Pushing forward: exploring the impact of the sitting position on muscle activation patterns and force generation during paralympic sit-cross-country skiing.

Paralympic cross-country sit-skiing is a discipline of the Paralympic Winter Games where athletes use a specialized sledge. Athletes are classified into different groups according to their functional abilities. The double poling technique is used to push the sledge forward and generate speed. Different sitting positions in the sledge are used based on the individual impairment. To date there is no data available on the effects of these different positions on muscle activation patterns. The aim of this study was to analyze the muscle activation patterns of the trunk and upper body muscles in relation to the poling force. Nine Able-bodied athletes were tested on a treadmill at submaximal speed in three sitting positions for 4 min in a flat and uphill condition. Sitting positions included a "knee-high" position, a "knee-low" position, and a "neutral" position with the sitting platform parallel to the ground. Unilateral pole forces and surface EMG from three trunk muscles, two upper limb muscles, and one lower limb muscle were recorded simultaneously on the dominate side. Data were segmented into individual cycles and mean values and standard deviations calculated for each subject and condition. Statistical analyses, including a Friedman test and Bonferroni correction, were applied to examine significant differences across different sitting positions. Our findings demonstrate that while certain muscle groups such as the erector spinae and triceps show consistent patterns of activation across different sitting positions, there is considerable variability among individual athletes, suggesting individualized strategies for task execution. Overall, force application was most efficient in the "knee low" position with 691.33 ± 148.83 N and least efficient in the "knee high" position with 582.81 ± 115.11 N. Testing impaired athletes will be the next step in understanding the neurophysiological aspects of the poling movement. This experimental protocol provides a basis for understanding the movement of paralympic cross-country sit-skiing in greater depth.

Locus coeruleus co-activation patterns at rest show higher state persistence in patients with dissociative seizures: A Pilot Study.

OBJECTIVE: Dissociative seizures are paroxysmal disruptions of awareness and behavioral control in the context of affective arousal. Alterations in stress-related endocrine function have been demonstrated, but the timescale of dissociation suggests that the central locus coeruleus (LC) noradrenergic system is likely pivotal. Here, we investigate whether LC activation at rest is associated with altered brain network dynamics. METHODS: A preliminary co-activation pattern (CAP) analysis of resting-state functional magnetic resonance imaging (fMRI) in 14 patients with dissociative seizures and 14 healthy controls was performed by using the LC as a seeding region. The red nucleus served as a control condition. Entry rates, durations, and state transition probabilities of identified CAPs were calculated. Analyses were corrected for demographic, technical, and clinical confounders including depression and anxiety. RESULTS: Three LC-related CAPs were identified, with the dominant two showing inverse activations and deactivations of the default mode network and the attention networks, respectively. Analysis of transition probabilities between and within the three CAPs revealed higher state persistence in patients compared to healthy controls for both CAP2LC (Cohen's d = -0.55; p = 0.01) and CAP3LC (Cohen's d = -0.57; p = 0.01). The control analysis using the red nucleus as a seed yielded similar CAPs, but no significant between-group differences in transition probabilities. SIGNIFICANCE: Higher state persistence of LC-CAPs in patients with dissociative seizures generates the novel hypothesis that arousal-related impairments of network switching might be a candidate neural mechanism of dissociation. PLAIN LANGUAGE SUMMARY: Dissociative seizures often arise during high affective arousal. The locus coeruleus is a brain structure involved in managing such acute arousal states. We investigated whether the activity of the locus coeruleus correlates with activity in other regions of the brain (which we refer to as "brain states"), and whether those brain states were different between patients with dissociative seizures and healthy controls. We found that patients tended to stay in certain locus coeruleus-dependent brain states instead of switching between them. This might be related to the loss of awareness and disruptions of brain functions ("dissociation") that patients experience during seizures.

Validation of neuron activation patterns for artificial intelligence models in oculomics.

Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the field of oculomics; the association between the retina and systemic health. Unlike conventional AI models trained on well-recognized retinal features, the retinal phenotypes that most oculomics models use are more subtle. Consequently, applying conventional tools, such as saliency maps, to understand how oculomics models arrive at their inference is problematic and open to bias. We hypothesized that neuron activation patterns (NAPs) could be an alternative way to interpret oculomics models, but currently, most existing implementations focus on failure diagnosis. In this study, we designed a novel NAP framework to interpret an oculomics model. We then applied our framework to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset. We found that the NAP generated from our framework was correlated to the clinically relevant endpoint of cardiovascular risk. Our NAP was also able to discern two biologically distinct groups among participants who were assigned the same predicted systolic blood pressure. These results demonstrate the feasibility of our proposed NAP framework for gaining deeper insights into the functioning of oculomics models. Further work is required to validate these results on external datasets.

Evolving Dynamics of Neck Muscle Activation Patterns in Dental Students: A Longitudinal Study.

Cervical pain has been linked to increased motor unit activity, potentially associated with the initiation and progression of chronic neck pain. Therefore, this study aimed to compare the time-course changes in cervical superficial muscle activation patterns among dental students with and without neck pain throughout their initial semester of clinical training. We used an online Nordic Musculoskeletal Questionnaire for group allocation between neck pain (NP) (n = 21) and control group (CG) (n = 23). Surface electromyography (sEMG) of the sternocleidomastoid and upper bilateral trapezius was recorded before starting their clinical practice and after their first semester while performing a cranio-cervical flexion test (CCFT) in five increasing levels between 22 mmHg and 30 mmHg. After the first semester, both the CG (p < 0.001) and NP (p = 0.038) groups showed decreased sternocleidomastoid activation. The NP group exhibited a concomitant increase in upper trapezius coactivation (p < 0.001), whereas the muscle activation pattern in asymptomatic students remained unchanged (p = 0.980). During the first semester of clinical training, dental students exhibited decreased superficial flexor activity, but those with neck pain had increased co-contraction of the upper trapezius, likely to stabilize the painful segment. This altered activation pattern could be associated with further dysfunction and symptoms, potentially contributing to chronicity.

Alterations in neural circuit dynamics between the limbic network and prefrontal/default mode network in patients with generalized anxiety disorder.

BACKGROUND: Widespread functional alterations have been implicated in patients with generalized anxiety disorder (GAD). However, most studies have primarily focused on static brain network features in patients with GAD. The current research focused on exploring the dynamics within functional brain networks among individuals diagnosed with GAD. METHODS: Seventy-five participants were divided into patients with GAD and healthy controls (HCs), and resting-state functional magnetic resonance imaging data were collected. The severity of symptoms was measured using the Hamilton Anxiety Scale and the Patient Health Questionnaire. Co-activation pattern (CAP) analysis, centered on the bed nucleus of the stria terminalis, was applied to explore network dynamics. The capability of these dynamic characteristics to distinguish between patients with GAD and HCs was evaluated using a support vector machine. RESULTS: Patients with GAD exhibited disruptions in the limbic-prefrontal and limbic-default-mode network circuits. Particularly noteworthy was the marked reduction in dynamic indicators such as occurrence, EntriesFromBaseline, ExitsToBaseline, in-degree, out-degree, and resilience. Moreover, these decreased dynamic features effectively distinguished the GAD group from the HC in this study. CONCLUSIONS: The current findings revealed the underlying brain networks associated with compromised emotion regulation in individuals with GAD. The dynamic reduction in connectivity between the limbic-default mode network and limbic-prefrontal networks could potentially act as a biomarker and therapeutic target for GAD in the future.

Alternative muscle synergy patterns of upper limb amputees.

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.

Co-activation patterns during viewing of different video game genres.

