Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability – in science in general, but also specifically in human neuroscience – have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

[1]  Eric W. Bridgeford,et al.  ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences , 2023, Nature Methods.

[2]  S. Luck,et al.  Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings , 2023, bioRxiv.

[3]  Y. Niv,et al.  Improving the reliability of cognitive task measures: A narrative review. , 2023, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[4]  S. Luck,et al.  Variations in ERP Data Quality Across Paradigms, Participants, and Scoring Procedures , 2023, bioRxiv.

[5]  T. Lonsdorf,et al.  Data sharing in experimental fear and anxiety research: From challenges to a dynamically growing database in 10 simple steps , 2022, Neuroscience & Biobehavioral Reviews.

[6]  D. Norris,et al.  A comparison of multiband and multiband multiecho gradient‐echo EPI for task fMRI at 3 T , 2022, Human brain mapping.

[7]  Anna-Lena Schubert,et al.  How robust is the relationship between neural processing speed and cognitive abilities? , 2022, Psychophysiology.

[8]  Stephanie J. Wilson,et al.  Oxytocin reactivity to a lab-based stressor predicts support seeking after stress in daily life: Implications for the Tend-and-Befriend theory , 2022, Psychoneuroendocrinology.

[9]  Sander van Bree,et al.  The Brain Time Toolbox, a software library to retune electrophysiology data to brain dynamics , 2022 .

[10]  B. Cludius,et al.  (When and how) does basic research in clinical psychology lead to more effective psychological treatment for mental disorders? , 2022, Clinical psychology review.

[11]  T. Stalder,et al.  Open and reproducible science practices in psychoneuroendocrinology: Opportunities to foster scientific progress , 2022, Comprehensive psychoneuroendocrinology.

[12]  Jacob L. Orquin,et al.  RETRACTED ARTICLE: Eye tracking: empirical foundations for a minimal reporting guideline , 2022, Behavior Research Methods.

[13]  W. Mileski,et al.  Development of a mobile low-field MRI scanner , 2022, Scientific Reports.

[14]  T. Lonsdorf,et al.  Navigating the manyverse of skin conductance response quantification approaches - A direct comparison of trough-to-peak, baseline correction, and model-based approaches in Ledalab and PsPM. , 2022, Psychophysiology.

[15]  Jasmine A. C. Kwasa,et al.  Addressing racial and phenotypic bias in human neuroscience methods , 2022, Nature Neuroscience.

[16]  T. Lonsdorf,et al.  Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear , 2022, bioRxiv.

[17]  Timothy O. Laumann,et al.  Reproducible brain-wide association studies require thousands of individuals , 2022, Nature.

[18]  G. Domes,et al.  How to choose the size of facial areas of interest in interactive eye tracking , 2022, PloS one.

[19]  A. Anderson,et al.  Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography , 2021, Human brain mapping.

[20]  Britta U. Westner,et al.  A unified view on beamformers for M/EEG source reconstruction , 2021, NeuroImage.

[21]  E. Phelps,et al.  Rating expectations can slow aversive reversal learning , 2021, Psychophysiology.

[22]  Introduction to the Event , 2021, In the Event of Women.

[23]  Matthew S. Goodwin,et al.  Adaptive thresholding increases ability to detect changes in rate of skin conductance responses to psychologically arousing stimuli , 2021 .

[24]  Vasant Honavar,et al.  Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics , 2021, Network Neuroscience.

[25]  D. Kugiumtzis,et al.  Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG , 2021, Frontiers in Network Physiology.

[26]  Brian Knutson,et al.  Multi-band FMRI compromises detection of mesolimbic reward responses , 2021, NeuroImage.

[27]  Remi Gau,et al.  A fMRI pre-registration template , 2021 .

[28]  V. Nikulin,et al.  Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms , 2021, NeuroImage.

[29]  P. Fitzgerald,et al.  EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation. , 2021, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[30]  Simon Morand-Beaulieu,et al.  Test-Retest Reliability of Event-Related Potentials Across Three Tasks , 2021, Journal of Psychophysiology.

[31]  Patrick Mair,et al.  Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity , 2021, NeuroImage.

[32]  M. Gamer,et al.  Search for the Unknown: Guidance of Visual Search in the Absence of an Active Template , 2021, Psychological science.

[33]  Charles J. Lynch,et al.  Improving precision functional mapping routines with multi-echo fMRI , 2021, Current Opinion in Behavioral Sciences.

[34]  Lianne H. Scholtens,et al.  Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space , 2021, NeuroImage.

[35]  Z. Kurth-Nelson,et al.  Temporally delayed linear modelling (TDLM) measures replay in both animals and humans , 2021, eLife.

[36]  Kåre Synnes,et al.  Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli , 2021, Sensors.

