUTHOR]

Arithmetic abilities are among the most important school-taught skills and form the basis for higher mathematical competencies. At the same time, their acquisition and application can be challenging. Hence, there is broad interest in methods to improve arithmetic abilities. One promising method is transcranial direct current stimulation (tDCS). In the present study, we compared two anodal tDCS protocols in their efficacy to improve arithmetic performance and working memory. In addition, we investigated stimulation-related electrophysiological changes. Three groups of participants solved arithmetic problems (additions and subtractions) and an n-back task before, during, and after receiving either frontal or parietal anodal tDCS (25 min; 1 mA) or sham stimulation. EEG was simultaneously recorded to assess stimulation effects on event-related (de-) synchronisation (ERS/ERD) in theta and alpha bands. Persons receiving frontal stimulation showed an acceleration of calculation speed in large subtractions from before to during and after stimulation. However, a comparable, but delayed (apparent only after stimulation) increase was also found in the sham stimulation group, while it was absent in the group receiving parietal stimulation. In additions and small subtractions as well as the working memory task, analyses showed no effects of stimulation. Results of ERS/ERD during large subtractions indicate changes in ERS/ERD patterns over time. In the left hemisphere there was a change from theta band ERD to ERS in all three groups, whereas a similar change in the right hemisphere was restricted to the sham group. Taken together, tDCS did not lead to a general improvement of arithmetic performance. However, results indicate that frontal stimulation accelerated training gains, while parietal stimulation halted them. The absence of general performance improvements, but acceleration of training effects might be a further indicator of the advantages of using tDCS as training or learning support over tDCS as a sole performance enhancer. [ABSTRACT FROM AUTHOR]

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future. [ABSTRACT FROM AUTHOR]

Using a hemifield paradigm, the authors investigated a possible left lateralization of the representation of arithmetic facts in a number bisection task. Keywords: numerical cognition; numerical development; number processing; neural correlates; neuro-cognitive plasticity; developmental dyscalculia; numerical training EN numerical cognition numerical development number processing neural correlates neuro-cognitive plasticity developmental dyscalculia numerical training N.PAG N.PAG 3 05/03/21 20210428 NES 210428 Living in our modern societies requires being numerically literate not only in everyday life (e.g., reading the clock, dealing with money, etc.) but also in educational (e.g., mathematics and science classes) and professional contexts (e.g., accounting but also crafts). Numerical cognition, numerical development, number processing, neural correlates, neuro-cognitive plasticity, developmental dyscalculia, numerical training. [Extracted from the article]

Many studies have shown that solving addition and subtraction problems can induce overt shifts of spatial attention. In particular, right-side targets are detected faster than left-side targets when preceded by an addition operation, while left-side targets are detected faster than right-side targets when preceded by a subtraction operation. However, the interaction between space and arithmetic in multiplication or division is hardly studied and remains controversial. In order to make a strong case for the interaction between space and mental arithmetic, we attempted to replicate the spatial-arithmetic association in addition and subtraction (Experiment 1), and at the same time investigated whether shift of spatial attention would also be induced by multiplication or division operations (Experiment 2). We found that solving addition problems facilitated the detection of right-side targets, whereas left-side targets were detected faster after solving subtraction problems. However, no interaction between space and arithmetic operation was observed in multiplication or division. The implication of these findings is discussed.

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided. [ABSTRACT FROM AUTHOR]

Many studies have shown that solving addition and subtraction problems can induce overt shifts of spatial attention. In particular, right-side targets are detected faster than left-side targets when preceded by an addition operation, while left-side targets are detected faster than right-side targets when preceded by a subtraction operation. However, the interaction between space and arithmetic in multiplication or division is hardly studied and remains controversial. In order to make a strong case for the interaction between space and mental arithmetic, we attempted to replicate the spatialarithmetic association in addition and subtraction (Experiment 1), and at the same time investigated whether shift of spatial attention would also be induced by multiplication or division operations (Experiment 2). We found that solving addition problems facilitated the detection of right-side targets, whereas left-side targets were detected faster after solving subtraction problems. However, no interaction between space and arithmetic operation was observed in multiplication or division. The implication of these findings is discussed. [ABSTRACT FROM AUTHOR]

