Signal Variability Research Articles

Exploring Ethnic Variability in Aryl Hydrocarbon Receptor Signaling: Delineating Differences in Prostate Cancer Outcomes Between African American and Caucasian Populations

Exploring Ethnic Variability in Aryl Hydrocarbon Receptor Signaling: Delineating Differences in Prostate Cancer Outcomes Between African American and Caucasian Populations

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings. In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar functional connectivity characteristics through clustering, thereby enhancing the classification process. Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.&#xD.

Modeling saccade reaction time in marmosets: the contribution of earlier visual response and variable inhibition

Marmosets are expected to serve as a valuable model for studying the primate visuomotor system due to their similar oculomotor behaviors to humans and macaques. Despite these similarities, differences exist; challenges in training marmosets on tasks requiring suppression of unwanted saccades, having consistently shorter, yet more variable saccade reaction times (SRT) compared to humans and macaques. This study investigates whether the short and variable SRT in marmosets is related to differences in visual signal transduction and variability in inhibitory control. We refined a computational SRT model, adjusting parameters to better capture the marmoset SRT distribution in a gap saccade task. Our findings indicate that visual information processing is faster in marmosets, and that saccadic inhibition is more variable compared to other species.

Hydrological controls of a riparian wetland based on stable isotope data and model simulations.

Isotopic evidence of groundwater and stream water is frequently used to investigate water exchanges with groundwater. Monthly sampling of rain, stream water, and groundwater was conducted at Tims Branch watershed in South Carolina for the oxygen and hydrogen stable isotope (δ2H and δ18O) measurement, as well as pH and oxidation-reduction potential (ORP). Together with a mass balance perspective, it was determined that it takes a few weeks to one month for groundwater in the hyporheic zone to fully exchange with stream water. From hydrodynamic modelling, we show that substantial (up to 70 %) groundwater exchange occurs at gaining and losing sites. Groundwater exfiltration, i.e. inflow into stream water, contributes up to 4 % to stream water, with the remainder from upstream exfiltration. A 2-4 % per day renewal rate of adjacent groundwater would indirectly indicate a groundwater residence time in the order of half a month to a full month (assuming either a well-mixed case or large dispersion rate in pulse flow case), in agreement with a greatly reduced variability of δ2H and δ18O of groundwater compared to stream water and rain. This reduced variability of stable isotope signal from groundwater confirms our hypothesis that riparian groundwater mixing at Tims Branch is more of a mixed type rather than a pulse flow type. A monthly time scale is sufficient for groundwater to become anoxic at exit points into stream water resulting in the episodic production of natural organic matter- and iron-rich flocs upon oxidation.

Testing the fidelity of zircon as a provenance indicator in fluvial‐fan successions: An example from the Palaeogene Colton Formation, Central Utah, USA

AbstractThe stratigraphic, spatial and temporal variability of detrital zircon age populations in continental sedimentary successions is a critical tool in understanding palaeodrainage networks and how these systems distributed detritus within sedimentary basins. However, multiple factors, such as variations in sediment‐transport processes, the scale of the depositional environment and the architecture of the sedimentary succession are often overlooked in detrital zircon studies. This article presents detrital zircon U–Pb geochronology from the fluvial‐dominated Colton Formation in the western Uinta Basin (Utah, USA) to assess the system's provenance and evolution. Significant differences in zircon age populations between the Colton Formation and the overlying Green River Formation suggest a reorganisation of the source‐to‐sink system during the transition between the two lithostratigraphic units. Notably, detrital zircon age spectra are not homogeneous across the Colton Formation, therefore physical morphometric parameters were used to verify the possible influence of selective bias during sediment transport. These data reveal that a relatively finer‐grained population of Precambrian, and to a less extent Mesozoic, zircon grains were affected by hydraulic sorting during transport, resulting in a greater relative abundance of older zircon grains in the distal reaches of the distributive fluvial system, whose basinward decrease in competence would have increased the relative proportion of finer zircon fractions in sandstones. Furthermore, there are different trends in the distributions of zircon age populations relative to their stratigraphic position, highlighting the complex architecture of the fluvial palaeo‐fan. The spatial and stratigraphic variability of provenance signals in fluvial‐fan successions must be carefully evaluated to improve the reliability of source‐to‐sink models and palaeodrainage reconstructions, as autogenically controlled noise can be generated during the dispersal of detrital zircon in fluvial sedimentary systems.

