Parcellation of the human amygdala using recurrence quantification analysis

The methods of recurrence plots (RPs) and recurrence quantification analysis (RQA) have been used to investigate the tribosystem. The morphology of RPs and RQA measures are strongly dependent on the embedding parameters of the recursive matrix and the segment sizes of the time-series. To improve the calculation accuracy of recursive characteristics analysis, the influences of the embedding parameters and segment sizes on the morphology of RPs and RQA measures have been studied in this letter. Three kinds of theoretical chaotic time-series and measured coefficient of friction (COF) signals during the running-in process were chosen as research objects, and the morphology of RPs and RQA measures were obtained using CRP toolbox afterward. The results indicate that no embedding was actually needed if the data sets are to be qualitatively analyzed using RPs and RQA. Additionally, the morphology of RPs and RQA measures are sensitive to the segment sizes for theoretical chaotic time-series, while the RQA measures of COF signal in the steady-state period are rather stable due to its self-similarity. Finally, according to the guidelines of the parameter settings, the dynamical evolution of measured COF signals during the running-in process have been investigated. It is indicated that recursive characteristics of COF signals could reveal the tribological behaviors’ evolution and conduct the running-in status identification. The results in this paper are significant for improving the calculation accuracy and saving computational time when using the method of recursive characteristics analysis on the tribological behaviors.

Unprecedented 4D spatiotemporal infant regional cerebral blood flow framework and region-specific physiology–function coupling across infancy were elucidated, highlighting strong physiology–function coupling specifically at the default-mode network to meet extraneuronal metabolic demand for network emergence.

BackgroundExcessive daytime sleepiness (EDS) is a frequent nonmotor symptoms of Parkinson’s disease (PD), which seriously affects the quality of life of PD patients and exacerbates other nonmotor symptoms. Previous studies have used static analyses of these resting-state functional magnetic resonance imaging (rs-fMRI) data were measured under the assumption that the intrinsic fluctuations during MRI scans are stationary. However, dynamic functional network connectivity (dFNC) analysis captures time-varying connectivity over short time scales and may reveal complex functional tissues in the brain.PurposeTo identify dynamic functional connectivity characteristics in PD-EDS patients in order to explain the underlying neuropathological mechanisms.MethodsBased on rs-fMRI data from 16 PD patients with EDS and 41 PD patients without EDS, we applied the sliding window approach, k-means clustering and independent component analysis to estimate the inherent dynamic connectivity states associated with EDS in PD patients and investigated the differences between groups. Furthermore, to assess the correlations between the altered temporal properties and the Epworth sleepiness scale (ESS) scores.ResultsWe found four distinct functional connectivity states in PD patients. The patients in the PD-EDS group showed increased fractional time and mean dwell time in state IV, which was characterized by strong connectivity in the sensorimotor (SMN) and visual (VIS) networks, and reduced fractional time in state I, which was characterized by strong positive connectivity intranetwork of the default mode network (DMN) and VIS, while negative connectivity internetwork between the DMN and VIS. Moreover, the ESS scores were positively correlated with fraction time in state IV.ConclusionOur results indicated that the strong connectivity within and between the SMN and VIS was characteristic of EDS in PD patients, which may be a potential marker of pathophysiological features related to EDS in PD patients.

The human dorsolateral prefrontal cortex (DLPFC; approximately corresponding to Brodmann areas 9 and 46) has demonstrable roles in diverse executive functions such as working memory, cognitive flexibility, planning, inhibition, and abstract reasoning. However, it remains unclear whether this is the result of one functionally homogeneous region or whether there are functional subdivisions within the DLPFC. Here, we divided the DLPFC into seven areas along rostral-caudal and dorsal-ventral axes anatomically and explored their respective patterns of structural and functional connectivity. In vivo probabilistic tractography (11 females and 13 males) and resting-state functional magnetic resonance imaging (fMRI; 57 females and 21 males) were employed to map out the patterns of connectivity from each DLPFC subregion. Structural connectivity demonstrated graded intraregional connectivity within the DLPFC. The patterns of structural connectivity between the DLPFC subregions and other cortical areas revealed that the dorsal-rostral subregions connections were restricted to other frontal and limbic areas, whereas the ventral-caudal region was widely connected to frontal, parietal, and limbic cortex. Functional connectivity analyses demonstrated that subregions of DLPFC were strongly interconnected to each other. The dorsal subregions were associated with the default mode network (DMN), while middle dorsal-rostral subregions were linked with the multiple demand network (MDN). The ventral-caudal subregion showed increased functional coupling with both DMN and MDN. Our results suggest that the connectivity of the DLPFC may be subdivided along a dorsorostral-ventrocaudal axis with differing (albeit graded) patterns of connectivity reflecting the integrative executive function of the DLPFC.SIGNIFICANCE STATEMENT Research has shown that the dorsolateral prefrontal cortex (DLPFC) plays a role in various executive functions. By dividing the DLPFC into seven areas along rostral-caudal and dorsal-ventral axes anatomically, we explored their patterns of structural and functional connectivity. The patterns of connectivity within DLPFC subregions demonstrated graded intraregional connectivity. There were distinctive patterns of connectivity with other cortical areas in dorsal-rostral and ventral-caudal DLPFC subregions. Divisions across DLPFC subregions seem to align with their structural and functional connectivity. Our results suggest that DLPFC may be subdivided by the diagonal axis of the dorsal-ventral axis and rostral-caudal axis, supporting the framework of a functional organization along the anterior-posterior axis in the lateral PFC.

Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production.

This study investigates the dynamic functional con-nectivity (DFC) in resting-state fMRI (rs-fMRI) data of chess players using a Vector Auto-Regression (VAR) model. The VAR model was constructed using the Group Lasso and Sliding Window technique. The study included 116 brain regions, and their correlation was examined in the context of their dynamic connection. Statistical feature selection techniques were used to determine which dynamic connections of brain areas were crucial in discriminating chess masters from novice players. After identifying key DFCs related to these brain regions, a classification model was built to classify chess experts and normal control individuals.Our classification model achieved an accuracy of 96.33%under a 10-fold cross-validation framework. This performance represents a substantial improvement over previous studies uti-lizing only rs-fMRI data, which reported a maximum accuracy of 85.45%, indicating a 10.88% enhancement in accuracy. Moreover, our model outperformed methods that combined rs-fMRI with T1-weighted MRI data, which achieved an accuracy of 88%, yielding an additional 8.33% improvement. These results demon-strate that our approach, relying solely on rs-fMRI data, offers a notable advancement in the classification of chess expertise.

Neurodevelopmental conditions such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) alter functional connectivity (FC) among the brain regions. ADHD and ASD affect <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 - 6 \%}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 - 2 \%}$</tex> of the population, respectively and hence need immediate attention to develop automated differential diagnosis. This work proposes an automated diagnostic system for ADHD and ASD by investigating FC among brain regions. We used publicly available restingstate functional Magnetic Resonance Imaging (rs-fMRI) data for ADHD, ASD and age-matched healthy (HC) subjects. The rsfMRI data helps to analyse the temporal activity in the brain, and computing FC using time-series data helps to capture the temporal correlation among regions. We used five diverse FC measures and performed three classification tasks: ASD vs. ADHD, ASD vs. HC and ADHD vs. HC. Further, we finetuned the pre-trained VGG19 model to develop a decision model. The best accuracy of 99.09% for ASD vs. ADHD, 78.18% for ADHD vs. HC and 65.46% for ASD vs. HC demonstrates the efficacy of the proposed method. The disrupted regions are also identified using SHapley Additive exPlanations (SHAP), an explainable artificial intelligence method, that are in accordance with the literature. The work demonstrates the capability of deep learning in analysing neuroimaging data for clinical and research applications.

Purpose: To investigate if there exists a difference in breathing patterns between patients treated with SBRT and IMRT using RQA and DC (using K2: correlation entropy and D2: correlation dimension) measures. Methods: 9 patients treated with SBRT and 8 treated with IMRT were scanned with 4D CT and the breathing patterns were acquired. One of the SBRT patients was scanned with and without meditation. Each breathing signal consisted of a scalar time series and recurrence quantification analysis (RQA) was utilized to determine the following measures: Periodicity of the system as percentage of recurrence points (%RR), determinism (DET), maximum diagonal line length (L_max) whose inverse the divergence (DIV) is measure for how fast trajectories diverge from each other, the average diagonal line length (L) that can be interpreted as the mean prediction time of the signal, and the entropy (ENTR) a measure of information complexity. In addition the invariant measures of K2 and D2 were also estimated. A locally nonlinear forecast was applied to predict future breathing signals of N time step ahead for the patient with and without meditation. Results: Our results showed %RR has significant correlation with L_max and has inverse correlation with DIV. DET has significant correlation with Lmax, L, ENTR and DIV. Independent t test suggests there is no difference between the SBRT and IMRT groups in terms of the RQA measures and K2. Patient that had undergone meditation showed improvement in %RR, L_max, DIV, K2 and had an estimated correlation dimension of 1.7. Prediction showed similar results for one and three time step ahead but meditation one had better prediction horizon when time step was higher. Conclusion: RQA is a powerful tool that allows one to analyze the dynamic nature of breathing pattern. No significant difference was found in the dynamical complexity of SBRT and IMRT patients.

Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the “Apnea-ECG Physionet database” and the “HuGCDN2014 database” are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.

