Neuroscience Methods

The temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed. Here, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a visual motion discrimination task. First, we report consistent infraslow (from 0.1 to 1.5 Hz) scale-free dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higher-order cortices; the anatomical gradient was more pronounced in task...

The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain.

In experiments involving small animals, the electroencephalogram (EEG) recorded during severe injury and accompanying resuscitation exhibit the strong presence of electrocardiogram (ECG). For improved quantitative EEG (qEEG) analysis, it is therefore imperative to remove ECG interference from EEG. In this paper, we validate the use of independent component analysis (ICA) to effectively suppress the interference of ECG from EEG recordings during normal activity, asphyxia and recovery following asphyxia. Two channels of EEG from five rats were recorded continuously for 2 h. Simultaneous recording of one channel ECG was also made. Epochs of 4 s and 1 min were selected from baseline, asphyxia and recovery (every 10 min) and their independent components and power spectra were calculated. The improvement in normalized power spectrum of EEG obtained for all animals was 7.71+/-3.63 db at the 3rd minute of recovery and dropped to 1.15+/-0.60 db at 63rd minute. The application of ICA has been particularly useful when the power of EEG is low, such as that observed during early brain hypoxic-asphyxic injury. The method is also useful in situations where accurate indications of EEG signal power and frequency content are needed.

Neuronavigation provides visual guidance of an instrument during procedures of neurological interventions, and has been shown to be a valuable tool for accurately positioning transcranial magnetic stimulation (TMS) coils relative to an individual's anatomy. Despite the importance of neuronavigation, its high cost, low portability, and low availability of magnetic resonance imaging facilities limit its insertion in research and clinical environments. We have developed and validated the InVesalius Navigator as the first free, open-source software for image-guided navigated TMS, compatible with multiple tracking devices. A point-based, co-registration algorithm and a guiding interface were designed for tracking any instrument (e.g. TMS coils) relative to an individual's anatomy. Localization, precision errors, and repeatability were measured for two tracking devices during navigation in a phantom and in a simulated TMS study. Errors were measured in two commercial navigated TMS...

In the present study, a novel emotional complexity marker is proposed for classification of discrete emotions induced by affective video film clips. Principal Component Analysis (PCA) is applied to full-band specific phase space trajectory matrix (PSTM) extracted from short emotional EEG segment of 6 s, then the first principal component is used to measure the level of local neuronal complexity. As well, Phase Locking Value (PLV) between right and left hemispheres is estimated for in order to observe the superiority of local neuronal complexity estimation to regional neuro-cortical connectivity measurements in clustering nine discrete emotions (fear, anger, happiness, sadness, amusement, surprise, excitement, calmness, disgust) by using Long-Short-Term-Memory Networks as deep learning applications. In tests, two groups (healthy females and males aged between 22 and 33 years old) are classified with the accuracy levels of 68.52% and 79.36% through the proposed emotional complexity markers and and connectivity levels in terms of PLV in amusement. The groups are found to be statistically different (p ≪ 0.5) in amusement with respect to both metrics, even if gender difference does not lead to different neuro-cortical functions in any of the other discrete emotional states. The high deep learning classification accuracy of 98.00% is commonly obtained for discrimination of positive emotions from negative emotions through the proposed new complexity markers. Besides, considerable useful classification performance is obtained in discriminating mixed emotions from each other through full-band connectivity features. The results reveal that emotion formation is mostly influenced by individual experiences rather than gender. In detail, local neuronal complexity is mostly sensitive to the affective valance rating, while regional neurocortical connectivity levels are mostly sensitive to the affective arousal ratings.

Conclusion: In the present study, a new emotional recognition methodology has been presented. The close relationship between affective stimulus parameters and neuro-cortical activities in young females and males in nine discrete emotional states. For this purpose, PCA is applied to PSTM of short EEG segments.

The primary concept was to observe the gender effect on emotional neuro-complexity levels, and then, the second concept was to observe the usefulness of the proposed complexity markers for emotion recognition. In all applications, EEG measurements were segmented into short epochs of 6 s and 12 s in order to investigate the influence of analysis interval for emotion recognition. Conventional and deep learning networks were trained in not only instant classification but also subject classification manners for both segmentation steps. Regarding those main concepts, the results were compatible with to each other: Females were differed from males in E4 (amusement) through CL-4 for both shorter and larger segments. Thus, CL-4 provided the relatively lower performance for discrimination of E4 from baseline in comparison to recognition of the other emotional states for both segment statements. For recognition of E4 (amusement) and E8 (calmness), classification performances were increased when the subjects were classified through CL-4 instead of instant classification. When the analysis interval was largened from 6 s to 12 s, emotion recognition performances were decreased through all classifiers.

