M. Lindquist

发表

Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years

Ann S. Choe, Craig K. Jones, J. Pekar, 2015, PloS one.

Ironing out the statistical wrinkles in “ten ironic rules”

Martin A. Lindquist, Ciprian M. Crainiceanu, Brian Caffo, 2013, NeuroImage.

Correlations and Multiple Comparisons in Functional Imaging: A Statistical Perspective (Commentary on Vul et al., 2009)

A. Gelman, M. Lindquist, 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.

Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask

Thomas E. Nichols, E. Vul, R. Poldrack, 2010, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation

Brent L. Hughes, Matthew L. Davidson, M. Lindquist, 2008, Neuron.

Building better biomarkers: brain models in translational neuroimaging

Luke J. Chang, M. Lindquist, T. Wager, 2017, Nature Neuroscience.

Meta-analysis of functional neuroimaging data: current and future directions.

M. Lindquist, T. Wager, Lauren Kaplan, 2007, Social cognitive and affective neuroscience.

The Statistical Analysis of fMRI Data.

M. Lindquist, 2008, 0906.3662.

Spin‐echo fMRI in humans using high spatial resolutions and high magnetic fields

K. Uğurbil, G. Adriany, Seong-Gi Kim, 2003, Magnetic resonance in medicine.

Fundamentals of functional neuroimaging

M. Lindquist, T. Wager, S. Geuter, 2016 .

Functional Magnetic Resonance Imagery, Analysis of

M. Lindquist, 2015 .

Essentials of Functional Neuroimaging

Martin A. Lindquist, Tor D. Wager, Luis Hernandez, 2009 .

36 52-4 3 Structural Magnetic Resonance Imaging

H. Ombao, J. Aston, M. Lindquist, 2019 .

Meta-Analyses in Functional Neuroimaging

M. Lindquist, T. Wager, 2015 .

Quantifying the reliability of image replication studies: The image intraclass correlation coefficient (I2C2)

M. B. Nebel, C. Crainiceanu, B. Caffo, 2013, Cognitive, affective & behavioral neuroscience.

Effect Size Estimation in Neuroimaging.

Marianne C. Reddan, M. Lindquist, T. Wager, 2017, JAMA psychiatry.

M. Lindquist, M. Irwin, S. Geuter, 2019, Pain.

Brain Mediators of Predictive Cue Effects on Perceived Pain

N. Bolger, M. Lindquist, T. Wager, 2010, The Journal of Neuroscience.

Dynamic Functional Connectivity States Reflecting Psychotic-like Experiences.

P. DeRosse, M. Lindquist, A. Barber, 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain

K. Davis, M. Lindquist, R. Bosma, 2018, Pain.

Neuroimaging-based biomarkers for pain: state of the field and current directions

M. Lindquist, T. Wager, Maite M. van der Miesen, 2019, Pain reports.

An fMRI-based neurologic signature of physical pain.

M. Lindquist, Ethan Kross, T. Wager, 2013, The New England journal of medicine.

Neural mechanisms of emotion regulation: Evidence for two independent prefrontal-subcortical pathways

Brent L. Hughes, Matthew L. Davidson, M. Lindquist, 2009 .

Resting brain activity in disorders of consciousness

B. Caffo, M. Lindquist, H. Sair, 2015, Neurology.

Explicit knowledge enhances motor vigor and performance: motivation versus practice in sequence tasks.

Aaron L. Wong, A. Haith, J. Krakauer, 2015, Journal of neurophysiology.

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

J. Pekar, B. Caffo, A. Choe, 2021, PloS one.

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

Ann S. Choe, J. Pekar, B. Caffo, 2020, bioRxiv.

A Survey of Statistics in the Neurological Sciences with a Focus on Human Neuroimaging

Amanda F. Mejia, B. Caffo, M. Lindquist, 2018, Wiley StatsRef: Statistics Reference Online.

Health Effects of Lesion Localization in Multiple Sclerosis: Spatial Registration and Confounding Adjustment

Peter A. Calabresi, Martin A. Lindquist, Russell T. Shinohara, 2014, PloS one.