[37]  Jennifer H. Pfeifer,et al.  A Researcher’s Guide to the Measurement and Modeling of Puberty in the ABCD Study® at Baseline , 2021, Frontiers in Endocrinology.

[38]  Peter E. Clayson,et al.  Data quality and reliability metrics for event-related potentials (ERPs): The utility of subject-level reliability. , 2021, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[39]  Ingrid K. Weigold,et al.  Traditional and Modern Convenience Samples: An Investigation of College Student, Mechanical Turk, and Mechanical Turk College Student Samples , 2021, Social Science Computer Review.

[40]  Andrew X. Stewart,et al.  Standardized measurement error: A universal metric of data quality for averaged event-related potentials. , 2021, Psychophysiology.

[41]  S. Sperber Invisible women: Exposing data bias in a world designed for men , 2021, Gender, Work & Organization.

[42]  G. Jackson,et al.  Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage , 2021, Frontiers in Neurology.

[43]  T. Wager,et al.  Functional MRI Can Be Highly Reliable, but It Depends on What You Measure: A Commentary on Elliott et al. (2020) , 2021, Psychological science.

[44]  Uri Alon,et al.  Hormone seasonality in medical records suggests circannual endocrine circuits , 2021, Proceedings of the National Academy of Sciences.

[45]  Evan M. Gordon,et al.  Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior , 2021, bioRxiv.

[46]  Daniel Lakens,et al.  Sample Size Justification , 2021, Collabra: Psychology.

[47]  M. Del Giudice,et al.  A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions , 2021, Advances in Methods and Practices in Psychological Science.

[48]  Ling-Li Zeng,et al.  Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics , 2020, Human brain mapping.

[49]  Evan M. Gordon,et al.  Parallel hippocampal-parietal circuits for self- and goal-oriented processing , 2020, Proceedings of the National Academy of Sciences.

[50]  Abigail S. Greene,et al.  Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol , 2020, Scientific Reports.

[51]  O. Andreassen,et al.  Improving the precision of intranasal oxytocin research , 2020, Nature Human Behaviour.

[52]  Jan R. Wessel,et al.  #EEGManyLabs: Investigating the replicability of influential EEG experiments , 2020, Cortex.

[53]  Richard Gao,et al.  Parameterizing neural power spectra into periodic and aperiodic components , 2020, Nature Neuroscience.

[54]  Matthias F. J. Sperl,et al.  Learning dynamics of electrophysiological brain signals during human fear conditioning , 2020, NeuroImage.

[55]  Md. Rafiul Amin,et al.  Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors , 2020, IEEE Transactions on Biomedical Engineering.

[56]  I. Økland,et al.  The Effects of Hormonal Contraceptives on the Brain: A Systematic Review of Neuroimaging Studies , 2020, Frontiers in Psychology.

[57]  Olaf Sporns,et al.  High-amplitude cofluctuations in cortical activity drive functional connectivity , 2020, Proceedings of the National Academy of Sciences.

[58]  Farnaz Zamani Esfahlani,et al.  Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture , 2020, Nature Neuroscience.

[59]  Dominik R Bach,et al.  Filtering and model-based analysis independently improve skin-conductance response measures in the fMRI environment: Validation in a sample of women with PTSD. , 2020, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[60]  Klaus Gramann,et al.  Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments , 2020, bioRxiv.

[61]  Riitta Salmelin,et al.  Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research , 2020, Nature Neuroscience.

[62]  Susana Ladra,et al.  From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas , 2020, Frontiers in Neuroinformatics.

[63]  Anne M. Scheel,et al.  Why Hypothesis Testers Should Spend Less Time Testing Hypotheses , 2020, Perspectives on psychological science : a journal of the Association for Psychological Science.

[64]  D. Quintana,et al.  Advances in the field of intranasal oxytocin research: lessons learned and future directions for clinical research , 2020, Molecular Psychiatry.

[65]  Leif D. Nelson,et al.  Specification curve analysis , 2020, Nature Human Behaviour.

[66]  Vincenzo Russo,et al.  ESB: A low-cost EEG Synchronization Box , 2020, HardwareX.

[67]  L. Alloy,et al.  Back to Basics: The Importance of Measurement Properties in Biological Psychiatry , 2020, Neuroscience & Biobehavioral Reviews.

[68]  J. Ioannidis,et al.  Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals , 2020, NeuroImage.

[69]  M. Gamer,et al.  Social anxiety is associated with heart rate but not gaze behavior in a real social interaction , 2020, Journal of behavior therapy and experimental psychiatry.

[70]  C. Hilgetag,et al.  Individual differences in local functional brain connectivity affect TMS effects on behavior , 2020, Scientific Reports.

[71]  Chun-Hung Yeh,et al.  Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities , 2020, Journal of magnetic resonance imaging : JMRI.

[72]  Louisa Kulke,et al.  Combining eye tracking with EEG: Effects of filter settings on EEG for trials containing task relevant eye-movements , 2020, bioRxiv.

