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This study investigates the network mechanisms of temporal lobe epilepsy (TLE) using MEG data, focusing on directed connectivity networks across different frequency bands. Unlike previous studies that primarily localize epileptogenic zones, this research aims to explore whole-brain network differences between left TLE (lTLE), right TLE (rTLE), and healthy controls (HCs). MEG data from 13 lTLE patients, 21 rTLE patients, and 14 HCs were source-reconstructed to 116 brain regions (AAL116). Directed Transfer Function (DTF) was used to construct directed connectivity networks, followed by networks and graph-theoretical analyses. The results indicate that, compared to HCs, TLE subjects exhibited a significant increase in average connectivity strength in the Low Gamma band. The connectivity patterns across frequency bands in TLE patients were found to be unstable. Both HC and TLE subjects demonstrated left hemisphere lateralization. In the mid-to-low frequency bands, TLE subjects showed increases in global clustering coefficient (GCC), global characteristic path length (GCPL), and local efficiency (LE) compared to HCs, which is attributed to enhanced synchronization between local brain regions in TLE subjects.

Using directed transfer function (DTF) and partial directed coherence (PDC) in the information version, this paper extends the theoretical framework to incorporate the instantaneous Granger causality (iGC) frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger connectivity, where interactions mediated without delay between time series can be easily detected.

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.

Dopamine is a key neurotransmitter in action control. However, influential theories of dopamine function make conflicting predictions about the effect of boosting dopamine neurotransmission. Here, we tested if increases in dopamine tone by administration of L-DOPA upregulate reward learning as predicted by reinforcement learning theories, and if increases are specific for deliberative “model-based” control or reflexive “model-free” control. Alternatively, L-DOPA may impair learning as suggested by “value” or “thrift” theories of dopamine. To this end, we employed a two-stage Markov decision-task to investigate the effect of L-DOPA (randomized cross-over) on behavioral control while brain activation was measured using fMRI. L-DOPA led to attenuated model-free control of behavior as indicated by the reduced impact of reward on choice. Increased model-based control was only observed in participants with high working memory capacity. Furthermore, L-DOPA facilitated exploratory behavior, particularly after a stream of wins in the task. Correspondingly, in the brain, L-DOPA decreased the effect of reward at the outcome stage and when the next decision had to be made. Critically, reward-learning rates and prediction error signals were unaffected by L-DOPA, indicating that differences in behavior and brain response to reward were not driven by differences in learning. Taken together, our results suggest that L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action. These findings provide support for the value and thrift accounts of dopamine and call for a refined integration of valuation and action signals in reinforcement learning models. •Theories make conflicting predictions about the effect of boosting dopamine.•Behaviorally, L-DOPA reduces the reflexive effect of reward (model-free control).•On average, L-DOPA does not change learning rates or model-based control.•In the brain, L-DOPA reduces reward outcome signals and their link to behavior.•Results support thrift and value theories of dopamine.

[...]in general direct application of connectivity measures to scalp EEG signals produces less than accurate results and also does not allow their clear interpretation in terms of underlying source dynamics. [...]the observed changes in the DTF can be explained by the fact that adding a sinusoid to all EEG channels with equal strength does not reflect volume conduction of a realistic brain source. Simulation Although the analytic derivation can be extended to more than two sources, computing and notating the individual elements of the mixed transfer matrix becomes quite unwieldy. [...]we simulated three cortical sources and used a realistic forward model of volume conduction from brain to scalp to numerically estimate their summed activity at three scalp EEG channels. [...]while DTF and other connectivity estimators can be applied to either scalp channels or to (estimated) source signals, results are highly likely to be more accurate when the analysis is based on source activities.

Purpose:The goal of the present study is to quantify the close association between graph theoretic global brain connectivity measures and Alzheimer’s Disease (AD) in comparison to Controls. Methods:International Mini-Mental State Examination (MMSE) was used to evaluate cognitive and neuropsychological state of the participants (AD, 12 men, 24 women, mean age = 66.4, sd = 7.9 and controls, 18 men, 11 women, mean age = 67.9, sd = 5.4). There are no comorbidities in patients. Eyes-closed 19-channel surface EEG series were collected from the 2nd Department of Neurology of AHEPA General Hospital of Thessaloniki by experienced neurologists. 2 min long resting-state recordings have been analyzed through non-overlapped sliding window of 1 second and graph theoretical connectivity indices have been estimated by using Directed Transfer Function (DTF) combined with Brain Connectivity Toolbox. EEG recordings and clinical test scores of the individuals were both downloaded from a public dataset on OpenNeuro platform (A dataset of EEG recordings from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects. https://doi.org/10.18112/openneuro.ds004504.v1.0.7.). Results:AD provided the lower measures in terms of Global Efficiency, Local Efficiency (LE) and Cluster Coefficients. LE estimations provided meaningful and significant statistical difference between patients and controls in theta (4.5–8 Hz), alpha (8.5–12 Hz), beta (12.5–30 Hz), gamma (30.5–45 Hz) sub-bands. Conclusion: The patients provided the lower segregation and integration measures than controls due to loss of connection. AD induces the considerable decrease in segregation. The brain fails to integrate cortical regions into effective networks since there is synaptic disconnection as neuropathology of AD.

