False discovery rate and permutation test: An evaluation in ERP data analysis

ObjectiveCluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. MethodsWe designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window. ResultsAfter applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data. ConclusionCompared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased. SignificanceOur study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing.

Estimating financial market volatility is integral to the study of investment decisions and behaviour. Previous literature has, therefore, attempted to identify an optimal volatility forecasting model. However, optimal volatility forecasting is dynamic. It depends on the asset being studied and financial market conditions. We propose a novel empirical methodology to account for this dynamism. Using our Multiple Hypothesis Testing with the False Discovery Rate (FDR) method, we identify buckets of superior-performing models relative to the literature’s benchmark models. We present evidence that our proposed FDR bucket with GJR-GARCH has the lowest forecast error in predicting one-step-ahead realized volatility. We also compare our FDR method with two Family-Wise Error Rate model selection frameworks, and the evidence supports our proposed FDR methodology.

Testing topographic differences between event related brain potentials by using non-parametric combinations of permutation tests

Multi-testing correction methods are usually applied in more than one single nucleotide polymorphisms (SNPs) of association analyses to determine the significance of results in the face of testing multiple hypotheses. One of the statistical methods applied in these analyses is called false discovery rate (FDR) procedure. In this study, we compared the FDR theories behind the statistical software of Statistical Analysis Software (SAS) and Golden Helix using a database obtained from a methadone maintenance treatment cohort in Taiwan. The association analyses were performed between the delta opioid receptor (OPRD1) of seven SNPs from rs2236861 to rs760588, or four SNPs from rs2236861 to rs419335, and the diastolic blood pressure (DBP) in both genotypes and allele types. In general linear model (GLM) of association analyses, SNP rs797397 had the most significant association with DBP among seven or four SNPs of both genotype (p=0.0012) and allele type (p=0.0004) using SAS and Golden Helix programs. The genotype and allele type of this SNP had 0.0039 and 0.0018 FDR, respectively after Benjamini and Hochberg (BH) procedure analyses using SAS program, and had 0.0083 and 0.0027 FDR, respectively after the positive false discovery rate (pFDR) analyses using Golden Helix program in seven SNPs. The genotype and allele type of this SNP had 0.0022 and 0.0015 FDR, respectively after BH analyses and 0.0047 and 0.0015 FDR, respectively after pFDR analyses in four SNPs. In summary, the pFDR had less significant level than BH procedure in the most significant GLM associations SNP in both genotypes and allele types at seven and four numbers of SNPs. The FDR values were the same in both BH and pFDR multiple corrections in the allele type of four SNPs. The BH procedure in SAS provided a more consistent FDR in both genotype and allele type of multiple correction analyses despite the number of SNPs.

We present MINEMO (Minimal Information for Neural ElectroMagnetic Ontologies), a checklist for the description of event-related potentials (ERP) studies. MINEMO extends MINI (Minimal Information for Neuroscience Investigations)to the ERP domain. Checklist terms are explicated in NEMO, a formal ontology that is designed to support ERP data sharing and integration. MINEMO is also linked to an ERP database and web application (the NEMO portal). Users upload their data and enter MINEMO information through the portal. The database then stores these entries in RDF (Resource Description Framework), along with summary metrics, i.e., spatial and temporal metadata. Together these spatial, temporal, and functional metadata provide a complete description of ERP data and the context in which these data were acquired. The RDF files then serve as inputs to ontology-based labeling and meta-analysis. Our ultimate goal is to represent ERPs using a rich semantic structure, so results can be queried at multiple levels, to stimulate novel hypotheses and to promote a high-level, integrative account of ERP results across diverse study methods and paradigms.