Pilot study for non-invasive diabetes detection through classification of photoplethysmography signals using convolutional neural networks

Diabetes is a chronic disorder affecting vascular health, often altering pulse wave characteristics. Traditional pulse wave analysis (PWA) methods face challenges such as variability and complexity of signals. This study aims to overcome these limitations by leveraging deep learning models for more accurate and efficient classification. The methodology used in this study involves four key steps: data collection, data preprocessing, Convolutional Neural Network (CNN) model development, and model evaluation. Primary data were collected using a multipara patient monitor, including finger photoplethysmography (PPG) signals, blood pressure, mean arterial pressure, oxygen saturation, and pulse rate. Single pulse wave cycles from 60 healthy individuals and 60 patients with type 2 diabetes underwent preprocessing. The CNN model was trained using 50 PPG images from each group and achieved a training accuracy of 92%. The prediction capability of the model was evaluated using 20 unseen images, comprising 10 healthy and 10 diabetes PPG images. It attained a 90% overall test accuracy in distinguishing between PPG images of individuals with diabetes and those who are healthy. These findings suggest that CNN-based analysis of PPG signals provides a precise, non-invasive tool for diabetes screening. To further enhance accuracy, future studies should focus on increasing the dataset size and performing hyperparameter tuning to optimize the CNN model.

Cryogenic quantum computer control signal generation using high-electron-mobility transistors

Multiplexed local charge storage, close to quantum processors at cryogenic temperatures could generate a multitude of control signals, for electronics or qubits, in an efficient manner. Such cryogenic electronics require generating quasi-static control signals with small area footprint, low noise, high stability, low power dissipation and, ideally, in a multiplexed fashion to reduce the number of input/outputs. In this work, we integrate capacitors with cryogenic high-electron mobility transistor (HEMT) arrays and demonstrate quasi-static bias generation using gate pulses controlled in time and frequency domains. Multi-channel bias generation is also demonstrated. Operation at 4 K exhibits improved bias signal variability and greatly reduced subthreshold swing, reaching values of ~6 mV/decade. Due to the very low threshold voltage of 80 mV at 4 K and the steep subthreshold swing, these circuits can provide an advantage over the silicon-based complementary metal-oxide-semiconductor equivalents by allowing operation at significantly reduced drive bias in the low output voltage regime <1 V. Together with their high-speed operation, this makes HEMTs an attractive platform for future cryogenic signal generation electronics in quantum computers.

Reassessing the Functional Significance of Blood Oxygen Level Dependent Signal Variability.

BOLD signal variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and is a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to "absolute" meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.

A Feasibility Study of [18F]F-AraG Positron Emission Tomography (PET) for Cardiac Imaging-Myocardial Viability in Ischemia-Reperfusion Injury Model.