RESIDUAL ANALYSIS FOR DETECTING MIS-MODELING IN fMRI

M. Lindquist, T. Wager, J. Loh, 2008 .

1 Principles of functional Magnetic Resonance Imaging

M. Lindquist, T. Wager, 2014 .

Imaging network level language recovery after left PCA stroke

A. Hillis, M. Lindquist, A. Faria, 2016, Restorative neurology and neuroscience.

Presurgical brain mapping of the language network in patients with brain tumors using resting‐state fMRI: Comparison with task fMRI

Martin A Lindquist, Ann S Choe, Raag D. Airan, 2016, Human brain mapping.

consciousness systematic review and meta-analysis

B. Caffo, M. Lindquist, H. Sair, 2015 .

Multiple Brain Networks Mediating Stimulus-Pain Relationships in Humans.

Leonie Koban, Martin A Lindquist, Anjali Krishnan, 2020, Cerebral cortex.

Statistical Research and Training Under the Brain Initiative

Genevera I. Allen, Thomas A. Nichols, J. Cunningham, 2014 .

DATA USING BAYESIAN NONPARAMETRIC BINARY REGRESSION

M. Lindquist, Y. Yue, J. Loh, 2011 .

A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes

M. Lindquist, M. Villani, A. Eklund, 2016, bioRxiv.

Adaptive spatial smoothing of fMRI images ( Running title : Adaptive spatial smoothing )

M. Lindquist, Y. Yue, J. Loh, 2009 .

Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol

David A. Williams, Richard E. Harris, B. Caffo, 2022, Frontiers in Medicine.

Connectivity in fMRI : A Review and a Preview

V. Solo, J. Poline, M. Lindquist, 2017 .

Supplemental Data Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation

Brent L. Hughes, Matthew L. Davidson, M. Lindquist, 2008 .

Acute lesions that impair affective empathy.

Johnny T. C. Hsu, C. Crainiceanu, A. Hillis, 2013, Brain : a journal of neurology.

FDG-PET analysis of amygdalar-cortical network covariance during pre- versus post-menopausal estrogen levels: potential relevance to resting state networks, mood, and cognition.

A. Fischman, M. Lindquist, D. Dougherty, 2008, Neuro endocrinology letters.

Essentials of Functional Neuroimaging

M. Lindquist, T. Wager, Luis Hernandez, 2009 .

Exposure‐based therapy changes amygdala and hippocampus resting‐state functional connectivity in patients with posttraumatic stress disorder

Martin A Lindquist, Tor D Wager, Xi Zhu, 2018, Depression and anxiety.

From CT to fMRI: Larry Shepp's Impact on Medical Imaging

M. Lindquist, 2016 .

Elements of Functional Neuroimaging 1 ELEMENTS OF FUNCTIONAL NEUROIMAGING

J. Jonides, M. Lindquist, T. Wager, 2007 .

Effects of Scan Length and Shrinkage on Reliability of Resting-State Functional Connectivity in the Human Connectome Project

Amanda F. Mejia, Ann S. Choe, M. B. Nebel, 2016, 1606.06284.

An fMRI-Based Neural Signature of Decisions to Smoke Cannabis

G. Bedi, M. Lindquist, M. Haney, 2015, Neuropsychopharmacology.

Altered cortical brain activity in end stage liver disease assessed by multi-channel near-infrared spectroscopy: Associations with delirium

M. Lindquist, C. Hogue, A. MacLullich, 2017, Scientific Reports.

Identification and characterization of genetic risk shared across 24 chronic pain conditions in the UK Biobank.

M. Lindquist, T. Wager, M. Keller, 2022, medRxiv.

Neural Correlates of Strategic Processes Underlying Episodic Memory in Women with Major Depression: A15O-PET Study

C. Savage, M. Lindquist, D. Dougherty, 2010 .

Fatigue induces long-lasting detrimental changes in motor-skill learning

P. Celnik, M. Lindquist, P. Kassavetis, 2018, bioRxiv.

Fatigue induces long lasting detrimental changes in motor skill learning

P. Celnik, M. Lindquist, P. Kassavetis, 2018, bioRxiv.

Detecting Model Mis-specification in fMRI using Scan Statistics

M. Lindquist, T. Wager, J. Loh, 2007 .

Enhanced hyperalignment via spatial prior information

M. Lindquist, L. Finos, A. Andreella, 2022, Human brain mapping.

Improved fMRI-based pain prediction using Bayesian group-wise functional registration.