New fluorescent biosensors enable the first super-resolution imaging of enzyme activity in live cells via fluorescence fluctuation increase by contact (FLINC). Compartmentalized biochemical activities are essential to all cellular processes, but there is no generalizable method to visualize dynamic protein activities in living cells at a resolution commensurate with cellular compartmentalization. Here, we introduce a new class of fluorescent biosensors that detect biochemical activities in living cells at a resolution up to threefold better than the diffraction limit. These 'FLINC' biosensors use binding-induced changes in protein fluorescence dynamics to translate kinase activities or protein–protein interactions into changes in fluorescence fluctuations, which are quantifiable through stochastic optical fluctuation imaging. A protein kinase A (PKA) biosensor allowed us to resolve minute PKA activity microdomains on the plasma membranes of living cells and to uncover the role of clustered anchoring proteins in organizing these activity microdomains. Together, these findings suggest that biochemical activities of the cell are spatially organized into an activity architecture whose structural and functional characteristics can be revealed by these new biosensors.

The methods applied for estimation of functional connectivity from multichannel data are described with special emphasis on the estimators of directedness such as directed transfer function (DTF) and partial directed coherence. These estimators based on multivariate autoregressive model are free of pitfalls connected with application of bivariate measures. The examples of applications illustrating the performance of the methods are given. Time-varying estimators of directedness: short-time DTF and adaptive methods are presented.

Background Bacterial RNA polymerase holoenzyme requires sigma70 factors to start transcription by identifying promoter elements. Cyanobacteria possess multiple sigma70 factors to adapt to a wide variety of ecological niches. These factors are grouped into two categories: primary sigma factor initiates transcription of housekeeping genes during normal growth conditions, while alternative sigma factors initiate transcription of specific genes under particular conditions. However, the present classification does not consider the modular organization of their structural domains, introducing therefore multiple functional and structural biases. A comprehensive analysis of this protein family in cyanobacteria is needed to address these limitations. Results We investigated the structure and evolution of sigma70 factors in cyanobacteria, analyzing their modular architecture and variation among unicellular, filamentous, and heterocyst-forming morphotypes. 4,193 sigma70 homologs were found with 59 distinct modular patterns, including six essential and 29 accessory domains, such as DUF6596. 90% of cyanobacteria typically have 5 to 17 sigma70 homologs and this number likely depends on the strain morphotype, the taxonomic order and the genome size. We classified sigma70 factors into 12 clans and 36 families. According to taxonomic orders and phenotypic traits, the number of homologs within the 14 main families was variable, with the A.1 family including the primary sigma factor since this family was found in all cyanobacterial species. The A.1, A.5, C.1, E.1, J.1, and K.1 families were found to be key sigma families that distinguish heterocyst-forming strains. To explain the diversification and evolution of sigma70, we propose an evolutionary scenario rooted in the diversification of a common ancestor of the A1 family. This scenario is characterized by evolutionary events including domain losses, gains, insertions, and modifications. The high occurrence of the DUF6596 domain in bacterial sigma70 proteins, and its association with the highest prevalence observed in Actinobacteria, suggests that this domain might be important for sigma70 function. It also implies that the domain could have emerged in Actinobacteria and been transferred through horizontal gene transfer. Conclusion Our analysis provides detailed insights into the modular domain architecture of sigma70, introducing a novel robust classification. It also proposes an evolutionary scenario explaining their diversity across different taxonomical orders.

Depression is a mental disease involved in emotional and cognitive impairments. Neuroimaging studies have found abnormalities in the structure and functional network of brain for major depressive disorder (MDD).However, neural mechanism of the dynamic connectivity for emotional attention of MDD is currently insufficient. In this study, event-related potentials (ERP) and time-varying network were analyzed to investigate attention bias and corresponding neural mechanisms induced by emotional facial stimuli. In the ERP results, N100 components in MDD had shorter latencies and smaller amplitudes than those in healthy controls (HC) for sad and fear faces. The P200 amplitudes induced by sad faces in MDD were significantly higher than those induced by happy and fear faces in MDD, and those induced by sad faces in HC. It was indicated that MDD patients had attention bias towards sad faces. For the time-varying network analysis, adaptive directed transfer function was explored to construct dynamic network connectivity. MDD patients had stronger information outflow from the right frontal region and weaker information outflow from parieto-occipital regions for sad faces. In addition, the network properties of sad faces were significantly correlated with PHQ-9 scores for MDD group. These findings may provide further explanation for understanding the MDD’s neural mechanism of attention bias during facial emotional tasks.