Many neuroimaging studies have demonstrated the different functional contribution of spatial distinct anatomical brain areas to working memory (WM) subsystems in various cognitive tasks that demand both local information processing and a coordinated mechanism between them. In WM cognitive tasks using electroencephalography (EEG), brain rhythms, such as θ and α, have been linked to a specific functional role located over a brain area but their functional coupling has not been yet studied extensively. In the present study, analyzing an arithmetic task designed with five cognitive workload levels (CWLs), we demonstrated the functional/effective coupling between the two subsystems of the WM, the central executive located over frontal (F) brain areas that oscillates on the dominant θ rhythm (Frontalθ/Fθ) and the storage buffer located over parieto-occipital (PO) brain areas that operates on the α2 dominant brain rhythm (Parieto-Occipitalα2 / POα2). Our analysis focused on demonstrating important differences between and within WM subsystems in relation to behavioral performance. Attempting to uncover the distinct role of amplitude, phase within and between frequencies and also the hierarchical role of functionally specialized brain areas related to the task, we employed a repertoire of brain connectivity estimators. Specifically, for each CWL, we conducted a) a conventional signal power analysis within both frequency bands at Fθ and POα2 ,b) the intra and inter-frequency phase interactions between Fθ and POα2 and c) their causal phase and amplitude relationship. We found no significant statistical difference of signal power and of phase interactions between correct and wrong answers. Interestingly, the study of causal interactions between Fθ and POα2 revealed frontal brain region(s) as the leader, while the strength was able to differentiate correct from wrong responses, in every CWL with absolute accuracy. Additionally, zero time-lag between bilateral Fθ and right POa2 could serve as an indicator of the failure in mental calculation. Overall, our study highlights the significant role of coordinated activity between Fθ and POα2 via their causal interactions and the timing for arithmetic performance.

In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features — mean, slope, variance, peak, skewness and kurtosis — are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic versus rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic versus rest for a two-class BCI.

Keywords: brain-computer interface; deep learning; machine learning; transfer learning; data augmentation; explainable artificial intelligence EN brain-computer interface deep learning machine learning transfer learning data augmentation explainable artificial intelligence 1 2 2 05/23/22 20220519 NES 220519 Introduction Recent advancements in deep learning with the support of large-scale datasets and computational power have led many studies to adopt deep neural networks (DNNs) to extract features from brain signals and decode brain states, which is an important element in brain-computer interface (BCI). The authors of the publications accepted presented articles that cover a DL-based BCI for specific applications and a novel model for high BCI performance or transfer learning, data augmentation. Brain-computer interface, deep learning, machine learning, transfer learning, data augmentation, explainable artificial intelligence. [Extracted from the article]

Many neuroimaging studies have demonstrated the different functional contributions of spatially distinct brain areas to working memory (WM) subsystems in cognitive tasks that demand both local information processing and interregional coordination. In WM cognitive task paradigms employing electroencephalography (EEG), brain rhythms such as θ and α have been linked to specific functional roles over given brain areas, but their functional coupling has not been extensively studied. Here we analyzed an arithmetic task with five cognitive workload levels (CWLs) and demonstrated functional/effective coupling between the two WM subsystems: the central executive located over frontal (F) brain areas that oscillates on the dominant θ rhythm (Frontalθ/Fθ) and the storage buffer located over parieto-occipital (PO) brain areas that operates on the α2 dominant brain rhythm (Parieto-Occipitalα2/POα2). We focused on important differences between and within WM subsystems in relation to behavioral performance. A repertoire of brain connectivity estimators was employed to elucidate the distinct roles of amplitude, phase within and between frequencies, and the hierarchical role of functionally specialized brain areas related to the task. Specifically, for each CWL, we conducted a) a conventional signal power analysis within both frequency bands at Fθ and POα2, b) the intra- and inter-frequency phase interactions between Fθ and POα2, and c) their causal phase and amplitude relationship. We found no significant statistical difference of signal power or phase interactions between correct and wrong answers. Interestingly, the study of causal interactions between Fθ and POα2 revealed frontal brain region(s) as the leader, while the strength differentiated between correct and wrong responses in every CWL with absolute accuracy. Additionally, zero time-lag between bilateral Fθ and right POa2 could serve as an indicator of mental calculation failure. Overall, our study highlights the significant role of coordinated activity between Fθ and POα2 via their causal interactions and the timing for arithmetic performance. [ABSTRACT FROM AUTHOR]