[73]  Matthew J. Brookes,et al.  Multi-channel whole-head OPM-MEG: Helmet design and a comparison with a conventional system , 2020, NeuroImage.

[74]  Kentaro Shirotsuki,et al.  Association between hair cortisol and diurnal basal cortisol levels: A 30-day validation study , 2020, Psychoneuroendocrinology.

[75]  Tyler Santander,et al.  Progesterone shapes medial temporal lobe volume across the human menstrual cycle , 2020, NeuroImage.

[76]  M. Gamer,et al.  Individual patterns of visual exploration predict the extent of fear generalization in humans , 2020 .

[77]  D. Bach,et al.  Calibrating the experimental measurement of psychological attributes , 2020, Nature Human Behaviour.

[78]  Sam Parsons,et al.  Exploring reliability heterogeneity with multiverse analyses: Data processing decisions unpredictably influence measurement reliability , 2020, Meta-Psychology.

[79]  Marcus Nyström,et al.  The impact of slippage on the data quality of head-worn eye trackers , 2020, Behavior Research Methods.

[80]  Vince D. Calhoun,et al.  Questions and controversies in the study of time-varying functional connectivity in resting fMRI , 2020, Network Neuroscience.

[81]  Jan Richter,et al.  Navigating the garden of forking paths for data exclusions in fear conditioning research , 2019, eLife.

[82]  Michael B. Miller,et al.  Functional reorganization of brain networks across the human menstrual cycle , 2019, NeuroImage.

[83]  Steven E Petersen,et al.  Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry. , 2019, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[84]  Nathalie George,et al.  Statistical power: Implications for planning MEG studies , 2019, NeuroImage.

[85]  Bradley C. Love,et al.  Variability in the analysis of a single neuroimaging dataset by many teams , 2019, Nature.

[86]  Soosan Beheshti,et al.  A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities , 2019, Journal of Neuroscience Methods.

[87]  John P. A. Ioannidis,et al.  Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals , 2019, NeuroImage.

[88]  G. Shields Stress and cognition: A user’s guide to designing and interpreting studies , 2019, Psychoneuroendocrinology.

[89]  Matthew J. Brookes,et al.  Optically pumped magnetometers: From quantum origins to multi-channel magnetoencephalography , 2019, NeuroImage.

[90]  Jordan C. Barone,et al.  How to study the menstrual cycle: Practical tools and recommendations , 2020, Psychoneuroendocrinology.

[91]  M. Hines Neuroscience and Sex/Gender: Looking Back and Forward , 2019, The Journal of Neuroscience.

[92]  Dustin Scheinost,et al.  A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis , 2019, NeuroImage.

[93]  Kaylie A. Carbine,et al.  Methodological reporting behavior, sample sizes, and statistical power in studies of event-related potentials: Barriers to reproducibility and replicability. , 2019, Psychophysiology.

[94]  Marc Brysbaert,et al.  How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables , 2019, Journal of cognition.

[95]  L. Andersen,et al.  Associations between serum and plasma brain-derived neurotrophic factor and influence of storage time and centrifugation strategy , 2019, Scientific Reports.

[96]  Robert Oostenveld,et al.  EEG-BIDS, an extension to the brain imaging data structure for electroencephalography , 2019, Scientific Data.

[97]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[98]  Xi-Nian Zuo,et al.  Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. , 2019, Cerebral cortex.

[99]  Dejan Draschkow,et al.  Cluster-based permutation tests of MEG/EEG data do not establish significance of effect latency or location. , 2019, Psychophysiology.

[100]  Jingwei Li,et al.  Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity , 2019, bioRxiv.

[101]  Thomas Schäfer,et al.  The Meaningfulness of Effect Sizes in Psychological Research: Differences Between Sub-Disciplines and the Impact of Potential Biases , 2019, Front. Psychol..

[102]  Christoph M. Michel,et al.  EEG Source Imaging: A Practical Review of the Analysis Steps , 2019, Front. Neurol..

[103]  Paulo Barraza,et al.  Implementing EEG hyperscanning setups , 2019, MethodsX.

[104]  Timothy J. Andrews,et al.  Power Contours: Optimising Sample Size and Precision in Experimental Psychology and Human Neuroscience , 2019, Psychological methods.

[105]  Lisa J. Weckesser,et al.  The psychometric properties and temporal dynamics of subjective stress, retrospectively assessed by different informants and questionnaires, and hair cortisol concentrations , 2019, Scientific Reports.

[106]  Kenneth Kreutz-Delgado,et al.  ICLabel: An automated electroencephalographic independent component classifier, dataset, and website , 2019, NeuroImage.

[107]  Heinrich René Liesefeld,et al.  Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods , 2018, Front. Neurosci..

[108]  Kotagiri Ramamohanarao,et al.  Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? , 2018, Magnetic resonance in medicine.