Past research has revealed cognitive improvements resulting from engagement with both traditional action video games and newer action-like video games, such as action real-time strategy games (ARSG). However, the cortical dynamics elicited by different video gaming genres remain unclear. This study explored the temporal dynamics of cortical networks in response to different gaming genres. Functional magnetic resonance imaging (fMRI) data were obtained during eye-closed resting and passive viewing of gameplay videos of three genres: life simulation games (LSG), first-person shooter games (FPS), and ARSG. Data analysis used a seed-free Co-Activation Pattern (CAP) based on Regions of Interest (ROIs). When comparing the viewing of action-like video games (FPS and ARSG) to LSG viewing, significant dynamic distinctions were observed in both primary and higher-order networks. Within action-like video games, compared to FPS viewing, ARSG viewing elicited a more pronounced increase in the Fraction of Time and Counts of attentional control-related CAPs, along with an increased Transition Probability from sensorimotor-related CAPs to attentional control-related CAPs. Compared to ARSG viewing, FPS viewing elicited a significant increase in the Fraction of Time of sensorimotor-related CAPs, when gaming experience was considered as a covariate. Thus, different video gaming genres, including distinct action-like video gaming genres, elicited unique dynamic patterns in whole-brain CAPs, potentially influencing the development of various cognitive processes.

Transient resting-state salience-limbic co-activation patterns in functional neurological disorders.

BACKGROUND: Functional neurological disorders were historically regarded as the manifestation of a dynamic brain lesion which might be linked to trauma or stress, although this association has not yet been directly tested yet. Analysing large-scale brain network dynamics at rest in relation to stress biomarkers assessed by salivary cortisol and amylase could provide new insights into the pathophysiology of functional neurological symptoms. METHODS: Case-control resting-state functional magnetic resonance imaging study of 79 patients with mixed functional neurological disorders (i.e., functional movement disorders, functional seizures, persistent perceptual-postural dizziness) and 74 age- and sex-matched healthy controls. Using a two-step hierarchical data-driven neuroimaging approach, static functional connectivity was first computed between 17 resting-state networks. Second, dynamic alterations in these networks were examined using co-activation pattern analysis. Using a partial least squares correlation analysis, the multivariate pattern of correlation between altered temporal characteristics and stress biomarkers as well as clinical scores were evaluated. RESULTS: Compared to healthy controls, patients presented with functional aberrancies of the salience-limbic network connectivity. Thus, the insula and amygdala were selected as seed-regions for the subsequent analyses. Insular co-(de)activation patterns related to the salience network, the somatomotor network and the default mode network were detected, which patients entered more frequently than controls. Moreover, an insular co-(de)activation pattern with subcortical regions together with a wide-spread co-(de)activation with diverse cortical networks was detected, which patients entered less frequently than controls. In patients, dynamic alterations conjointly correlated with amylase measures and duration of symptoms. CONCLUSION: The relationship between alterations in insular co-activation patterns, stress biomarkers and clinical data proposes inter-related mechanisms involved in stress regulation and functional (network) integration. In summary, altered functional brain network dynamics were identified in patients with functional neurological disorder supporting previously raised concepts of impaired attentional and interoceptive processing.

The co-activation pattern between the DMN and other brain networks affects the cognition of older adults: evidence from naturalistic stimulation fMRI data.

Brain function changes affect cognitive functions in older adults, yet the relationship between cognition and the dynamic changes of brain networks during naturalistic stimulation is not clear. Here, we recruited the young, middle-aged and older groups from the Cambridge Center for Aging and Neuroscience to investigate the relationship between dynamic metrics of brain networks and cognition using functional magnetic resonance imaging data during movie-watching. We found six reliable co-activation pattern (CAP) states of brain networks grouped into three pairs with opposite activation patterns in three age groups. Compared with young and middle-aged adults, older adults dwelled shorter time in CAP state 4 with deactivated default mode network (DMN) and activated salience, frontoparietal and dorsal-attention networks (DAN), and longer time in state 6 with deactivated DMN and activated DAN and visual network, suggesting altered dynamic interaction between DMN and other brain networks might contribute to cognitive decline in older adults. Meanwhile, older adults showed easier transfer from state 6 to state 3 (activated DMN and deactivated sensorimotor network), suggesting that the fragile antagonism between DMN and other cognitive networks might contribute to cognitive decline in older adults. Our findings provided novel insights into aberrant brain network dynamics associated with cognitive decline.