In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features--mean, slope, variance, peak, skewness and kurtosis--are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI. [ABSTRACT FROM AUTHOR]

Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired. Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology with a potential to satisfy these conditions. EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on www.opennirs.org. It provides features such as scalable channel number, configurable regulated light intensities, programmable gain and lock-in amplification. In this paper, the system concept, hardware, software and mechanical implementation of the lightweight stand-alone instrument are presented and the evaluation and verification results of the instrument's hardware and physiological fNIRS functionality are described. Its capability to measure brain activity is demonstrated by qualitative signal assessments and a quantitative mental arithmetic based BCI study with 12 subjects. [ABSTRACT FROM AUTHOR]

The ability to accurately process numerical magnitudes and solve mental arithmetic is of highest importance for schooling and professional career. Although impairments in these domains in disorders such as developmental dyscalculia (DD) are highly detrimental, remediation is still sparse. In recent years, transcranial brain stimulation methods such as transcranial Direct Current Stimulation (tDCS) have been suggested as a treatment for various neurologic and neuropsychiatric disorders. The posterior parietal cortex (PPC) is known to be crucially involved in numerical magnitude processing and mental arithmetic. In this study, we evaluated whether tDCS has a beneficial effect on numerical magnitude processing and mental arithmetic. Due to the unclear lateralization, we stimulated the left, right as well as both hemispheres simultaneously in two experiments. We found that left anodal tDCS significantly enhanced performance in a number comparison and a subtraction task, while bilateral and right anodal tDCS did not induce any improvements compared to sham. Our findings demonstrate that the left PPC is causally involved in numerical magnitude processing and mental arithmetic. Furthermore, we show that these cognitive functions can be enhanced by means of tDCS. These findings encourage to further investigate the beneficial effect of tDCS in the domain of mathematics in healthy and impaired humans.

The present study investigated changes in cortical oxygenation during mental arithmetic using near-infrared spectroscopy (NIRS). Twenty-nine male volunteers were examined using a 52-channel continuous wave system for analyzing activity in prefrontal areas. With the help of a probabilistic mapping method, three regions of interest (ROIs) on each hemisphere were defined: The inferior frontal gyri (IFG), the middle frontal gyri (MFG), and the superior frontal gyri (SFG). Oxygenation as an indicator of functional brain activation was compared over the three ROI and two levels of arithmetic task difficulty (simple and complex additions). In contrast to most previous studies using fMRI or NIRS, in the present study arithmetic tasks were presented verbally in analogue to many daily life situations. With respect to task difficulty, more complex addition tasks led to higher oxygenation in all defined ROI except in the left IFG compared to simple addition tasks. When compared to the channel positions covering different gyri of the temporal lobe, the observed sensitivity to task complexity was found to be restricted to the specified ROIs. As to the comparison of ROIs, the highest oxygenation was found in the IFG, while MFG and SFG showed significantly less activation compared to IFG. The present cognitive-neuroscience approach demonstrated that NIRS is a suitable research tool for the identification of cortical regions involved in mental arithmetic and for quantifying neural effects of increasing arithmetic task difficulty.