Considering emotional EEG complexity levels mediated by audio-visual affective video films, gender differences became slightly un-avoidable when subjects were classified instead of instant classification. Similarly, each emotional states were differed from baseline with very high CA levels when subjects were classified instead of instant classification. Among conventional and deep learning networks, the most useful classifier was CL-4, i.e., CNN. Although, SVM classifiers provided slightly better performance when the number of features was low, deep learning algorithms, CNNs and LSTMNs were found to be better when the large number of features were examined.

In conclusion, mixed emotions highly modulate the functional connectivity of the amygdala with the other regions of the brain. In particular, regional PCPSTM estimations characterize the dynamic signature of emotion formation depending on individual experiences driven by ongoing perception and cognition processes. From EEG signal processing point of view, primary extraction of PSTM reduces the background EEG, i.e., increases the signal-to-noise ratio. Then, application of PCA on PSTM highlights the main harmonics in association with audio-visual evoked potentials embedded in short epochs. Due to ongoing combination of excitatory and inhibitory post-synaptic potentials as well as Action Potentials (APs), EEG series are time-varying psychophysiological signals. In particular, time-varying audio-visual affective stimuli continuously cause both generation and propagation of nerve APs at auditory, visual and cognitive cortices simultaneously. Therefore, EEG segmentation and analysis of short non-overlapped epochs are crucial pre-processes for emotion recognition. As well, the most useful length of short epoch is found to be 6 s due to AP phases lasting about 2 s.

In recent neuroscience studies, it is highlighted that emotional responsiveness of individuals can be clinical support in not only rare disease so called pre-symptomatic Huntington’s disease [101] but also several important and widespread psychiatric conditions such as unipolar depression [102], Parkinson’s disease with lack of dementia [103] and autism spectrum [104] as well as amygdalar lesions [105]. Full-band PSTMCs can be proposed as single-channel emotion recognition system.

https://ieeexplore.ieee.org/document/8933102

Social anxiety (SA) has as its main feature the fear of social situations, being characterized as social phobia or social anxiety disorder when functional impairment emerges as a result of that fear. Although the recognition of the condition has increased in recent years, it is believed that many patients and physicians still take the symptoms of the disorder for personality traits with no need for treatment. There is evidence that people with SA display abnormal patterns of facial emotion processing that could account for the onset and maintenance of the disorder. The objective of this review is to describe, compare, and discuss the methods used to study facial emotion processing in SA with an emphasis on the tasks and stimuli employed. Articles were searched for on online scientific databases. Forty research articles were selected according to the inclusion and exclusion criteria established. The articles were read and information from them was gathered on a comparative table for analysis. Evidence available to date suggests that SA individuals have abnormal patterns of facial information processing characterized by a bias for negative emotions. The results of the articles analyzed have a high degree of concordance, in spite of the variety of tasks and stimuli employed. The similarity between results from non-clinical samples with SA and patients affected by social phobia speaks in favor of the current view that SA occurs as a continuum of severity, rather than a clearly circumscribed nosological entity.

Despite the variety of effective connectivity measures, few methods can quantify direct nonlinear causal couplings and most of them are not applicable to high-dimensional datasets. In this paper, a novel approach (called βmRMR-MLP-GC) is proposed to estimate direct nonlinear effective connectivity of high-dimensional datasets. βmRMR is used to select a suitable subset of candidate regressors for approximating each neural (here EEG) signal. The multilayer perceptron (MLP) is used for multivariate characterization of EEG signals while the optimum MLP structure is selected using an iterative cross-validation scheme. Finally a causality measure is defined based on Granger Causality (GC) concept to quantify the casual relations among EEG channels. Applying βmRMR-MLP-GC to high-dimensional simulated datasets with different linear and nonlinear structures yields sensitivity and specificity values higher than 95%. Also, applying it to eyes-closed resting state EEG of six normal subjects in the alpha frequency band yields significant net activity propagations from the posterior to anterior brain regions. This is in accordance with the most previous studies in this field. βmRMR-MLP-GC is compared with Granger Causality Index, Conditional Granger Causality Index, and Transfer Entropy. It outperforms these methods in terms of sensitivity and specificity in simulated datasets. Also, βmRMR-MLP-GC detects the most number of significant and reproducible Back-to-Front net information flows among the specified brain regions and highlights the posterior brain regions as dominant source of alpha activity propagation. βmRMR-MLP-GC provides a novel tool to estimate the direct nonlinear causal networks of high-dimensional datasets.

