Nonlinear Correlation Analysis Research Articles

Impact of the Volume of Developer Housing Units on Real Estate Prices in Poland: Correlation and Coherence Analysis

This article conducts a comprehensive correlation analysis to explore the relationship between the development volume by developers and average market prices in Poland's housing sector from 2010 to 2023, utilizing quarterly data on the number of apartments released and their average prices. It employs both linear and nonlinear correlation analysis alongside wavelet coherence analysis. Preliminary correlational analysis offered insights into the basic interdependency patterns, highlighting how developer-supplied apartment numbers impact average prices. Wavelet coherence analysis, a more sophisticated approach, decomposed the data across various frequencies to uncover complex, nonlinear relationship patterns potentially missed by conventional correlation methods. The findings of the study highlight significant connections between the variables that vary over time and space, emphasizing the complexity of the housing market and underscoring the necessity for thorough analysis. These results carry important implications for developers, investors, and housing policy formulation, contributing significantly to understanding Poland's residential real estate market dynamics and supporting further research and strategic real estate industry planning.

The Relationship Between Serum Cholesterol, Triglycerides, and Self-Reported Appetitive and Reactive Aggression, as Well as Violent Crimes in Male Forensic Patients with Substance Use Disorder

Numerous studies have linked lower levels of serum cholesterol with heightened aggression, violent crimes, and violent deaths across diverse populations, including forensic psychiatric patients, criminals, and the broad public. Interestingly, this association appears to be more pronounced in men than in women. Conversely, findings regarding serum triglycerides present a more nuanced picture, with some studies indicating heightened aggression with elevated levels and others suggesting the opposite. However, most studies have neglected to examine this phenomenon in conjunction with psychological traits. Additionally, no prior research has explored the association between serum lipid levels and different types of aggression, e.g., reactive, instrumental, or appetitive. Considering these gaps, our study aimed to assess serum cholesterol and triglyceride levels upon clinic admission and correlate them with scores from the Appetitive and Facilitative Aggression Scale (AFAS), a self-rating questionnaire assessing reactive and appetitive aggression. We conducted our investigation on a cohort of 135 forensic psychiatric patients undergoing drug addiction treatment under Section 64 of the German Criminal Code. This provision offers a nearly unique opportunity worldwide for individuals who have committed crimes under the influence of drugs or in the context of their addiction to receive specialized treatment. Using non-linear Spearman correlation analyses, we observed an inverse relationship between serum concentrations of both cholesterol and triglycerides and AFAS appetitive violence scores but not for reactive aggression. Additionally, triglyceride levels exhibited an inverse association with the prevalence of violent crimes as an index crime. In conclusion, blood lipids may affect numerous areas of health and disease beyond what is currently known, offering a potential shift in how we understand their role in aggression.

A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments

According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.

A Methodology for Modeling a Multi-Dimensional Joint Distribution of Parameters Based on Small-Sample Data, and Its Application in High Rockfill Dams

The composition of high rockfill dam materials is complex, and the mechanical parameters are uncertain and correlated in unknown ways due to the influences of the environment and construction, leading to complex deformation mechanisms in the dam–foundation system. Statistical characteristics of material parameters are the basis for deformation and stress analysis of high core rockfill dams, and using an inaccurate distribution model may result in erroneous analysis results. Furthermore, empirically evaluated distribution types of parameters are susceptible to the influence of small sample sizes, which are common in the statistics of geotechnical engineering. Therefore, proposing a multi-dimensional joint distribution model for parameters based on small-sample data is of great importance. This study determined the interval estimation values of Duncan–Chang E-B model parameters—such as the mean value and coefficient of variation for the core wall, rockfill, and overburden materials—using parameter statistical analysis, bootstrap sampling methods, and Akaike information criterion (AIC) optimization. Additionally, the marginal distribution types of each parameter were identified. Subsequently, a multi-dimensional joint distribution model for Duncan–Chang model parameters was constructed based on the multi-dimensional nonlinear correlation analysis of parameters and the Copula function theory. The application results for the PB dam demonstrate that joint sampling can effectively reflect the inherent correlation laws of material parameters, and that the results for stress and deformation are reasonable, leading to a sound evaluation of the cracking risk in the core wall of high core rockfill dams.