The article offers information on the attention gained by mental athletes (MA) from Neuroscience community. Topics discussed include various demonstrations which involve tricks that regular individuals can learn, efforts of Daniel Tammet for his extraordinary calculation abilities, and several myths about autistic savant calendrical calculators.

Frontal midline theta rhythm (Fmϴ) appears widely distributed over medial prefrontal areas in EEG recordings, indicating focused attention. Although mental calculation is often used as an attention-demanding task, little has been reported on calculation-related activation in Fmϴ experiments. In this study we used spatially filtered MEG and permutation analysis to precisely localize cortical generators of the magnetic counterpart of Fmϴ, as well as other sources of oscillatory activity associated with mental calculation processing (i.e., arithmetic subtraction). Our results confirmed and extended earlier EEG/MEG studies indicating that Fmϴ during mental calculation is generated in the dorsal anterior cingulate and adjacent medial prefrontal cortex. Mental subtraction was also associated with gamma event-related synchronization, as an index of activation, in right parietal regions subserving basic numerical processing and number-based spatial attention. Gamma eventrelated desynchronization appeared in the right lateral prefrontal cortex, likely representing a mechanism to interrupt neural activity that can interfere with the ongoing cognitive task. [ABSTRACT FROM AUTHOR]

Frontal midline theta rhythm (Fmθ) appears widely distributed over medial prefrontal areas in EEG recordings, indicating focused attention. Although mental calculation is often used as an attention-demanding task, little has been reported on calculation-related activation in Fmθ experiments. In this study we used spatially filtered MEG and permutation analysis to precisely localize cortical generators of the magnetic counterpart of Fmθ, as well as other sources of oscillatory activity associated with mental calculation processing (i.e., arithmetic subtraction). Our results confirmed and extended earlier EEG/MEG studies indicating that Fmθ during mental calculation is generated in the dorsal anterior cingulate and adjacent medial prefrontal cortex. Mental subtraction was also associated with gamma event-related synchronization, as an index of activation, in right parietal regions subserving basic numerical processing and number-based spatial attention. Gamma event-related desynchronization appeared in the right lateral prefrontal cortex, likely representing a mechanism to interrupt neural activity that can interfere with the ongoing cognitive task. [ABSTRACT FROM AUTHOR]

Recent progress in grapheme-color synesthesia research has revealed that certain regularities, as well as individual differences, figure into grapheme-color associations. Although several factors are known to regulate grapheme-color associations, the impact of factors, including their interrelationships, on synesthesia remains unclear. We investigated determinants of synesthetic color for graphemes (characters, letters) of Hiragana, a phonetic script in the Japanese language, and the English alphabet. Results revealed that grapheme ordinality was the strongest predictor of synesthetic colors for Hiragana characters, followed by character sound, and visual shape. Ordinality and visual shapes also significantly predicted synesthetic colors for English alphabet letters, however, sounds did not. The relative impact of grapheme properties on grapheme-color associations and the differences between these two writing systems are accounted for by considering the way graphemes are processed in the brain and introduced during an individual's development. A new model is proposed which takes into account the developmental process of grapheme learning. The model provides comprehensive explanation of synesthetic grapheme-color association determination processes, including the differences across writing systems. [ABSTRACT FROM AUTHOR]

The ability to accurately process numerical magnitudes and solve mental arithmetic is of highest importance for schooling and professional career. Although impairments in these domains in disorders such as developmental dyscalculia (DD) are highly detrimental, remediation is still sparse. In recent years, transcranial brain stimulation methods such as transcranial Direct Current Stimulation (tDCS) have been suggested as a treatment for various neurologic and neuropsychiatric disorders. The posterior parietal cortex (PPC) is known to be crucially involved in numerical magnitude processing and mental arithmetic. In this study, we evaluated whether tDCS has a beneficial effect on numerical magnitude processing and mental arithmetic. Due to the unclear lateralization, we stimulated the left, right as well as both hemispheres simultaneously in two experiments. We found that left anodal tDCS significantly enhanced performance in a number comparison and a subtraction task, while bilateral and right anodal tDCS did not induce any improvements compared to sham. Our findings demonstrate that the left PPC is causally involved in numerical magnitude processing and mental arithmetic. Furthermore, we show that these cognitive functions can be enhanced by means of tDCS. These findings encourage to further investigate the beneficial effect of tDCS in the domain of mathematics in healthy and impaired humans. [ABSTRACT FROM AUTHOR]