[109]  Kirstie J. Whitaker,et al.  Raincloud plots: a multi-platform tool for robust data visualization , 2018, PeerJ Prepr..

[110]  Simone Kühn,et al.  Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED) , 2018, eLife.

[111]  Leonie Koban,et al.  Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging , 2018, Neuron.

[112]  Robert Oostenveld,et al.  MEG-BIDS, the brain imaging data structure extended to magnetoencephalography , 2018, Scientific Data.

[113]  Megan A. Boudewyn,et al.  How many trials does it take to get a significant ERP effect? It depends. , 2018, Psychophysiology.

[114]  Satrajit S. Ghosh,et al.  FMRIPrep: a robust preprocessing pipeline for functional MRI , 2018, Nature Methods.

[115]  Michael W. Cole,et al.  Task activations produce spurious but systematic inflation of task functional connectivity estimates , 2018, NeuroImage.

[116]  Bennett A. Landman,et al.  Histological validation of diffusion MRI fiber orientation distributions and dispersion , 2018, NeuroImage.

[117]  Daniel B. Rowe,et al.  Impacts of simultaneous multislice acquisition on sensitivity and specificity in fMRI , 2018, NeuroImage.

[118]  Dustin Scheinost,et al.  Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility , 2017, Cerebral cortex.

[119]  E. Adam,et al.  Diurnal cortisol slopes and mental and physical health outcomes: A systematic review and meta-analysis , 2017, Psychoneuroendocrinology.

[120]  Evan M. Gordon,et al.  Precision Functional Mapping of Individual Human Brains , 2017, Neuron.

[121]  Annchen R. Knodt,et al.  The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences , 2017, Behavior research methods.

[122]  Annchen R. Knodt,et al.  The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences , 2017, Behavior Research Methods.

[123]  Kamil Ugurbil,et al.  Imaging at ultrahigh magnetic fields: History, challenges, and solutions , 2017, NeuroImage.

[124]  Evan M. Gordon,et al.  Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI , 2017, bioRxiv.

[125]  J. Kaiser,et al.  Stability of BDNF in Human Samples Stored Up to 6 Months and Correlations of Serum and EDTA-Plasma Concentrations , 2017, International journal of molecular sciences.

[126]  Kristoffer Hougaard Madsen,et al.  Are Movement Artifacts in Magnetic Resonance Imaging a Real Problem?—A Narrative Review , 2017, Front. Neurol..

[127]  S. Gais,et al.  Decoding material-specific memory reprocessing during sleep in humans , 2017, Nature Communications.

[128]  Meredith Ringel Morris,et al.  Toward Everyday Gaze Input: Accuracy and Precision of Eye Tracking and Implications for Design , 2017, CHI.

[129]  Jonathan R. Polimeni,et al.  Analysis strategies for high-resolution UHF-fMRI data , 2017, NeuroImage.

[130]  Daniel Rueckert,et al.  Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex , 2017, NeuroImage.

[131]  Andrew M. McCullough,et al.  The Effects of Acute Stress on Episodic Memory: A Meta-Analysis and Integrative Review , 2017, Psychological bulletin.

[132]  M. Gamer,et al.  Preferential Processing of Social Features and Their Interplay with Physical Saliency in Complex Naturalistic Scenes , 2017, Front. Psychol..

[133]  Matteo Fraschini,et al.  A comparison between scalp- and source-reconstructed EEG networks , 2017, Scientific Reports.

[134]  Steen Moeller,et al.  Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise , 2017, Front. Neurosci..

[135]  Kenneth Holmqvist,et al.  Joint visual working memory through implicit collaboration , 2017 .

[136]  D. Rees,et al.  Measuring cortisol in serum, urine and saliva – are our assays good enough? , 2017, Annals of clinical biochemistry.

[137]  Chenggang Zhang,et al.  A crucial temporal accuracy test of combining EEG and Tobii eye tracker , 2017, Medicine.

[138]  J. Ioannidis,et al.  Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature , 2017, PLoS biology.

[139]  E. Montoya,et al.  How Oral Contraceptives Impact Social-Emotional Behavior and Brain Function , 2017, Trends in Cognitive Sciences.

[140]  Tobias U. Hauser,et al.  The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data , 2017, Journal of Neuroscience Methods.

[141]  B. McEwen,et al.  Understanding the broad influence of sex hormones and sex differences in the brain , 2017, Journal of neuroscience research.

[142]  Clemens Brunner,et al.  Volume Conduction Influences Scalp-Based Connectivity Estimates , 2016, Front. Comput. Neurosci..

[143]  Martin Eimer,et al.  Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection , 2016, bioRxiv.

[144]  Christos Papavassiliou,et al.  Hearables: Multimodal physiological in-ear sensing , 2016, Scientific Reports.

