Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

EEG cortical network reveals the temporo-spatial mechanism of visual search

This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.

EEG based functional brain networks analysis in dyslexic children during arithmetic task

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task. Brain functional network measures such as node strength, clustering coefficient, characteristic pathlength and small-world were calculated using graph theory methods for both groups. Task performance showed significantly less performance accuracy in dyslexics against NDC. The neural findings showed increased connectivity in the delta band and reduced connectivity in theta, alpha, and beta band at temporoparietal, and prefrontal regions in dyslexic group while performing the task. The node strengths were found to be significantly high in delta band (T3, O1, F8 regions) and low in theta (T5, P3, Pz regions), beta (Pz) and gamma band (T4 and prefrontal regions) during the task in dyslexics compared to the NDC. The clustering coefficient was found to be significantly low in the dyslexic group (theta and alpha band) and characteristic pathlength was found to be significantly high in the dyslexic group (theta and alpha band) compared to the NDC group while performing task. In conclusion, the present study shows evidence for poor fact-retrieval mechanism and altered network topology in dyslexic brain networks during arithmetic task performance.

Subchondral Drilling Independent of Drill Hole Number Improves Articular Cartilage Repair and Reduces Subchondral Bone Alterations Compared With Debridement in Adult Sheep

BACKGROUND

Subchondral drilling is an established marrow stimulation technique for small cartilage defects, but whether drilling is required at all and if the drill hole density affects repair remains unclear.

HYPOTHESES

Osteochondral repair is improved when the subchondral bone is perforated by a higher number of drill holes per unit area, and drilling is superior to defect debridement alone.

STUDY DESIGN

Controlled laboratory study.

METHODS

Rectangular full-thickness chondral defects (4 × 8 mm) were created in the trochlea of adult sheep (N = 16), debrided down to the subchondral bone plate without further treatment as controls (no treatment; n = 7) or treated with either 2 or 6 (n = 7 each) subchondral drill holes (diameter, 1.0 mm; depth, 10.0 mm). Osteochondral repair was assessed at 6 months postoperatively by standardized (semi-)quantitative macroscopic, histological, immunohistochemical, biochemical, and micro-computed tomography analyses.

RESULTS

Compared with defect debridement alone, histological overall cartilaginous repair tissue quality (P = .025) and the macroscopic aspect of the adjacent cartilage (P≤ .032) were improved after both drilling densities. Only drilling with 6 holes increased type 2 collagen content in the repair tissue compared with controls (P = .038). After debridement, bone mineral density was significantly decreased in the subchondral bone plate (P≤ .015) and the subarticular spongiosa (P≤ .041) compared with both drilling groups. Debridement also significantly increased intralesional osteophyte sectional area compared with drilling (P≤ .034). No other differences in osteochondral repair existed between subchondral drilling with 6 or 2 drill holes.

CONCLUSION

Subchondral drilling independent of drill hole density significantly improves structural cartilage repair compared with sole defect debridement of full-thickness cartilage defects in sheep after 6 months. Subchondral drilling also leads to a better reconstitution of the subchondral bone compartment below the defects. Simultaneously, drilling reduced the formation of intralesional osteophytes caused by osseous overgrowth compared with debridement.

CLINICAL RELEVANCE

These results have important clinical implications, as they support subchondral drilling independent of drill hole number but discourage debridement alone for the treatment of small cartilage defects. Clinical studies are warranted to further quantify the effects of subchondral drilling in similar settings.

Effective connectivity in cortical networks during deception: A lie detection study using EEG