Averaging amplitudes over consecutive time samples (i.e., time window) is widely used to calculate the peak amplitude of event-related potentials (ERPs). Cluster analysis of the spatio-temporal ERP data is a promising tool to determine the time window of an ERP of interest. However, determining an appropriate number of clusters to optimally represent ERPs is still challenging. Here, we develop a new method to estimate the optimal number of clusters utilizing consensus clustering. Various polarity dependent clustering methods, namely, k-means, hierarchical clustering, fuzzy c-means, self-organizing map, spectral clustering, and Gaussian mixture model, are used to configure consensus clustering after assessing them individually. When a range of clusters is applied many times, the optimal number of clusters should correspond to the expectation, which is the average of the obtained mean inner-similarities of estimated time windows across all conditions and groups converge in the satisfactory thresholds. In order to assess our method, the proposed method has been applied to simulated data and prospective memory experiment ERP data aimed to qualify N2 and P3, and N300 and prospective positivity components, respectively. The results of determining the optimal number of clusters meet at six cluster maps for both ERP data. In addition, our results revealed that the proposed method could be reliably applied to ERP data to determine the appropriate time window for the ERP of interest when the measurement interval is not accurately defined.

The multiplicity problem has become increasingly important in genetic studies as the capacity for high-throughput genotyping has increased. The control of False Discovery Rate (FDR) (Benjamini and Hochberg. [1995] J. R. Stat. Soc. Ser. B 57:289-300) has been adopted to address the problems of false positive control and low power inherent in high-volume genome-wide linkage and association studies. In many genetic studies, there is often a natural stratification of the m hypotheses to be tested. Given the FDR framework and the presence of such stratification, we investigate the performance of a stratified false discovery control approach (i.e. control or estimate FDR separately for each stratum) and compare it to the aggregated method (i.e. consider all hypotheses in a single stratum). Under the fixed rejection region framework (i.e. reject all hypotheses with unadjusted p-values less than a pre-specified level and then estimate FDR), we demonstrate that the aggregated FDR is a weighted average of the stratum-specific FDRs. Under the fixed FDR framework (i.e. reject as many hypotheses as possible and meanwhile control FDR at a pre-specified level), we specify a condition necessary for the expected total number of true positives under the stratified FDR method to be equal to or greater than that obtained from the aggregated FDR method. Application to a recent Genome-Wide Association (GWA) study by Maraganore et al. ([2005] Am. J. Hum. Genet. 77:685-693) illustrates the potential advantages of control or estimation of FDR by stratum. Our analyses also show that controlling FDR at a low rate, e.g. 5% or 10%, may not be feasible for some GWA studies.

Partial least squares analysis of neuroimaging data: applications and advances

Fontal midline theta and the error-related negativity: neurophysiological mechanisms of action regulation