[145]  T. Lonsdorf,et al.  Don't startle me-Interference of startle probe presentations and intermittent ratings with fear acquisition. , 2016, Psychophysiology.

[146]  Francis Tuerlinckx,et al.  Increasing Transparency Through a Multiverse Analysis , 2016, Perspectives on psychological science : a journal of the Association for Psychological Science.

[147]  Stefan Haufe,et al.  Consistency of EEG source localization and connectivity estimates , 2016, NeuroImage.

[148]  M. Bakermans-Kranenburg,et al.  The Role of Oxytocin in Parenting and as Augmentative Pharmacotherapy: Critical Issues and Bold Conjectures , 2016, Journal of neuroendocrinology.

[149]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[150]  Thomas E. Nichols,et al.  Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.

[151]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[152]  M. Goldhacker,et al.  Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement , 2016, NeuroImage.

[153]  Raveendran Paramesran,et al.  Review of medical image quality assessment , 2016, Biomed. Signal Process. Control..

[154]  Krzysztof J. Gorgolewski,et al.  MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites , 2016, bioRxiv.

[155]  Raag D. Airan,et al.  Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI , 2016, Human brain mapping.

[156]  U. Hegerl,et al.  Tobacco use is associated with reduced amplitude and intensity dependence of the cortical auditory evoked N1-P2 component , 2016, Psychopharmacology.

[157]  R. Nelson,et al.  Endocrine Effects of Circadian Disruption. , 2016, Annual review of physiology.

[158]  E. Adam,et al.  Assessment of the cortisol awakening response: Expert consensus guidelines , 2016, Psychoneuroendocrinology.

[159]  D. Tucker,et al.  EEG source localization: Sensor density and head surface coverage , 2015, Journal of Neuroscience Methods.

[160]  S. Debener,et al.  Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear , 2015, Scientific Reports.

[161]  Klaus-Robert Müller,et al.  On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[162]  I. Hooge,et al.  Consequences of eye color, positioning, and head movement for eye-tracking data quality in infant research , 2015 .

[163]  Adam Gazzaley,et al.  Age-Related Changes in 1/f Neural Electrophysiological Noise , 2015, The Journal of Neuroscience.

[164]  Michael C. Frank,et al.  Estimating the reproducibility of psychological science , 2015, Science.

[165]  Evan M. Gordon,et al.  Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.

[166]  S. Luck,et al.  How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition. , 2015, Psychophysiology.

[167]  V. Garovic,et al.  Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm , 2015, PLoS biology.

[168]  P. V. Rekkas,et al.  Progesterone mediates brain functional connectivity changes during the menstrual cycle—a pilot resting state MRI study , 2015, Front. Neurosci..

[169]  Arno Villringer,et al.  Sex hormones affect neurotransmitters and shape the adult female brain during hormonal transition periods , 2015, Front. Neurosci..

[170]  Damien A. Fair,et al.  Connectotyping: Model Based Fingerprinting of the Functional Connectome , 2014, PloS one.

[171]  Biyu J. He Scale-free brain activity: past, present, and future , 2014, Trends in Cognitive Sciences.

[172]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[173]  L. Astolfi,et al.  Social neuroscience and hyperscanning techniques: Past, present and future , 2014, Neuroscience & Biobehavioral Reviews.

[174]  M. Papademetriou,et al.  Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa. , 2014, Scientific Reports.

[175]  Steven J. Luck,et al.  ERPLAB: an open-source toolbox for the analysis of event-related potentials , 2014, Front. Hum. Neurosci..

[176]  Oliver Kraff,et al.  Vestibular Effects of a 7 Tesla MRI Examination Compared to 1.5 T and 0 T in Healthy Volunteers , 2014, PloS one.

[177]  P. Blignaut,et al.  Eye-tracking data quality as affected by ethnicity and experimental design , 2014, Behavior research methods.

[178]  Thomas E. Nichols,et al.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data , 2014, Brain Imaging and Behavior.

[179]  Dominik R. Bach,et al.  Sympathetic nerve activity can be estimated from skin conductance responses — A comment on Henderson et al. (2012)☆ , 2014, NeuroImage.

[180]  Karl J. Friston,et al.  An improved algorithm for model-based analysis of evoked skin conductance responses☆ , 2013, Biological Psychology.

[181]  C. Cook,et al.  Measurement of steroid hormones in saliva: Effects of sample storage condition , 2013, Scandinavian journal of clinical and laboratory investigation.

[182]  Christopher G. Courtney,et al.  Can you give me a hand? A comparison of hands and feet as optimal anatomical sites for skin conductance recording. , 2013, Psychophysiology.

[183]  C. Feng,et al.  Response to comments on ‘Log transformation: Application and interpretation in biomedical research’ , 2013, Statistics in medicine.

[184]  Vivek Prabhakaran,et al.  The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated , 2013, NeuroImage.