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

Discussion  and Conclusion:
In the present study, contrasting discrete emotional states have been classified with SVMs in accordance with two different kernels (RBFs vs GKs) with respect to methodological variables such as dependency methods (PC vs SC), EEG segmentation largeness (6 sec vs 12 sec), threshold definition in transforming the dependency levels into binary adjacency data (60%max and the mean). The group differences are also obtained using statistical one-way Anova tests and logistic regression modeling. The most useful results are provided by PC in larger segments in accordance with the specified threshold as the mean when RBFs are used as the kernels in SVMs. The main result of the study is to show the close correlations between emotional arousal of affective video film clips and Graph Theoretical complex network measures in terms of both segregation and integration.

Methodologically, superior performance of PC can be explained by following items: (1) PC is based on the linear relationship, while SC is based on the monotonic relationships between two variables, (2) PC can work with un-processed variables, while SC can work with rank-ordered variables. Thus, PC can be proposed for estimation of statistical dependency between EEG segments across the cortex in classifying emotions based on Graph Theoretical network analysis.

SVMs have been frequently used to classify EEG based emotional groups Torres-Valencia (2017); Doma and Pirouz (2020); Saeidi and Karwowski (2021). Since, architecture of SVMs is based on statistical learning theory that provides finding the best decision function, kernel specification affects its performance Debnath and Takahashi (2002). In the present study, RBFs are found to be superior to GKs as reported in references Bajaj and Pachori (2013); Kai (2014); Aydin (2019). In conclusion, classification performance of SVM depends on both feature space, i.e. spectral distribution of the features and kernels.

Regarding the identical dataset DREAMER, the best classification performance was obtained in analysis of shorter EEG segments (6 sec) identified by single-channel phase domain local complexity estimations in Aydin (2020), however, the recent useful results are provided in larger EEG segments (12 sec) represented by global brain network indices across the whole cortex. In Table 4, large variety of window length is given in emotion research based on EEG analysis: Due to decisive differences in both emotional stimulus types (visual, auditory, audio-visual) and experimental paradigms (inter-stimulus-inter duration, stimulus display/presentation time, recording equipment (number of recording electrodes)) as well as state definition (pleasantness in accordance with valance scores, negativity in accordance with arousal scores, discrete emotional state), the proposed segmentation length differs from each other. Moreover, emotional features have been extracted from EEG measurements by examining several methods depending on the goal such as observing neural activities at EEG recording placements (local analysis), quantifying inter-hemispheric neural communications (regional EEG analysis) and understanding the brain network mechanism across the whole cortex (global EEG analysis).

While defining this state can be based on the valence scores of the stimuli (pleasant-unpleasant), researchers have also combine the emotional states, placed in the identical quarter of circumplex emotion model (Fig. 4), in a single category (negative-positive). However, each discrete emotional states have been considered as a different state in accordance with discrete emotion model (Fig. 4) and then each discrete emotional state is identified by EEG based Graph Theoretical network measures in the present study, since both nerve action potentials superimposed by post-synaptic potentials including excitatory and inhibitory neurotransmitter activities are embedded in EEG series. Apart from brain-computer interfaces, it is crucial to assign each emotion as a separate discrete state in accordance with discrete emotion model (see Fig. 4), even if they have similar arousal-valance scores, in not only understanding the functional brain mechanism but also recognizing particular neuropsychiatric diseases characterized by perceptional deficit in computational and behavioural neuroscience. Several emotions (such as fear and anger) are considered as members of a single group in accordance with the identical quarter of arousal-valance dimensions in accordance with circumplex emotion model (see Fig. 4), although neurotransmitter activities embedded in EEG series are quite different in every emotional state.

Therefore, emotions are categorized into basic emotions and their derivatives mentioned as mixed emotions. It’s known that neurotransmitters have a great impact on emotion forming, behaviour and psychiatric disorders Ruhé et al. (2007); Liu et al. (2018); Wang et al. (2020). Three neurotransmitters of serotonin, dopamine and norephinephrine are presented as the most important neurotransmitters in psychopharmacology Schatzberg and Nemeroff (2017). The brief role of them is to change (increase/decrease) post-synaptic potentials of pyramidal nerve cells in the brain. Once temporal and spatial summation of synchronized post-synaptic potentials exceeds the threshold level, nerve action potential is generated and then propagated along with secondary neurons. Both generation and propagation of action potentials provide neural information flow across the cortex. Therefore, external stimulus type (auditory, acoustic, visual, audio-visual, somatosensorial, etc.), duration (time-locked, short duration, long duration), intensity (low-moderate-high) and content (emotional/affective, attentional, working memory, recalling memory, etc.) are all sources in releasing the specified neurotransmitters. Therefore, time-varying post-synaptic potentials including both excitatory and inhibitory actions driven by particular neurotransmitters are embedded in EEG segments. The current findings reveal the close association between emotional arousal score of external video stimuli and functional brain connectivity mechanism due to varying neurotransmitter release at pyramidal nerves