Human brain state dynamics reflect individual neuro-phenotypes.

Neural activity and behavior manifest state and trait dynamics, as well as variation within and between individuals. However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Atrial fibrillation activation patterns predict freedom from arrhythmias after catheter ablation: utility of ExTRa mapping™.

Background: Mechanisms underlying atrial fibrillation (AF) are widely complex and vary tremendously among individuals. Objectives: This retrospective study aimed to investigate the association between AF activation patterns and clinical outcomes post-ablation. Methods: Fifty-five AF patients (64.0 ± 12.9 years; 41 men; 17 paroxysmal) underwent bi-atrial endocardial driver mapping during AF pre-ablation with a real-time phase mapping system (ExTRa Mapping). The nonpassively activated ratio (%NP) of meandering rotors and multiple wavelets relative to the recording time was evaluated in 26 atrial segments [15 in the left atrium (LA) and 11 in the right atrium]. Irrespective of the mapping results, all patients underwent standard AF ablation via cryoballoons and/or radiofrequency catheters. Results: In a median follow-up interval of 27(14-30) months, 69.1% of patients were free from recurrent arrhythmias and antiarrhythmic drugs at one year post-procedure. Patients with recurrent AF were more likely to have non-paroxysmal AF, a significantly larger LA size, and higher LA maximal %NP(LAmax%NP) and LA anterior wall %NP(LAAW%NP) than those without recurrent AF. A multivariate Cox regression analysis showed that both an LAmax%NP (hazard ratio [HR] = 1.075; 95% confidence interval [CI] = 1.02-1.14, p = 0.012) and LAAW%NP (HR = 1.061; 95% CI = 1.01-1.11, p = 0.013) were independent predictors of atrial arrhythmia recurrence. The optimal cutoff points for the LAmax%NP and LAAW%NP for predicting AF recurrence were 64.5% and 60.0%, respectively. A Kaplan-Meier analysis demonstrated that both an LAmax%NP > 64.5% (p = 0.0062) and LAAW%NP > 60.0% (p = 0.014) were associated with more frequent AF recurrences. Conclusion: Baseline AF activation pattern mapping may aid in predicting freedom from arrhythmias after standard AF ablation procedures.

Distorted image classification using neural activation pattern matching loss.

In image classification, a deep neural network (DNN) that is trained on undistorted images constitutes an effective decision boundary. Unfortunately, this boundary does not support distorted images, such as noisy or blurry ones, leading to accuracy drop-off. As a simple approach for classifying distorted images as well as undistorted ones, previous methods have optimized the trained DNN again on both kinds of images. However, in these methods, the decision boundary may become overly complicated during optimization because there is no regularization of the decision boundary. Consequently, this decision boundary limits efficient optimization. In this paper, we study a simple yet effective decision boundary for distorted image classification through the use of a novel loss, called a "neural activation pattern matching (NAPM) loss". The NAPM loss is based on recent findings that the decision boundary is a piecewise linear function, where each linear segment is constructed from a neural activation pattern in the DNN when an image is fed to it. The NAPM loss extracts the neural activation patterns when the distorted image and its undistorted version are fed to the DNN and then matches them with each other via the sigmoid cross-entropy. Therefore, it constrains the DNN to classify the distorted image and its undistorted version by the same linear segment. As a result, our loss accelerates efficient optimization by preventing the decision boundary from becoming overly complicated. Our experiments demonstrate that our loss increases the accuracy of the previous methods in all conditions evaluated.