Association between park vitality and commercial vitality: a case study in Chengdu

ABSTRACT Urban vitality stands as a pivotal metric for evaluating the sustainability and appeal of urban environments. Global experiences underscore the significance of parks and shopping centers as crucial components of urban vitality spaces. However, existing literature predominantly concentrates on the individual impacts of parks or shopping centers on urban vitality, with limited exploration into the vitality relationship between them, often relying on linear correlation analyses. To address this gap, this study, centered on the renowned Park City of Chengdu in China, employs Baidu heatmap and POI data, utilizing a combination of spatial syntax and linear and non-linear analysis methods to examine the influence of road accessibility on the vitality of shopping malls and parks, as well as their correlation. Findings indicate: (1) High-vitality shopping malls primarily cluster within the first ring road, while park utilization is notably higher in the southern urban area. (2) High-vitality parks and shopping centers are situated in regions characterized by elevated global integration and efficiency values, suggesting their vitality is influenced by road accessibility. (3) Linear correlation analysis, with global integration and efficiency values controlled, yields correlation coefficients of 0.797 and 0.795 for high-vitality parks and shopping centers, respectively. Non-linear correlation analysis indicates a correlation coefficient close to 0.7 between park vitality and shopping mall vitality, highlighting a strong correlation between them. This study elucidates the critical relationship between park vitality and commercial vitality in urban layout, offering valuable insights for urban planning strategies aimed at promoting sustainable urban development.

From Complexity to Clarity: M2KCSVA's Nonlinear Temporal Correlation Analysis and Stationary Estimation Pave the Way for Fault Diagnosis in Ironmaking Processes

From Complexity to Clarity: M2KCSVA's Nonlinear Temporal Correlation Analysis and Stationary Estimation Pave the Way for Fault Diagnosis in Ironmaking Processes

Особенности ментальной регуляции познавательных состояний школьников в зависимости от уровня стрессогенности ситуации

Background. The relevance of the study is due to insufficient knowledge of the influence of the structures of consciousness on the self-regulation of cognitive states in various stressful situations. The author's concept of mental regulation of psychological states served as a theoretical basis for empirical research. Objective. To identify the features of the relationship between indicators of mental regulation and the frequency of experiencing cognitive states in four situations with different stress levels. Study Participants. The study sample consisted of 70 schoolchildren (31 boys and 29 girls, Mage = 17.7 years) from secondary education schools. Methods. The study implemented the author’s questionnaires “Assessment of the frequency of experiencing mental states” and “Methods of self-regulation of mental states” (M.E. Valiullina). In the instructions, the respondents were presented with four types of situations characterized by different stress levels — from the weak level with a neutral context to the strong level with a negative emotional background. Apart from that, the study participants were tested by means of well-known psychological questionnaires: “Method for Studying the System of Life Meanings” (V.Yu. Kotlyakov), “Self-attitude Questionnaire” (S.R. Pantileev), “Method for Diagnosing Reflexivity” (A.V. Karpov, V.V. Ponomareva). Statistical data processing was carried out using frequency analysis and nonlinear correlation analysis according to Spearman. Results. The data analysis demonstrated: as the tension in the situation increases, the frequency of experiencing productive cognitive states decreases, the probability of unproductive states increases, and the number of applied regulation methods increases as well. Besides, as the tension in the situation increases, the involvement of the consciousness components (those of semantic sphere, reflection, and self-attitude) in the mental regulation of cognitive states increases, the degree of relationships integration increases, and the structure-forming indicators are highlighted. Conclusions. The results obtained show that the level of stress in the situation significantly influences the qualitative composition of the characteristics involved in the systems “mental regulation — cognitive state — regulation method” and “mental regulation — regulation method — cognitive state”

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Sustainability-related disclosure rules and financial market indicators: Searching for interconnections in developed and developing countries

In today’s fast-paced business environment, integrating sustainability into financial decision-making has been a key driver of change. As stakeholders increasingly demand greater corporate transparency and accountability, regulatory bodies have stepped in to ensure that sustainability reporting is standardized and robust. This paper aims to establish the relationship between the sustainability-related disclosure rules and the dynamic indicators of the financial market. The object of the study is 74 countries of the world, which are grouped into developed and developing countries. The time period is 2021, for the stock market capitalization indicators – 2020, as the most recent years with available data. The research methods are normality tests (Shapiro-Wilk and Shapiro-Francia test), comparison methods (Student’s t-test and Mann-Whitney U test, regression analysis with dummy variables), linear and non-linear correlation and regression analysis (logarithmic, polynomial). The results obtained confirmed that the sustainability-related disclosure rules are higher in developed countries than in developing ones. At the same time, in developed countries, the growth of such requirements affects the increase in stock price volatility, stock market capitalization, foreign direct and portfolio investments. For developing countries, there is also an increase in the stock market capitalization, portfolio investments and the volume of stock trading. Recognizing these trends can benefit both financial market regulators and participants to encourage the formation of a transparent and efficient financial market, thereby mitigating the problems associated with information asymmetry.