Previous studies have identified activated regions associated with deceptive tasks and most of them utilized time, frequency, or temporal features to identify deceptive responses. However, when deception behaviors occur, the functional connectivity pattern and the communication between different brain areas remain largely unclear. In this study, we explored the most important information flows between different brain cortices during deception. First, we employed the guilty knowledge test protocol and recorded on 64 electrodes electroencephalogram (EEG) signals from 30 subjects (15 guilty and 15 innocent). EEG source estimation was then performed to compute the cortical activities on the 24 regions of interest (ROIs). Next, effective connectivity was calculated by partial directed coherence (PDC) analysis applied to the cortical signals. Furthermore, based on the graph-theoretical analysis, the network parameters with significant differences were extracted as features to identify two groups of subjects. In addition, the ROIs frequently involved in the above network parameters were selected, and based on the difference in the group mean of PDC values of all the edges connected with the selected ROIs, we presented the strongest information flows (MIIF) in the guilty group relative to the innocent group. Experimental results first show that the optimal classification features are mainly in-degree and out-degree measures of the ROI and the high classification accuracy for four bands demonstrated that the proposed method is suitable for lie detection. In addition, the frontoparietal network was found to be most prominent among all the MIIFs in four bands. Finally, combining the neurophysiology signification of four frequency bands, respectively, we analyzed the roles of all the important information flows to uncover the underlying cognitive processes and mechanisms used in deception.

NIRS Studies Show Reduced Interhemispheric Functional Connectivity in Individuals with Multiple Sclerosis That Exhibit Cortical Hypoxia

Many with multiple sclerosis (MS) have low cortical microvascular oxygen levels (hypoxia), which have been previously proposed to exacerbate inflammation in MS. We do not know if hypoxia impacts or relates to brain function. We hypothesise that within the MS population, those who have hypoxia may show reduced brain functional connectivity (FC). We recruited 20 MS participants and grouped them into normoxic and hypoxic groups (n = 10 in each group) using frequency-domain near-infrared spectroscopy (fdNIRS). Functional coherence of the haemodynamic signal, quantified with functional near-infrared spectroscopy (fNIRS) was used as a marker of brain function and was carried out during resting-state, finger-tapping, and while completing two neurocognitive tasks. Reduced FC was detected in the hypoxic MS group. fNIRS measures of haemodynamic coherence in MS could be a biomarker of functional impairment and/or disease progression.

Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis

This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG are mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

The Role of Reward System in Dishonest Behavior: A Functional Near-Infrared Spectroscopy Study

Previous studies showed that the cortical reward system plays an important role in deceptive behavior. However, how the reward system activates during the whole course of dishonest behavior and how it affects dishonest decisions remain unclear. The current study investigated these questions. One hundred and two participants were included in the final analysis. They completed two tasks: monetary incentive delay (MID) task and an honesty task. The MID task served as the localizer task and the honesty task was used to measure participants' deceptive behaviors. Participants' spontaneous responses in the honesty task were categorized into three conditions: Correct-Truth condition (tell the truth after guessing correctly), Incorrect-Truth condition (tell the truth after guessing incorrectly), and Incorrect-Lie condition (tell lies after guessing incorrectly). To reduce contamination from neighboring functional regions as well as to increase sensitivity to small effects (Powell et al., Devel Sci 21:e12595, 2018), we adopted the individual functional channel of interest (fCOI) approach to analyze the data. Specially, we identified the channels of interest in the MID task in individual participants and then applied them to the honesty task. The result suggested that the reward system showed different activation patterns during different phases: In the pre-decision phase, the reward system was activated with the winning of the reward. During the decision and feedback phase, the reward system was activated when people made the decisions to be dishonest and when they evaluated the outcome of their decisions. Furthermore, the result showed that neural activity of the reward system toward the outcome of their decision was related to subsequent dishonest behaviors. Thus, the present study confirmed the important role of the reward system in deception. These results can also shed light on how one could use neuroimaging techniques to perform lie-detection.

Interview based connectivity analysis of EEG in order to detect deception

Deception is mentioned as an expression or action which hides the truth and deception detection as a concept to uncover the truth. In this research, a connectivity analysis of Electro Encephalography study is presented regarding cognitive processes of an instructed liar/truth-teller about identity during an interview. In this survey, connectivity analysis is applied because it can provide unique information about brain activity patterns of lying and interaction among brain regions. The novelty of this paper lies in applying an open-ended questions interview protocol during EEG recording. We recruited 40 healthy participants to record EEG signal during the interview. For each subject, whole-brain functional and effective connectivity networks such as coherence, generalized partial direct coherence and directed directed transfer function, are constructed for the lie-telling and truth-telling conditions. The classification results demonstrate that lying could be differentiated from truth-telling with an accuracy of 86.25% with the leave-one-person-out method. Results show functional and effective connectivity patterns of lying for the average of all frequency bands are different in regions from that of truth-telling. The current study may shed new light on neural patterns of deception from connectivity analysis view point.