[185]  E. Kochs,et al.  Plasma Oxytocin and Vasopressin do not Predict Neuropeptide Concentrations in Human Cerebrospinal Fluid , 2013, Journal of neuroendocrinology.

[186]  F. Plessow,et al.  Transformation techniques for cross-sectional and longitudinal endocrine data: Application to salivary cortisol concentrations , 2013, Psychoneuroendocrinology.

[187]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[188]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[189]  Marcus Nyström,et al.  The influence of calibration method and eye physiology on eyetracking data quality , 2013, Behavior research methods.

[190]  Robert Oostenveld,et al.  Online and offline tools for head movement compensation in MEG , 2013, NeuroImage.

[191]  Changyong Feng,et al.  Log transformation: application and interpretation in biomedical research , 2013, Statistics in medicine.

[192]  Joachim Gross,et al.  Good practice for conducting and reporting MEG research , 2013, NeuroImage.

[193]  Stefan Haufe,et al.  A critical assessment of connectivity measures for EEG data: A simulation study , 2013, NeuroImage.

[194]  Lucas C. Parra,et al.  Subject position affects EEG magnitudes , 2013, NeuroImage.

[195]  Joshua Carp,et al.  On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments , 2012, Front. Neurosci..

[196]  Dan Witzner Hansen,et al.  Parallax error in the monocular head-mounted eye trackers , 2012, UbiComp.

[197]  John Ashburner,et al.  SPM: A history , 2012, NeuroImage.

[198]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[199]  Jean-Baptiste Poline,et al.  The general linear model and fMRI: Does love last forever? , 2012, NeuroImage.

[200]  Erich Schröger,et al.  Filter Effects and Filter Artifacts in the Analysis of Electrophysiological Data , 2012, Front. Psychology.

[201]  Guillaume A. Rousselet,et al.  Does Filtering Preclude Us from Studying ERP Time-Courses? , 2012, Front. Psychology.

[202]  Redmond G O'Connell,et al.  Retest reliability of event-related potentials: evidence from a variety of paradigms. , 2012, Psychophysiology.

[203]  Marcus Nyström,et al.  Eye tracker data quality: what it is and how to measure it , 2012, ETRA.

[204]  Jerry L. Prince,et al.  Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI , 2012, NeuroImage.

[205]  Andreas Heinz,et al.  Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures , 2012, NeuroImage.

[206]  Rufin VanRullen,et al.  Four Common Conceptual Fallacies in Mapping the Time Course of Recognition , 2011, Front. Psychology.

[207]  David M. Groppe,et al.  Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. , 2011, Psychophysiology.

[208]  A. Jacobs,et al.  Coregistration of eye movements and EEG in natural reading: analyses and review. , 2011, Journal of experimental psychology. General.

[209]  Kenneth Holmqvist,et al.  Eye tracking: a comprehensive guide to methods and measures , 2011 .

[210]  M. Tangermann,et al.  Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals , 2011, Behavioral and Brain Functions.

[211]  Sébastien Ourselin,et al.  A comparison of voxel and surface based cortical thickness estimation methods , 2011, NeuroImage.

[212]  Karl J. Friston,et al.  Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG , 2011, Comput. Intell. Neurosci..

[213]  N. Bolger,et al.  Social effects of oxytocin in humans: context and person matter , 2011, Trends in Cognitive Sciences.

[214]  N. Schneiderman,et al.  Evaluation of Enzyme Immunoassay and Radioimmunoassay Methods for the Measurement of Plasma Oxytocin Nih Public Access Author Manuscript Introduction , 2022 .

[215]  Guillaume A. Rousselet,et al.  LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data , 2011, Comput. Intell. Neurosci..

[216]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[217]  P. Federico Simultaneous Eeg and Fmri: Recording, Analysis and Application , 2010, Neurology.

[218]  Anders M. Dale,et al.  Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.

[219]  David M. Simcha,et al.  Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.

[220]  Marcus Nyström,et al.  Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more , 2010 .

[221]  S. Luck,et al.  The effects of electrode impedance on data quality and statistical significance in ERP recordings. , 2010, Psychophysiology.

[222]  M. Benedek,et al.  A continuous measure of phasic electrodermal activity , 2010, Journal of Neuroscience Methods.

[223]  J. Henrich,et al.  The weirdest people in the world? , 2010, Behavioral and Brain Sciences.

[224]  G. Chrousos,et al.  Interactions of the circadian CLOCK system and the HPA axis , 2010, Trends in Endocrinology & Metabolism.

[225]  Till R. Schneider,et al.  Using ICA for the Analysis of Multi-Channel EEG Data , 2010 .

[226]  D. Fessler,et al.  Oral Contraceptives Suppress Ovarian Hormone Production , 2010, Psychological science.

[227]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[228]  E. Erdfelder,et al.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses , 2009, Behavior research methods.