Significant Delayed Activation on the Right Ventricular Outflow Tract Represents Complete Right Bundle-Branch Block Pattern in Brugada Syndrome.

Background The appearance of complete right bundle-branch block (CRBBB) in Brugada syndrome (BrS) is associated with an increased risk of ventricular fibrillation. The pathophysiological mechanism of CRBBB in patients with BrS has not been well established. We aimed to clarify the significance of a conduction delay zone associated with arrhythmias on CRBBB using body surface mapping in patients with BrS. Methods and Results Body surface mapping was recorded in 11 patients with BrS and 8 control patients both with CRBBB. CRBBB in control patients was transiently exhibited by unintentional catheter manipulation (proximal RBBB). Ventricular activation time maps were constructed for both of the groups. We divided the anterior chest into 4 areas (inferolateral right ventricle [RV], RV outflow tract [RVOT], intraventricular septum, and left ventricle) and compared activation patterns between the 2 groups. Excitation propagated to the RV from the left ventricle through the intraventricular septum with activation delay in the entire RV in the control group (proximal RBBB pattern). In 7 patients with BrS, excitation propagated from the inferolateral RV to the RVOT with significant regional activation delay. The remaining 4 patients with BrS showed a proximal RBBB pattern with the RVOT activation delay. The ventricular activation time in the inferolateral RV was significantly shorter in patients with BrS without a proximal RBBB pattern than in control patients. Conclusions The CRBBB morphology in patients with BrS consisted of 2 mechanisms: (1) significantly delayed conduction in the RVOT and (2) proximal RBBB with RVOT conduction delay. Significant RVOT conduction delay without proximal RBBB resulted in CRBBB morphology in patients with BrS.

Safety and efficacy of catheter ablation on patients with persistent atrial fibrillation by targeting repetitive activation patterns and focal impulses.

BACKGROUND: The study is intended to evaluate the acute and long-term effectiveness and peri-procedural safety in ablation of persistent atrial fibrillation (PsAF) using the CartoFinder algorithm guided ablation (CFGA) targeting on repetitive activation patterns (RAPs) and focal impulses (FIs) identified in dynamic maps. METHODS: This is a prospective, single-arm, multicenter study. A 64-pole multielectrode basket catheter was used for intracardiac global electrogram (EGM) mapping. The RAPs or FIs were repeatedly mapped and ablated for up to five iterations by the CartoFinder algorithm to achieve sinus rhythm (SR) or organized atrial tachycardia (AT), which were followed by PVI. All patients were followed up for 12 months after procedure. RESULTS: Sixty-four PsAF patients (age, 60.7 ± 9.1 years; male, 76.6%; median PsAF duration, 6.0 months) underwent CFGA on RAPs/FIs. Six patients (9.4%) reported primary adverse event (PAE) including groin hematoma (2), complete heart block (1), tamponade (1), pericarditis (1), and pseudoaneurysm (1). Repeated mapping and ablation on RAPs/FIs resulted in the cycle length (CL) increase from 191.0 ± 167.6 ms at baseline to 365.7 ± 296.7 ms in the LA and from 167.8 ± 41.6 ms to 379.4 ± 293.5 ms in the RA and 30.2% (19/63) AF termination to SR or organized AT. The 12-month arrhythmia-free and symptomatic AF-free rates were 60.9% and 75.0%, respectively. Patients with acute AF termination showed a higher 12-month arrhythmia-free rate (76.9%) than those without (50.0%, p = .04). CONCLUSIONS: The study demonstrated that the CartoFinder algorithm can be used for global activation mapping during PsAF ablation. Patients with acute AF termination had a lower 12-month AF recurrence rate compared to patients without.

The properties of long-term potentiation at SC-CA1/ TA-CA1 hippocampal synaptic pathways depends upon their input pathway activation patterns.