Influence of body mass index and waist-hip ratio on male semen parameters in infertile men in the real world: a retrospective study.

It is suggested that body mass index (BMI) can affect male semen quality; however, the results remain controversial. In addition, most studies have focused on the effect of obesity on semen quality. Evidence on the relationship of underweight or waist-hip ratio (WHR) with semen quality is rare. This study aimed to assess the association of BMI and WHR with semen quality. Data, including BMI and WHR, was collected from 715.00 men who underwent a fertility evaluation. BMI (kg/m2) was categorized as <18.50 (underweight), 18.50-24.90 (normal), 25.00-27.90 (overweight), and ≥28.00 (obese) kg/m2 for analysis. WHR was categorized as <0.81 (normal) and ≥0.81 (high). Semen volume, sperm concentration, progressive motility, and total motile sperm count were detected by experienced clinical technicians. Spearman's correlation showed that BMI was weakly associated with sperm progressive motility (r = 0.076, P < 0.05), while WHR showed no relationship with semen parameters. The azoospermia rate was significantly higher (33.33% vs. 2.10%, P < 0.001) and the sperm concentration was lower (P < 0.05) in the underweight group. The nonlinear correlation analysis showed that BMI was negatively associated with sperm concentration while BMI was more than 22.40 kg/m2 (P < 0.05), while WHR was negatively related to sperm progressive motility within 0.82 to 0.89 (P < 0.05). Furthermore, the multivariate logistic analysis showed that follicular stimulating hormone (FSH) was an independent risk factor for normal sperm concentration (odds ratio [OR]: 0.791, P = 0.001) and morphology (OR: 0.821, P = 0.002), BMI was an independent risk factor for normal sperm progressive motility, and testosterone was an independent risk factor for sperm morphology (OR: 0.908, P = 0.023). BMI and WHR were significantly associated with semen parameters, while BMI was an independent risk factor for normal sperm progressive motility. Reproductive hormones, including FSH and testosterone, had a significant influence on sperm concentration and sperm morphology.

Dynamic evolution of the anterior cingulate-insula network during seizures.

In physiological situations, the anterior cingulate cortex (ACC) and anterior insular cortex (AIC) are prone to coactivation. The functional connectivity and interaction between ACC and AIC in the context of epilepsy remain unclear. This study aimed to investigate the dynamic coupling between these two brain regions during seizures. Patients who underwent stereoelectroencephalography (SEEG) recording were included in this study. The SEEG data were visually inspected and quantitatively analyzed. The narrowband oscillations and aperiodic components at seizure onset were parameterized. The frequency-specific non-linear correlation analysis was applied to the functional connectivity. The excitation/inhibition ratio (E:I ratio) reflected by the aperiodic slope was performed to evaluate the excitability. Twenty patients were included in the study, with 10 diagnosed with anterior cingulate epilepsy and 10 with anterior insular epilepsy. In both types of epilepsy, the correlation coefficient (h2 ) between the ACC and AIC at seizure onset exhibited a significantly higher value than that during interictal and preictal periods (p < 0.05). The direction index (D) showed a significant increase at seizure onset, serving as an indicator for the direction of information flow between these two brain regions with up to 90% accuracy. The E:I ratio increased significantly at seizure onset, with the seizure-onset zone (SOZ) demonstrating a more pronounced increase compared to non-SOZ (p < 0.05). For seizures originating from AIC, the E:I ratio was significantly higher in the AIC than in the ACC (p = 0.0364). In the context of epilepsy, the ACC and AIC are dynamically coupled during seizures. The functional connectivity and excitability exhibit a significant increase at seizure onset. By analyzing connectivity and excitability, the SOZ in ACC and AIC can be identified. The direction index (D) serves as an indicator for the direction of information flow from SOZ to non-SOZ. Notably, the excitability of SOZ changes more significantly than that of non-SOZ.

Non-linear correlation analysis in financial markets using hierarchical clustering

Distance correlation coefficient (DCC) can be used to identify new associations and correlations between multiple variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets of variables that provide equivalent information, is zero only when variables are independent, and is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient (PCC). Hence, DCC provides more information than the PCC. We analyze numerous pairs of stocks in S&P500 database with the distance correlation coefficient and provide an overview of stochastic evolution of financial market states based on these correlation measures obtained using agglomerative clustering.