Single-Trial Decoding from Local Field Potential Using Bag of Word Representation

Neural decoding allows us to study the brain functions by investigating the relationship between a stimulus and the corresponding response. Recently, the local field potential (LFP) has been targeted as a hallmark of brain activity for neural decoding. Despite several decoding methods, there is still a lack of a comprehensive framework to decode cognitive functions in an integrated structure. Here, we addressed this issue by developing a dictionary-based method to represent the LFP signals via a bag-of-words (BOW) approach. First, we defined a general dictionary consisting of various Gabor wavelets as the words which enabled us to represent LFPs in word domain. For each trial, the LFP signal was convolved with the dictionary words. The integral of the absolute value and the mean phase of the complex output were considered as histogram weights. In the next step, using cross-validation leave-one-out method, the trials were split into the training and test sets. The weights of each individual word were swapped across trials within a certain category of the training set while the sequential order was maintained. Finally, the test trial was classified using label voting in the k-nearest training trials. We conducted the proposed method on two independent LFP data sets, recorded from the rat primary auditory cortex (A1) and monkey middle temporal area in order to evaluate its efficiency. In addition to the chance level, the proposed method was compared with a standard BOW approach that has been extended recently for biomedical signals classification. Results show a high efficiency (~ 15% improvement in decoding accuracy) of the proposed method. Together, the aforementioned method provides a comprehensive framework for single-trial decoding from short-length electrophysiological signals.

Functional Connectivity Pattern Analysis Underlying Neural Oscillation Synchronization during Deception

To characterize system cognitive processes during deception, event-related coherence was computed to investigate the functional connectivity among brain regions underlying neural oscillation synchronization. In this study, 15 participants were randomly assigned to honesty or deception groups and were instructed to tell the truth or lie when facing certain stimuli. Meanwhile, event-related potential signals were recorded using a 64-channel electroencephalography cap. Event-related coherence was computed separately in four frequency bands (delta (1-3.5 Hz), theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 HZ)) for the long-range intrahemispheric electrode pairs (F3P3, F4P4, F3T7, F4T8, F3O1, and F4O2). The results indicated that deceptive responses elicited greater connectivities in the frontoparietal and frontotemporal networks than in the frontooccipital network. Furthermore, the deception group displayed lower values of coherence in the frontoparietal electrode pairs in the alpha and beta bands than the honesty group. In particular, increased coherence in the delta and theta bands on specific left frontoparietal electrode pairs was observed. Additionally, the deception group exhibited higher values of coherence in the delta band and lower values of coherence in the beta band on the frontotemporal electrode pairs than did the honesty group. These data indicated that the active cognitive processes during deception include changes in ensemble activities between the frontal and parietal/temporal regions.

Deception Decreases Brain Complexity

Extensive evidence suggests the feasibility of lie detection using electroencephalograms (EEGs). However, it is largely unknown whether there are any differences in the nonlinear features of EEGs between guilty and innocent subjects. In this study, we proposed a complexity-based method to distinguish lying from truth telling. A total of 35 participants were randomly divided into two groups, and their EEG signals were recorded with 14 electrodes. Averages for sequential sets of five trials were first calculated for the probe responses within each subject. Next, a common wavelet entropy (WE) measure and an improved one were used to quantify complexity from each five-trial average. The results show that for both measures, the WE values in the guilty subjects are statistically lower than those in the innocent subjects for most of the 14 electrodes. More importantly, using the improved measure, the difference in WE between the two groups of subjects significantly increases for 11 brain regions compared with the values from the common measure. Finally, the highest balanced classification accuracy, 89.64%, is achieved when using the combined WE feature vector in five brain regions from the sites of Pz, P3, C4, Cz, and C3. Our findings indicate that the lying task elicits a more ordered brain activity in some specific brain regions than the task of telling the truth. This study not only demonstrates that improved WE measurements could be a powerful quantitative index for detecting lying but also sheds light on the brain mechanisms underlying deceptive behaviors.

Dynamic Default Mode Network across Different Brain States

The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states.