Neural effects of cognitive control load on auditory selective attention

Multiresolution Analysis of Event-Related Potentials by Wavelet Decomposition

Event Abstract Back to Event Brain pathways underlying Response Inhibition and Response Caution Renate Thienel1*, Elise Mansfield1, Patrick S. Cooper1, Andrew Heathcote1, Birte Forstmann2, Pat Michie1, Gavin Cooper1 and Frini Karayanidis1 1 School of Psychology, University of Newcastle, Australia 2 University of Amsterdam, Netherlands Background: Our previous findings suggest that the striatum is instrumental in mediating response caution in a task switching paradigm (Mansfield et al., J. Neurosci 2011, 31:14688-92), and that structural integrity in two separate fronto-striatal networks are associated with adopting an overall more risky or more cautious response threshold. In the present study, we examine whether a similar network underlies response inhibition, as operationalized by a stop signal task. Response inhibition has been shown to be mediated by cortical and subcortical structures within the inferior frontal lobe and the subthalamic nucleus, respectively. We investigate whether structural integrity measures in these regions can explain the relationship between stop signal related event related potential (ERP) components and estimates of stop signal reaction time (SSRT). Finally the relationship between, and the differential structural pathways contributing to, response threshold adjustment (task-switching paradigm) and stop signal response time will be examined. Methods: Diffusion weighted Imaging (DWI), ERP and behavioural data has been collected in 16 healthy participants (24.5 years, 5 male). A correlational analysis was performed for the ERPs and the behavioural measures of both tasks (task switching and stop signal task). Tract based spatial statistics (TBSS) of the DWI data was performed to compare the white matter pathways associated with response thresholds adjustment (task-switching task), and response inhibition (stop-signal task) respectively. These regions were then used to explain variation in ERP data. Results/Discussion: The integrity within distinct structural pathways (DWI data) explained the relationship between (1) ERP measures and response threshold adjustment and (2) ERP measures and SSRTs. Distinct fronto-basal ganglia pathways mediated threshold setting and response suppression. Keywords: task-switching, stop signal task, DWI, fronto-striatal circuits, response inhibition Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Executive Processes Citation: Thienel R, Mansfield E, Cooper PS, Heathcote A, Forstmann B, Michie P, Cooper G and Karayanidis F (2013). Brain pathways underlying Response Inhibition and Response Caution. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00145 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Sep 2013; Published Online: 25 Nov 2013. * Correspondence: Dr. Renate Thienel, School of Psychology, University of Newcastle, Newcastle, Australia, renate.thienel@newcastle.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis Google Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis Google Scholar Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis PubMed Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Context.The new generation of broad-band radio continuum surveys will provide large data sets with polarization information. New algorithms need to be developed to extract reliable catalogs of linearly polarized sources that can be used to characterize those sources and produce a dense rotation measure (RM) grid to probe magneto-ionized structures along the line of sight via Faraday rotation.Aims.The aim of the paper is to develop a computationally efficient and rigorously defined source-finding algorithm for linearly polarized sources.Methods.We used a calibrated data set from the LOw Frequency ARray (LOFAR) at 150 MHz centered on the nearby galaxy M 51 to search for polarized background sources. With a new imaging software, we re-imaged the field at a resolution of 18″ × 15″ and cataloged a total of about 3000 continuum sources within 2.5° of the center of M 51. We made small StokesQandUimages centered on each source brighter than 100 mJy in total intensity (201 sources) and used RM synthesis to create corresponding Faraday cubes that were analyzed individually. For each source, the noise distribution function was determined from a subset of the measurements at high Faraday depths where no polarization is expected; the peaks in polarized intensity in the Faraday spectrum were identified and thep-value of each source was calculated. Finally, the false discovery rate method was applied to the list ofp-values to produce a list of polarized sources and quantify the reliability of the detections. We also analyzed sources fainter than 100 mJy but that were reported as polarized in the literature at at least another radio frequency.Results.Of the 201 sources that were searched for polarization, six polarized sources were detected confidently (with a false discovery rate of 5%). This corresponds to a number density of one polarized source per 3.3 square degrees, or 0.3 source per square degree. Increasing the false discovery rate to 50% yields 19 sources. A majority of the sources have a morphology that is indicative of them being double-lobed radio galaxies, and the ones with literature redshift measurements have 0.5 < z < 1.0.Conclusions.We find that this method is effective in identifying polarized sources, and is well suited for LOFAR observations. In the future, we intend to develop it further and apply it to larger data sets such as the LOFAR Two-meter Survey of the whole northern sky, LOTSS, and the ongoing deep LOFAR observations of the GOODS-North field.

Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects’ data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.

With the development of data mining technology, the analysis of event-related potential (ERP) data has evolved from statistical analysis of time-domain features to data-driven techniques based on supervised and unsupervised learning. However, there are still many challenges in understanding the relationship between ERP components and the representation of familiar and unfamiliar faces. To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length filter generator to dynamically determine the optimal filter length for time-series samples, thereby capturing features at different time scales. Comparative experiments are conducted to evaluate the model’s performance against SOTA models. The results demonstrate that our model achieves impressive outcomes, with a balanced accuracy rate of 93.20% and an F1 score of 88.54%, outperforming the methods used for comparison. The ERP data extracted from different time regions in the model can also provide data-driven technical support for research based on the representation of different ERP components.