Prediction method of cigarette draw resistance based on correlation analysis

The cigarette draw resistance monitoring method is incomplete and single, and the lacks correlation analysis and preventive modeling, resulting in substandard cigarettes in the market. To address this problem without increasing the hardware cost, in this paper, multi-indicator correlation analysis is used to predict cigarette draw resistance. First, the monitoring process of draw resistance is analyzed based on the existing quality control framework, and optimization ideas are proposed. In addition, for the three production units, the cut tobacco supply (VE), the tobacco rolling (SE), and the cigarette-forming (MAX), direct and potential factors associated with draw resistance are explored, based on the linear and non-linear correlation analysis. Then, the correlates of draw resistance are used as inputs for the machine learning model, and the predicted values of draw resistance are used as outputs. Finally, this research also innovatively verifies the practical application value of draw resistance prediction: the distribution characteristics of substandard cigarettes are analyzed based on the prediction results, the time interval of substandard cigarettes being produced is determined, the probability model of substandard cigarettes being sampled is derived, and the reliability of the prediction result is further verified by the example. The results show that the prediction model based on correlation analysis has good performance in three months of actual production.

Deciphering the Impact of Helium Tagging on Flexible Molecules: Probing Microsolvation Effects of Protonated Acetylene by Quantum Configurational Entropy

Helium, the lightest and most weakly interacting noble gas, is well-known for its unsurpassed chemical inertness. In many applications of helium in experimental techniques, such as tagging, messenger, or nanodroplet isolation action spectroscopy of molecules or complexes, it is assumed that the interaction of helium with the respective species, and thus the resulting interaction-induced perturbation, is small enough not to affect their structure and dynamics. Here, we probe the impact of one up to many attached helium atoms on protonated acetylene─an important nonclassical carbocation subject to three-center two-electron bonding in its ground state structure─using highly accurate interaction potentials in conjunction with entropy-based higher-order nonlinear correlation analysis. In particular, using neural network potentials at CCSD(T) accuracy, we disclose the specific structural perturbations due to the tagging of C2H3+ with up to 20 He atoms at a temperature of 1 K. Analysis reveals that microsolvation by helium influences the structure of C2H3+ noticeably, while our investigation of the quantum configurational information entropy additionally shows that correlations between individual orientational degrees of freedom are affected as a function of cluster size. In particular, it is found that the most probable bridge-like structure of the ro-vibrational quantum ground state of C2H3+, which is nonplanar and trans-bent in contrast to the perfectly planar equilibrium structure, becomes increasingly more localized upon adding helium atoms. The remarkably nonlinear behavior of the angular correlations as a function of cluster size is traced back to the buildup of the quantum microsolvation shell that enhances anisotropy up to NHe = 6 while more and more isotropic solvation takes over beyond six. Our approach is general and thus sets the stage to investigate the salient effects on the structure of flexible molecules due to tagging beyond the specific case.

Physics‐Based Human‐in‐the‐Loop Machine Learning Combined with Genetic Algorithm Search for Multicriteria Optimization: Electrochemical CO2 Reduction Reaction

Machine learning (ML) can be a powerful tool to expedite materials research, but the deployment for experimental research is often hindered by data scarcity and model uncertainty. An human‐in‐the‐loop procedure to tailor the implementation of ML for multicriteria optimization is described. The effectiveness of this procedure in the development of a nafion‐based membrane electrode assembly for electrochemical CO2 reduction reaction (CO2RR) into CO for two targets is demonstrated: energy efficiency (EE) and partial current density for CO2RR (). Model‐agnostic nonlinear correlation analyses identify the 11 features relevant to those targets. The three studied decision tree‐based ML models yield similar cross‐validation errors so an ad hoc feature analysis of the models is done with SHapley Additive exPlanations and nonlinear correlation techniques. The predicted EE‐ space and the functional dependency of the predictions are investigated to assess model plausibility. A genetic algorithm with CO production cost as the final target with subsequent validation experiments of candidate conditions is devised. The model chosen through ad hoc analysis yields the highest accuracy and the only one that can locate the Pareto front with a single round of experiments, demonstrating how appropriate model selection through careful inspection can greatly accelerate the research cycle.