Myocardial infarction (MI) with subsequent inflammation is one of the most common heart conditions leading to progressive tissue damage. A reliable imaging marker to assess tissue viability after MI would help determine the risks and benefits of any intervention. In this study, we investigate whether a new mitochondria-targeted imaging agent, 18F-labeled 2'-deoxy-2'-18F-fluoro-9-β-d-arabinofuranosylguanine ([18F]F-AraG), a positron emission tomography (PET) agent developed for imaging activated T cells, is suitable for cardiac imaging and to test the myocardial viability after MI. To test whether the myocardial [18F]-F-AraG signal is coming from cardiomyocytes or immune infiltrates, we compared cardiac signal in wild-type (WT) mice with that of T cell deficient Rag1 knockout (Rag1 KO) mice. We assessed the effect of dietary nucleotides on myocardial [18F]F-AraG uptake in normal heart by comparing [18F]F-AraG signals between mice fed with purified diet and those fed with purified diet supplemented with nucleotides. The myocardial viability was investigated in rodent model by imaging rat with [18F]F-AraG and 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) before and after MI. All PET signals were quantified in terms of the percent injected dose per cc (%ID/cc). We also explored [18F]FDG signal variability and potential T cell infiltration into fibrotic area in the affected myocardium with H&E analysis. The difference in %ID/cc for Rag1 KO and WT mice was not significant (p = ns) indicating that the [18F]F-AraG signal in the myocardium was primarily coming from cardiomyocytes. No difference in myocardial uptake was observed between [18F]F-AraG signals in mice fed with purified diet and with purified diet supplemented with nucleotides (p = ns). The [18F]FDG signals showed wider variability at different time points. Noticeable [18F]F-AraG signals were observed in the affected MI regions. There were T cells in the fibrotic area in the H&E analysis, but they did not constitute the predominant infiltrates. Our preliminary preclinical data show that [18F]F-AraG accumulates in cardiomyocytes indicating that it may be suitable for cardiac imaging and to evaluate the myocardial viability after MI.

Surface Electromyography: A Pilot Study in Canine Spinal Muscles

In veterinary practice, rehabilitation modalities are often used to help in the recovery of animals affected by InterVertebral Disc Disease (IVDD), a condition frequently observed in chondrodystrophic dog breeds and can lead to Spinal Cord Injury (SCI), resulting in pain, motor impairments and neurological deficits, but there is a lack of objective assessment tools for patient evolution. In this work, an innovative approach using surface ElectroMyoGraphy (sEMG) is proposed to be applied in the field of veterinary medicine rehabilitation. The observed results are thought to be a direct result of nerve compression, leading to unusual patterns of muscle activation; this phenomenon can be attributed to muscle denervation, where the loss of Motor Units (MU) is the primary cause. This is thought to be responsible for the decrease in recorded sEMG amplitude and the increase in frequency observed in the pathological group.•This study involved rigorous animal preparation and signal acquisition protocols, involving multiple exercises and sub-movements, which were subsequently analysed.•RMSA is most used metric to analyse amplitude in sEMG signals, as it results in a more representative measurement of the signal variability than the Mean amplitude or the Standard Deviation.

Motor variability during resistance training: Acceleration signal as intensity indicator

Analysis of variability in physiological time series has been shown to be an indicator of the state of the organism. Although there is evidence of the usefulness of analysis of the amount and/or structure of variability (complexity) in cycling actions, there is limited knowledge about its application in resistance exercise. The aim of this study is to find out whether variability in acceleration signals can be an indicator of intensity level in a squat task. For this purpose, an experimental design was developed in which the following participated seventy-two participants (age = 25.7 ± 4.4 years; height = 169.2 ± 9.8 cm; body mass = 67.7 ± 11.2 kg; ratio 1RM/body mass = 1.4 ± 0.3). They performed four repetitions of back squat at loads of 10%, 30%, 50%, 70%, and 90% of 1RM. Acceleration during the exercise was recorded using an inertial measurement unit (IMU) and a force platform. The variability of the movement was then analyzed using Standard Deviation (SD), Detrended Fluctuation Analysis (DFA), Fuzzy Entropy (FuzzyEn), and Sample Entropy (SampEn). For the IMU and for the force platform, significant effects were observed in all variables (p &lt; 0.001). In pairwise comparisons, IMU showed a significant increase in motor complexity with increasing intensity, among most intensities, in DFA, FuzzyEn and SampEn. Differences in force platform were more limited, and only DFA detected differences between most intensities. The results suggest that measures of signal and acceleration variability may be a useful indicator of the relative intensity at which a squat exercise is performed.