The modalities examined in previous simultaneity judgment (SJ) were limited to vision, audition, and touch. By contrast, olfaction and gustation have not been addressed to date in SJ. In this study, we constructed a measurement system for performing SJ with three cross-modal, combinations of odor, taste, and light stimuli. Odor and taste stimulators were able to stimulate to only the receptors corresponding to the modalities of each stimulus, without inducing tactile sensation. Furthermore, in order to precisely calculate the time points at which stimulus reached receptors in each trial, we monitored the presented stimuli in real time. After we calculated the actual values of stimulus onset asynchrony (SOA) between standard and comparison stimuli on the basis of the records of real-time monitoring, we evaluated the temporal distributions of simultaneous response rates in each cross-modal combination. When we fitted a Gaussian distribution to these temporal distributions, we observed low error rates in all cross-modal combinations, as demonstrated in SJ using visual, audio, and tactile stimuli. SJ using chemical stimuli and SJ using physical stimuli exhibit the same degree of measurement accuracy. We succeeded in development a high accurate measurement system for SJ using chemical stimuli. We attribute this success to the use of strict real-time monitoring of stimulus presentation.

The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain.

Although scalp potential distributions do not uniquely determine the location and configuration of neural generators, they are important because they provide the necessary conditions that any hypothesized sources must satisfy and suggest a basis for testing alternate source hypotheses. One problem that could confound the correct interpretation of scalp potentials is the choice of reference electrode. Changing the reference may make activity patterns and waveform components appear and disappear (Pascual-Marqui et al. (1988) Int. J. Neurosci., 43: 237-249). The cortical imaging technique (CIT), a method for approximating potential fields on the cortical surface, was used to test the effects of the choice of reference electrode on these fields. Simulated and empirical evoked potential scalp-recorded referential data were mathematically analyzed for the case in which the reference (linked-ears) was arbitrarily assumed to be at zero potential, and the case in which the reference was the 'average' electrode, the arithmetic mean of all of the scalp-recorded voltages in the referential montage. The results for the two references were similar. This is encouraging because potential measurements relative to a point at infinity (zero potential) are never available and the assumption that any actual reference used for a recording is at zero potential is therefore suspect.

The 6-hydroxydopamine (6-OHDA) lesion is the most widely used rat model of Parkinson's disease. A single unilateral injection of 6-OHDA into the median forebrain bundle (MFB) selectively destroys dopamine neurons in the ipsilateral substantia nigra pars compacta (SNc) and ventral tegmental area (VTA), removing more than 95% of the dopamine innervation from target areas. The stereotaxic coordinates used to deliver 6-OHDA to the MFB have been used in our laboratory successfully for more than 25 years. However, in recent years we have observed a decline in the success rate of this lesion. Previously regular success rates of >80% of rats lesioned, have become progressively more variable, with rates as low as 20% recorded in some experiments. Having excluded variability of the neurotoxin and operator errors, we hypothesized that the change seen might be due to the use of smaller rats at the time of first surgery. An attempt to proportionally adjust the lesion coordinates base on head size did not increase lesion efficacy. However, in support of the small rat hypothesis it was observed that, using the standard coordinates, rat's heads had a "nose-up" position in the stereotaxic fame. Adjustment of the nose bar to obtain a flat head position during surgery improved lesion success, and subsequent adjustments of the lesion coordinates to account for smaller head size led to a greatly increased lesion efficacy (>90%) as assessed by amphetamine induced rotation.

A touchscreen interface permits rich user interactions for research in many fields, but is rarely found within a Magnetic Resonance Imaging (MRI) environment due to difficulties adapting conventional technologies to the strong electromagnetic fields. Conventional MR-compatible video display technology uses either large-screen displays that are placed outside of the bore of the MRI itself, or projectors located beyond the participant's reach, making touch interfaces impossible. Here, we describe the MR-compatibility of the 'MRItab' in terms of MR safety and image quality. The MRItab adapts inexpensive off-the-shelf components with special signal-driver circuitry and shielding to bring the touchscreen interface into the MR environment, without adversely affecting MRI image quality, thereby making touch interfaces possible. Our testing demonstrated that the functioning of the MRItab was not affected by the functioning of the MRI scanner and that the MRItab did not adversely...