[229]  A. Zellner,et al.  INTRODUCTION TO MEASUREMENT WITH THEORY , 2009, Macroeconomic Dynamics.

[230]  S. Orr,et al.  An alternative scoring method for skin conductance responding in a differential fear conditioning paradigm with a long-duration conditioned stimulus. , 2009, Psychophysiology.

[231]  Abhyuday Mandal,et al.  Multi-objective optimal experimental designs for event-related fMRI studies , 2009, NeuroImage.

[232]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[233]  Stefan Wüst,et al.  Why do we respond so differently? Reviewing determinants of human salivary cortisol responses to challenge , 2009, Psychoneuroendocrinology.

[234]  S. Entringer,et al.  Covariance Between Psychological and Endocrine Responses to Pharmacological Challenge and Psychosocial Stress: A Question of Timing , 2008, Psychosomatic medicine.

[235]  Russell A. Poldrack,et al.  Guidelines for reporting an fMRI study , 2008, NeuroImage.

[236]  Brian Scassellati,et al.  The incomplete fixation measure , 2008, ETRA.

[237]  G. Gratton,et al.  Combining structural and functional neuroimaging data for studying brain connectivity: a review. , 2008, Psychophysiology.

[238]  J. Lagopoulos Electrodermal activity , 2007, Acta Neuropsychiatrica.

[239]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[240]  M. Dawson,et al.  The electrodermal system , 2007 .

[241]  Gian Luca Romani,et al.  Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis , 2007, NeuroImage.

[242]  Bettina Sorger,et al.  Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact , 2007, NeuroImage.

[243]  Matt A Bernstein,et al.  Imaging artifacts at 3.0T , 2006, Journal of magnetic resonance imaging : JMRI.

[244]  Daniel Brandeis,et al.  Synchronization facilitates removal of MRI artefacts from concurrent EEG recordings and increases usable bandwidth , 2006, NeuroImage.

[245]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[246]  S.C. Strother,et al.  Evaluating fMRI preprocessing pipelines , 2006, IEEE Engineering in Medicine and Biology Magazine.

[247]  A. Young,et al.  Assessing cortisol and dehydroepiandrosterone (DHEA) in saliva: effects of collection method , 2006, Journal of psychopharmacology.

[248]  Thomas E. Nichols,et al.  Non-white noise in fMRI: Does modelling have an impact? , 2006, NeuroImage.

[249]  Rami K. Niazy,et al.  Removal of FMRI environment artifacts from EEG data using optimal basis sets , 2005, NeuroImage.

[250]  H. Sequeira,et al.  Diurnal autonomic variations and emotional reactivity , 2005, Biological Psychology.

[251]  A. Gelman Discussion of "Analysis of variance--why it is more important than ever" by A. Gelman , 2005, math/0508530.

[252]  Karl J. Friston,et al.  Mixed-effects and fMRI studies , 2005, NeuroImage.

[253]  John M Lachin,et al.  The role of measurement reliability in clinical trials , 2004, Clinical trials.

[254]  E. Young,et al.  Cortisol pulsatility and its role in stress regulation and health , 2004, Frontiers in Neuroendocrinology.

[255]  Brenna Argall,et al.  SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[256]  Stephen M. Smith,et al.  General multilevel linear modeling for group analysis in FMRI , 2003, NeuroImage.

[257]  W. Drevets,et al.  Glucocorticoid regulation of diverse cognitive functions in normal and pathological emotional states , 2003, Neuroscience & Biobehavioral Reviews.

[258]  D. Borsboom,et al.  The Theoretical Status of Latent Variables , 2003 .

[259]  Karl J. Friston,et al.  Estimating efficiency a priori: a comparison of blocked and randomized designs , 2003, NeuroImage.

[260]  Thomas E. Nichols,et al.  Optimization of experimental design in fMRI: a general framework using a genetic algorithm , 2003, NeuroImage.

[261]  Tim Halverson,et al.  Cleaning up systematic error in eye-tracking data by using required fixation locations , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[262]  Wilkin Chau,et al.  An Empirical Comparison of SPM Preprocessing Parameters to the Analysis of fMRI Data , 2002, NeuroImage.

[263]  Klaas E. Stephan,et al.  The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.

[264]  R. Feise Do multiple outcome measures require p-value adjustment? , 2002, BMC medical research methodology.

[265]  Jan Born,et al.  Sniffing neuropeptides: a transnasal approach to the human brain , 2002, Nature Neuroscience.

[266]  W. Singer,et al.  Dynamic predictions: Oscillations and synchrony in top–down processing , 2001, Nature Reviews Neuroscience.

[267]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[268]  Don M. Tucker,et al.  The spatial resolution of scalp EEG , 2001, Neurocomputing.

[269]  Joseph H. Goldberg,et al.  Identifying fixations and saccades in eye-tracking protocols , 2000, ETRA.