In vivo targeted-imaging of mitochondrial acidification in an aristolochic acid I-induced nephrotoxicity mouse model by a fluorescent/photoacoustic bimodal probe

Aristolochic acid I (AAI), a natural compound in aristolochia type Chinese medicinal herb, is generally acknowledged to have nephrotoxicity, which may be associated with mitophagy. Mitophagy is a cellular process with important functions that drive AAI-induced renal injury. Mitochondrial pH is currently measured by fluorescent probes in cell culture, but existing probes do not allow for in situ imaging of AAI-induced mitophagy in vivo. We developed a ratiometric fluorescent/PA dual-modal probe with a silicon rhodamine fluorophore and a pH-sensitive hemicyanine dye covalently linked via a short chain to obtain a FRET type probe. The probe was used to measure AAI-mediated mitochondrial acidification in live cells and in vivo. The Förster resonance energy transfer (FRET)-mediated ratiometric and bimodal method can efficiently eliminate signal variability associated with the commonly used one-emission and single detection mode by ratiometric two channels of the donor and acceptor. The probe has good water-solubility and low molecular weight with two positively charged, facilitating its precise targeting into renal mitochondria, where the fluorescent/PA changes in response to mitochondrial acidification, enabling dynamic and semi-quantitative mapping of subtle changes in mitochondrial pH in AAI-induced nephrotoxicity mouse model for the first time. Also, the joint use of L-carnitine could mitigate the mitophagy in AAI-induced nephrotoxicity.

Pisolithus microcarpus isolates with contrasting abilities to colonise Eucalyptus grandis exhibit significant differences in metabolic signalling

Biotic factors in fungal exudates impact plant-fungal symbioses establishment. Mutualistic ectomycorrhizal fungi play various ecological roles in forest soils by interacting with trees. Despite progress in understanding secreted fungal signals, dynamics of signal production in situ before or during direct host root contact remain unclear. We need to better understand how variability in intra-species fungal signaling at these stages impacts symbiosis with host tissues. Using the ECM model Pisolithus microcarpus, we selected two isolates (Si9 and Si14) with different abilities to colonize Eucalyptus grandis roots. Hypothesizing that distinct early signalling and metabolite profiles between these isolates would influence colonization and symbiosis, we used microdialysis to non-destructively collect secreted metabolites from either the fungus, host, or both, capturing the dynamic interplay of pre-symbiotic signalling over 48 hours. Our findings revealed significant differences in metabolite profiles between Si9 and Si14, grown alone or with a host root. Si9, with lower colonization efficiency than Si14, secreted a more diverse range of compounds, including lipids, oligopeptides, and carboxylic acids. In contrast, Si14's secretions, similar to the host's, included more aminoglycosides. This study emphasizes the importance of intra-specific metabolomic diversity in ectomycorrhizal fungi, suggesting that early metabolite secretion is crucial for establishing successful mutualistic relationships.

Boltzmann's Theorem Revisited: Inaccurate Time-to-Action Clocks in Affective Disorders.

Timely goal-oriented behavior is essential for survival and is shaped by experience. In this paper, a multileveled approach was employed, ranging from the polymorphic level through thermodynamic molecular, cellular, intracellular, extracellular, non-neuronal organelles and electrophysiological waves, attesting for signal variability. By adopting Boltzmann's theorem as a thermodynamic conceptualization of brain work, we found deviations from excitation-inhibition balance and wave decoupling, leading to wider signal variability in affective disorders compared to healthy individuals. Recent evidence shows that the overriding on-off design of clock genes paces the accuracy of the multilevel parallel sequencing clocks and that the accuracy of the time-to-action is more crucial for healthy behavioral reactions than their rapidity or delays. In affective disorders, the multilevel clocks run free and lack accuracy of responsivity to environmentally triggered time-to-action as the clock genes are not able to rescue mitochondria organelles from oxidative stress to produce environmentally-triggered energy that is required for the accurate time-to-action and maintenance of the thermodynamic equilibrium. This maintenance, in turn, is dependent on clock gene transcription of electron transporters, leading to higher signal variability and less signal accuracy in affective disorders. From a Boltzmannian thermodynamic and energy-production perspective, the option of reversibility to a healthier time-toaction, reducing entropy is implied. We employed logic gates to show deviations from healthy levelwise communication and the reversed conditions through compensations implying the role of nonneural cells and the extracellular matrix in return to excitation-inhibition balance and accuracy in the time-to-action signaling.