Recurrence quantification analysis (RQA) was developed in order to quantify differently appearing recurrence plots (RPs) based on their small-scale structures, which generally indicate the number and duration of recurrences in a dynamical system. Although RQA measures are traditionally employed in analyzing complex systems and identifying transitions, recent work has shown that they can also be used for pairwise dissimilarity comparisons of time series. We explain why RQA is not only a modern method for nonlinear data analysis but also is a very promising technique for various time series mining tasks.

NBS-Predict: A prediction-based extension of the network-based statistic

Somatic symptoms are prevalent in patients with depression. The centromedial amygdala (CMA) is a key brain region that mediates autonomic and somatic responses. Abnormal function in the CMA may contribute to the development of somatic symptoms in depressed patients. We compared the resting-state functional connectivity (RSFC) based on the seed of the left and right CMA between 37 patients with depression and 30 healthy controls. The severity of depressive and somatic symptoms was assessed using the Hamilton Depression Rating Scale (HDRS) and the 15-item somatic symptom severity scale of the Patient Health Questionnaire (PHQ-15). Correlation analysis was performed to investigate the relationship between the RSFC and clinical variables (HDRS and PHQ-15) in depressed patients. Compared with healthy controls, patients with depression exhibited decreased RSFC between the CMA and insula, and superior temporal gyrus. In addition, functional connectivity between the left CMA and left insula was negatively correlated with PHQ-15 (r = -0.348, p = .037) in depressed patients. No significant relation was found between the RSFC and HDRS in depressed patients. Functional connectivity between the CMA and insula is reduced in depressive patients, which is associated with the severity of somatic symptoms. Our findings may provide a potential neural substrate to interpret the co-occurrence of depression with somatic symptoms.

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data from the multicenter Enhancing Neuro-Imaging and Genetics through Meta-Analysis (ENIGMA)-OCD consortium. Baseline clinical and resting-state functional magnetic imaging (rs-fMRI) data from 159 adult patients aged 18-60years (88 female) with OCD who received CBT at four treatment/neuroimaging sites were included. Fractional amplitude of low frequency fluctuations, regional homogeneity and atlas-based functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p=0.001). Lower baseline symptom severity, younger age, an absence of cleaning obsessions, unmedicated status, and higher education had the highest model impact in predicting remission. The best predictive performance using only rs-fMRI was obtained with regional homogeneity for remission (AUC=0.59). Predicting response with rs-fMRI generally did not exceed chance level. Machine learning models based on clinical data may thus hold promise in predicting remission after CBT for OCD, but the predictive power of multicenter rs-fMRI data is limited.

This paper presents efficient low-power adaptive filter architecture for electroencephalogram (EEG) signal and epileptic seizure detection using recurrence quantification analysis (RQA). The preprocessing of EEG is done using notch, wavelet and adaptive filter. The comparison of signal-to-noise ratio of the filter outputs proves that the adaptive filter provides better performance and a parallel interleaved sample of direct form adaptive FIR filter architecture is implemented and low-power issues are addressed. An innovative compressor-based addition technique is utilized in the filter implementation to reduce the area and power consumption. The design is developed using Verilog HDL and mapped to 65-nm technological node. The power results are compared with conventional architecture of adaptive filter. The major advantage of choosing RQA is that it provides better information even for short non-stationary and nonlinear signals where other methods fail to provide good results. And it requires no conventions about data set size or dispersal of the data. The algorithm is applied on epileptic EEG signal from CHB-MIT database. The RQA measures are determined from the recurrence plot, its performance is measured in terms of sensitivity and specificity as 97.4 and 93.5 %, respectively, and leakage power is reduced to 10 %.

In the last two decades, the avian hippocampus has been repeatedly studied with respect to its architecture, neurochemistry, and connectivity pattern. We review these insights and conclude that we unfortunately still lack proper knowledge on the interaction between the different hippocampal subregions. To fill this gap, we need information on the functional connectivity pattern of the hippocampal network. These data could complement our structural connectivity knowledge. To this end, we conducted a resting-state fMRI experiment in awake pigeons in a 7-T MR scanner. A voxel-wise regression analysis of blood oxygenation level-dependent (BOLD) fluctuations was performed in 6 distinct areas, dorsomedial (DM), dorsolateral (DL), triangular shaped (Tr), dorsolateral corticoid (CDL), temporo-parieto-occipital (TPO), and lateral septum regions (SL), to establish a functional connectivity map of the avian hippocampal network. Our study reveals that the system of connectivities between CDL, DL, DM, and Tr is the functional backbone of the pigeon hippocampal system. Within this network, DM is the central hub and is strongly associated with DL and CDL BOLD signal fluctuations. DM is also the only hippocampal region to which large Tr areas are functionally connected. In contrast to published tracing data, TPO and SL are only weakly integrated in this network. In summary, our findings uncovered a structurally otherwise invisible architecture of the avian hippocampal formation by revealing the dynamic blueprints of this network.