[270]  C. Sandman,et al.  The auditory event-related potential is a stable and reliable measure in elderly subjects over a 3 year period , 2000, Clinical Neurophysiology.

[271]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[272]  Douglas C. Noll,et al.  Overt Verbal Responding during fMRI Scanning: Empirical Investigations of Problems and Potential Solutions , 1999, NeuroImage.

[273]  Ken Kelley,et al.  Designing Experiments and Analyzing Data: A Model Comparison Perspective, Third Edition , 1999 .

[274]  Karl J. Friston,et al.  Stochastic Designs in Event-Related fMRI , 1999, NeuroImage.

[275]  O Josephs,et al.  Event-related functional magnetic resonance imaging: modelling, inference and optimization. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[276]  P. Morosan,et al.  Observer-Independent Method for Microstructural Parcellation of Cerebral Cortex: A Quantitative Approach to Cytoarchitectonics , 1999, NeuroImage.

[277]  R Mezrich,et al.  A perspective on K-space. , 1995, Radiology.

[278]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[279]  Alan C. Evans,et al.  A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[280]  Scott E. Maxwell,et al.  Designing Experiments and Analyzing Data: A Model Comparison Perspective , 1990 .

[281]  E. Haus,et al.  Chronobiology in the endocrine system. , 1989, Advanced drug delivery reviews.

[282]  D. Siddle,et al.  Effects of stimulus omission and stimulus novelty on dishabituation of the skin conductance response. , 1986, Psychophysiology.

[283]  R F Vining,et al.  Salivary Cortisol: A Better Measure of Adrenal Cortical Function than Serum Cortisol , 1983, Annals of clinical biochemistry.

[284]  G. Ben-Shakhar,et al.  Publication recommendations for electrodermal measurements. , 1981, Psychophysiology.

[285]  P. Venables,et al.  Direct measurement of skin conductance: a proposal for standardization. , 1971, Psychophysiology.

[286]  L. Cronbach The two disciplines of scientific psychology. , 1957 .

[287]  C. Spearman CORRELATION CALCULATED FROM FAULTY DATA , 1910 .

[288]  O. Sporns,et al.  How much data do we need? Lower bounds of brain activation states to predict human cognitive ability , 2022 .

[289]  V. Preedy,et al.  Test–Retest Reliability , 2022, The SAGE Encyclopedia of Research Design.

[290]  E. K. Shriver,et al.  Measuring Sex, Gender Identity, and Sexual Orientation , 2022 .

[291]  Sam Parsons,et al.  Splithalf: Robust Estimates of Split Half Reliability , 2021, J. Open Source Softw..

[292]  R. Deriche,et al.  Diffusion MRI Fiber Orientation Distribution Function Estimation Using Voxel-Wise Spherical U-Net , 2021, Computational Diffusion MRI.

[293]  Marilyn A. Uy,et al.  The Body and the Brain: Measuring Skin Conductance Responses to Understand the Emotional Experience , 2019 .

[294]  Venkatapavani Pallavi Punugu Machine Learning in Neuroimaging , 2017 .

[295]  Nicholas Gaspelin,et al.  How to get statistically significant effects in any ERP experiment (and why you shouldn't). , 2017, Psychophysiology.

[296]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[297]  Andrea Bergmann,et al.  Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .

[298]  Dorothy V. M. Bishop,et al.  Journal of Neuroscience Methods , 2015 .

[299]  Zhongming Liu,et al.  Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal , 2015, Brain Topography.

[300]  G. A. Miller,et al.  Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. , 2014, Psychophysiology.

[301]  A. Gelman,et al.  The statistical crisis in science , 2014 .

[302]  G. Cumming,et al.  The New Statistics , 2014, Psychological science.

[303]  W. Boucsein Electrodermal activity, 2nd ed. , 2012 .

[304]  U. Ehlert,et al.  Psychoendokrinologie und Psychoimmunologie , 2011 .

[305]  Peter J Hellyer,et al.  Human brain mapping , 2012, Nature Methods.

[306]  Markus Ullsperger,et al.  Simultaneous EEG and fMRI , 2010 .

[307]  Daniel C. Alexander,et al.  Chapter 4 – Multiple Fibers: Beyond the Diffusion Tensor , 2009 .

[308]  W. D. Penny,et al.  Random-Effects Analysis , 2002 .

[309]  Andrew T. Duchowski,et al.  Eye Tracking Methodology - Theory and Practice, Third Edition , 2003 .

[310]  W. Penny,et al.  Random-Effects Analysis , 2002 .

[311]  D. Lykken,et al.  Habituation of the skin conductance response to strong stimuli: a twin study. , 1988, Psychophysiology.

[312]  M. Greicius,et al.  Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity , 2009, Brain Structure and Function.

[313]  A. Gregoriades,et al.  Assessing the reliability of socio‐technical systems , 2002 .