DeepTRNet: Time-resolved reconstruction of flow around a circular cylinder via spatiotemporal deep neural networks

We propose spatiotemporal deep neural networks for the time-resolved reconstruction of the velocity field around a circular cylinder (DeepTRNet) based only on two flow data types: the non-time-resolved wake velocity field and sparse time-resolved velocity measurements at specific discrete points. The DeepTRNet consists of two operations, i.e., compact spatial representations extraction and sequential learning. We use the convolutional autoencoder (CAE) in DeepTRNet to extract compact spatial representations embedded in the non-time-resolved velocity field. The nonlinear CAE modes and corresponding CAE coefficients are thus obtained. Based on the nonlinear correlation analysis of the velocity field, we use the bidirectional recurrent neural networks (RNN) with the gated recurrent unit for mapping the sparse time-resolved velocity measurements to the CAE coefficients via sequential learning. The early stopping technique is used to train the DeepTRNet to avoid overfitting. With the well-trained DeepTRNet, we can reconstruct the time-resolved velocity field around the circular cylinder. The DeepTRNet is verified on the simulated datasets at two representative Reynolds numbers, 200 and 500, and the experimental dataset at Reynolds number 3.3 × 104 with the steady jet at the rear stagnation point of the cylinder. We systematically compare the DeepTRNet method and the RNN-proper orthogonal decomposition (POD) approach. The DeepTRNet can obtain the accurate time-resolved velocity field depending on the two data types mentioned above. The DeepTRNet method outperforms the RNN-POD method in the reconstruction accuracy, especially for the reconstruction of small-scale flow structures. In addition, we get the reliable velocity field even for the high-frequency components.

Localizing epileptogenic network from SEEG using non-linear correlation, mutual information and graph theory analysis.

The key challenge in epilepsy surgery is precise localization and removal of the epileptogenic zone (EZ) from the brain. Localization of the epileptogenic network by visual analysis of intracranial EEG is extremely difficult. In this retrospective study, we used interictal connectivity and graph theory analysis on intracranial EEG to better delineate the epileptogenic zone. Patients who underwent surgery for drug-refractory mesial temporal and neocortical epilepsy were included. Computational measures, such as h2 nonlinear correlation and mutual information, were used to estimate the interdependency of intracranial EEGs. We observed that the Out-Degree, Out-Strength, and Betweenness centrality (graph properties) were the best predictors of EZ. From the results, we also found that graph properties with a normalized value above 0.75 were found to be a useful measure to localize the EZ with a sensitivity of 87.88 and a specificity of 87.13. Our results also validate that frequently occurring types of interictal fast discharges (IFD) with connectivity measures and graph properties can better localize the EZ. We foresee graph theory analysis of interictal intracranial EEG data can help precise localization of EZ for cortical resection as well as in minimally invasive radiofrequency ablation of epileptogenic hubs. Further, prospective validation is required for clinical use.

Output-related fault detection in non-stationary processes using constructive correlative-SAE and demoting correlative-DNN

Fault detection in non-stationary processes is a timely research topic in industrial systems. The conventional approaches based on principal component regression (PCR) and partial least-squares (PLS) cannot be loosely used for non-stationary and non-linear processes when the statistical behaviour of the measurements does not follow a Gaussian distribution with constant mean and standard deviation values. This paper introduces a new application of deep learning (DL), specifically a combination of proposed correlative stacked auto-encoder (C-SAE) and correlative deep neural networks (C-DNN) for output-related anomaly detection without complete decomposition of process variables with respect to quality output(s). With this aim, two new constructive and demoting loss functions are proposed to relatively decompose the process measurements with respect to their relevance to quality output variable(s). The loss functions are modified with the incorporation of non-linear correlation analysis and hence integrated into SAE and DNN structures to suggest a correlative SAE and DNN. The proposed C-SAE and C-DNN are integrated into a scheme with an inverted pyramid structure that enables output-related fault detection without limiting stationarity assumptions. Moreover, the proposed framework can be freely applied to both linear and non-linear processes. The performance of the proposed DL strategy is tested and validated on a non-stationary numerical example and Tennessee Eastman Process. The comparison results with recent approaches indicate the outperformance of the proposed approach for process output-related fault detection purposes.

Cyclic Nonlinear Correlation Analysis for Time Series

Principal component analysis (PCA) and kernel PCA allow the decorrelation of data with respect to a basis that is found via variance maximization. However, these techniques are based on pointwise correlations. Especially in the context of time series analysis this is not optimal. We present a novel generalization of PCA that allows to imprint any desired correlation pattern. Thus the proposed method can be used to incorporate previously known statistical dependencies between input variables into the model which is increasing the overall performance. This is achieved by generalizing the projection onto the direction of maximum variance—as known from PCA—to a projection onto a multi-dimensional subspace. We focus on the use of cyclic correlation patterns, which is especially of interest in the domain of time series analysis. Beneath introducing the presented variation of PCA, we discuss the role of this method with respect to other well-known time series analysis techniques.

Non-linear Functional Brain Co-activations in Short-Term Memory Distortion Tasks.

Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 ± 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.