Long-term potentiation (LTP) has been considered as a cellular mechanism of memory. Since the Schaffer collateral (SC) and temporoammonic (TA) inputs to CA1 are distinct synaptic pathways that could mediate different cognitive functions, this study was therefore aimed to separately study and compare the properties of LTP of these two synaptic pathways. In the current study we used slice electrophysiological methods to compare various properties of these two synaptic pathways in response to single, paired pulse stimulation, and to three standard protocols for inducing LTP: the high frequency electrical stimulation (HFS), theta-burst (TBS), and primed burst (PBs) stimulation. We found that the SC-CA1 synapses could produce bigger maximum synaptic responses than TA-CA1 synapses. In addition, we showed that paired-pulse ratios of the SC-CA1 synapses were higher than TA-CA1 synapses at certain inter-pulses intervals. Finally, we showed a higher LTP% was induced by PBs or TBS at the SC-CA1 synapse than the TA-CA1 synapse. Briefly, our findings suggest the differential basal synaptic transmission, paired-pulse evoked synaptic responses, and LTP exhibition of the hippocampal SC-CA1/ TA-CA1 synaptic pathways, which may rely on spontaneous and evoked activity pattern at the local circuit level.

Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium.

Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.

Deficient dynamics of prefrontal-striatal and striatal-default mode network (DMN) neural circuits in internet gaming disorder.

BACKGROUND: Studies have proven that individuals with internet gaming disorder (IGD) show impaired cognitive control over game craving; however, the neural mechanism underlying this process remains unclear. Accordingly, the present study aimed to investigate the dynamic features of brain functional networks of individuals with IGD during rest, which have barely been understood until now. METHODS: Resting-state fMRI data were collected from 333 subjects (123 subjects with IGD (males/females: 73/50) and 210 healthy controls (males/females: 135/75)). First, the data-driven methodology, named co-activation pattern analysis, was applied to investigate the dynamic features of nucleus accumbens (the core region involved in craving/reward processing and addiction)-centered brain networks in IGD. Further, machine learning analysis was conducted to investigate the prediction effect of the dynamic features on participants' addiction severity. RESULTS: Compared to controls, subjects in the IGD group showed decreased resilience, betweenness centrality and occurrence in the prefrontal-striatal neural circuit, and decreased in-degree in the striatal-default mode network (DMN) circuit. Moreover, these decreased dynamic features could significantly predict participants' addiction severity. LIMITATIONS: The causal relationship between IGD and the abnormal dynamic features cannot be identified in this study. All the subjects were university students. CONCLUSIONS: The present results revealed the underlying brain networks of uncontrollable craving and game-seeking behaviors in individuals with IGD during rest. The decreased dynamics of the prefrontal-striatal and striatal-DMN neural circuits might be potential biomarkers for predicting the addiction severity of IGD and potential targets for effective interventions to reduce game craving of this disorder.

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression.

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

The neural coding of tonal working memory load: An functional magnetic resonance imaging study.

Tonal working memory load refers to the number of pitches held in working memory. It has been found that different verbal working memory loads have different neural coding (local neural activity pattern). However, whether there exists a comparable phenomenon for tonal working memory load remains unclear. In this study, we used a delayed match-to-sample paradigm to evoke tonal working memory. Neural coding of different tonal working memory loads was studied with a surface space and convolution neural network (CNN)-based multivariate pattern analysis (SC-MVPA) method. We found that first, neural coding of tonal working memory was significantly different from that of the control condition in the bilateral superior temporal gyrus (STG), supplement motor area (SMA), and precentral gyrus (PCG). Second, neural coding of nonadjacent tonal working memory loads was distinguishable in the bilateral STG and PCG. Third, neural coding is gradually enhanced as the memory load increases. Finally, neural coding of tonal working memory was encoded in the bilateral STG in the encoding phase and shored in the bilateral PCG and SMA in the maintenance phase.

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.

Background: At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs. Methods: Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets. Main results: With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 ± 14.56% with LDA classifier, 89.73 ± 5.71% with QDA classifier, and 87.04 ± 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.