The Role of Amide Proton Transfer (APT)-Weighted Imaging in Glioma: Assessment of Tumor Grading, Molecular Profile and Survival in Different Tumor Components.

Amide Proton Transfer-weighted (APTw) imaging is a molecular MRI technique used to quantify protein concentrations in gliomas, which have heterogeneous components with varying cellularity and metabolic activity. This study aimed to assess the correlation between the component-specific APT signal of the neoplasm and WHO grade, molecular profile and survival status. Sixty-one patients with adult-type diffuse gliomas were retrospectively analyzed. APT values were semi-automatically extracted from tumor solid and, whenever present, necrotic components. APT values were compared between groups stratified by WHO grade, IDH-mutation, MGMT promoter methylation and 1- and 2-year survival status using Wilcoxon rank-sum test, adjusting for multiple comparisons. Overall survival (OS) was analyzed in the subgroup of 48 patients with grade 4 tumors using Cox proportional-hazards models. Random-effects models were used to assess inter-subject heterogeneity of the mean APT values in each tumor component. APT values of the solid component significantly differed between patients with grades 2-3 and 4 tumors (mean 1.58 ± 0.50 vs. 2.04 ± 0.56, p = 0.028) and correlated with OS after 1 year (1.81 ± 0.58 in survivors vs. 2.17 ± 0.51 in deceased patients, p = 0.030). APT values did not differ by IDH-mutation, MGMT methylation, and 2-year survival status. Within grade 4 glioma patients, higher APT kurtosis of the solid component was a negative prognostic factor (hazard ratio = 1.60, p = 0.040). Mean APT values of the necrosis showed high inter-subject variability, although most necrotic tumors were grade 4 and IDH wildtype. In conclusion, APTw imaging in the solid component provided metrics associated with glioma grade and survival status but showed weak correlation with IDH-mutation and MGMT promoter methylation status, in contrast to previous works. Further research is needed to understand APT signal variability within the necrotic component of high-grade gliomas.

Stretched non-negative matrix factorization

A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.

Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis

Background22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. MethodsWe analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. ResultsBSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. Conclusion22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.

Emotion recognition of EEG signals based on contrastive learning graph convolutional model.

Electroencephalogram (EEG) signals offer invaluable insights into the complexities of emotion generation within the brain. Yet, the variability in EEG signals across individuals presents a formidable obstacle for empirical implementations. Our research addresses these challenges innovatively, focusing on the commonalities within distinct subjects' EEG data.We introduce a novel approach named Contrastive Learning Graph Convolutional Network (CLGCN). This method captures the distinctive features and crucial channel nodes related to individuals' emotional states. Specifically, CLGCN merges the dual benefits of Contrastive Learning's synchronous multisubject data learning and the Graph Convolutional Network's proficiency in deciphering brain connectivity matrices.Understanding multifaceted brain functions and their information interchange processes is realized as CLGCN generates a standardized brain network learning matrix during a dataset's learning process. Our model significantly streamlines the retraining process for new subjects, requiring only 5% of the initial sample size for fine-tuning to attain a remarkable 92.8% accuracy rate. Additionally, our model has undergone extensive testing on the DEAP and SEED datasets, demonstrating the effectiveness of our model.&#xD.

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Abstract Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise—the dominant contributing noise component in high-resolution fMRI. NOise Reduction with DIstribution Corrected Principal Component Analysis (NORDIC PCA) is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. While investigating auditory functional responses poses unique challenges, we anticipated NORDIC to have a positive impact on the data on the basis of previous applications. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we did observe a reduction in the average response amplitude (percent signal change) within regions of interest, which may suggest that a portion of the signal of interest, which could not be distinguished from general i.i.d. noise, was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high-resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.