The present study investigated changes in cortical oxygenation during mental arithmetic using near-infrared spectroscopy (NIRS). Twenty-nine male volunteers were examined using a 52-channel continuous wave system for analyzing activity in prefrontal areas. With the help of a probabilistic mapping method, three regions of interest (ROIs) on each hemisphere were defined: The inferior frontal gyri (IFG), the middle frontal gyri (MFG), and the superior frontal gyri (SFG). Oxygenation as an indicator of functional brain activation was compared over the three ROI and two levels of arithmetic task difficulty (simple and complex additions). In contrast to most previous studies using fMRI or NIRS, in the present study arithmetic tasks were presented verbally in analogue to many daily life situations. With respect to task difficulty, more complex addition tasks led to higher oxygenation in all defined ROI except in the left IFG compared to simple addition tasks. When compared to the channel positions covering different gyri of the temporal lobe, the observed sensitivity to task complexity was found to be restricted to the specified ROIs. As to the comparison of ROIs, the highest oxygenation was found in the IFG, while MFG and SFG showed significantly less activation compared to IFG. The present cognitive-neuroscience approach demonstrated that NIRS is a suitable and highly feasible research tool for investigating and quantifying neural effects of increasing arithmetic task difficulty. [ABSTRACT FROM AUTHOR]

The article focuses on the study on numerical cognition processes in neurons through disconnection hypothesis. Mentioned is the usage of the Triple Code Model in neuroimaging as well as probabilistic fiber tracking. Discussed is the interpretation of the data after brain imaging, along with the mapping of basal ganglia affections.

Many neuroimaging studies have demonstrated the different functional contributions of spatially distinct brain areas to working memory (WM) subsystems in cognitive tasks that demand both local information processing and interregional coordination. In WM cognitive task paradigms employing electroencephalography (EEG), brain rhythms such as θ and α have been linked to specific functional roles over given brain areas, but their functional coupling has not been extensively studied. Here we analyzed an arithmetic task with five cognitive workload levels (CWLs) and demonstrated functional/effective coupling between the two WM subsystems: the central executive located over frontal (F) brain areas that oscillates on the dominant θ rhythm (Frontal(θ)/F(θ)) and the storage buffer located over parieto-occipital (PO) brain areas that operates on the α2 dominant brain rhythm (Parieto-Occipital(α2)/PO(α2)). We focused on important differences between and within WM subsystems in relation to behavioral performance. A repertoire of brain connectivity estimators was employed to elucidate the distinct roles of amplitude, phase within and between frequencies, and the hierarchical role of functionally specialized brain areas related to the task. Specifically, for each CWL, we conducted a) a conventional signal power analysis within both frequency bands at F(θ) and PO(α2), b) the intra- and inter-frequency phase interactions between F(θ) and PO(α2), and c) their causal phase and amplitude relationship. We found no significant statistical difference of signal power or phase interactions between correct and wrong answers. Interestingly, the study of causal interactions between F(θ) and PO(α2) revealed frontal brain region(s) as the leader, while the strength differentiated between correct and wrong responses in every CWL with absolute accuracy. Additionally, zero time-lag between bilateral F(θ) and right PO(a2) could serve as an indicator of mental calculation failure. Overall, our study highlights the significant role of coordinated activity between F(θ) and PO(α2) via their causal interactions and the timing for arithmetic performance.