M. Lindquist, A. Datta, Guoqing Wang, 2022, Biostatistics.

A machine learning based approach towards high-dimensional mediation analysis

B. Caffo, M. Lindquist, T. Wager, 2022, NeuroImage.

Sequential fear generalization and network connectivity in trauma exposed humans with and without psychopathology

M. Lindquist, T. Wager, S. Lissek, 2022, Communications Biology.

Detecting functional connectivity change points for single-subject fMRI data

Martin A. Lindquist, Tor D. Wager, Ivor Cribben, 2013, Front. Comput. Neurosci..

Modeling state-related fMRI activity using change-point theory

Martin A. Lindquist, Tor D. Wager, Christian E. Waugh, 2007, NeuroImage.

Response variability of different anodal transcranial direct current stimulation intensities across multiple sessions

Martin A. Lindquist, Claudia Ammann, Pablo A. Celnik, 2017, Brain Stimulation.

Supplemental Data Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation

Brent L. Hughes, Matthew L. Davidson, M. Lindquist, 2008 .

Investigating the impact of autocorrelation on time-varying connectivity

Martin A. Lindquist, James J. Pekar, Hamed Honari, 2019, NeuroImage.

Modular preprocessing pipelines can reintroduce artifacts into fMRI data

Brian S Caffo, Martin A Lindquist, Stephan Geuter, 2018, bioRxiv.

Comparing test-retest reliability of dynamic functional connectivity methods

Brian Caffo, Ann S. Choe, Yuting Xu, 2017, NeuroImage.

Group-regularized individual prediction: theory and application to pain

Martin A. Lindquist, Leonie Koban, Luke J. Chang, 2017, NeuroImage.

A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies

Martin A. Lindquist, David Degras, M. Lindquist, 2014, NeuroImage.

Dissociable Influences of Opiates and Expectations on Pain

Martin A Lindquist, Tor D Wager, M. Lindquist, 2012, The Journal of Neuroscience.

Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression

Martin A. Lindquist, Yu Ryan Yue, Ji Meng Loh, 2012, 1206.6674.

Rapid three-dimensional functional magnetic resonance imaging of the initial negative BOLD response.

Martin A Lindquist, Cun-Hui Zhang, Gary Glover, 2008, Journal of magnetic resonance.

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Vince D. Calhoun, Martin A. Lindquist, Adeel Razi, 2020, Network Neuroscience.

Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach

Martin A. Lindquist, Mary Beth Nebel, Brian Caffo, 2014, NeuroImage.

Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data

Martin A. Lindquist, Yuting Xu, M. Lindquist, 2015, Front. Neurosci..

Effect Size and Power in fMRI Group Analysis

Martin A. Lindquist, Stephan Geuter, Tor D. Wager, 2018, bioRxiv.

Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat

Martin A Lindquist, Michael E Sobel, M. Sobel, 2014, Journal of the American Statistical Association.

A Bayesian General Linear Modeling Approach to Slice-wise fMRI Data Analysis

Martin A. Lindquist, David Bolin, Finn Lindgren, 2014 .

Spatial smoothing in fMRI using prolate spheroidal wave functions

Martin A Lindquist, Tor D Wager, M. Lindquist, 2008, Human brain mapping.

THE ACQUISITION AND STATISTICAL ANALYSIS OF RAPID 3D FMRI DATA

Gary H. Glover, Martin A. Lindquist, Cun-Hui Zhang, 2008 .

Logistic Regression With Brownian-Like Predictors

Martin A. Lindquist, Ian W. McKeague, I. McKeague, 2009 .

Automatic Identification of Twin Zygosity in Resting-State Functional MRI

Moo K. Chung, Martin A. Lindquist, Andrey Gritsenko, 2018, 1807.00244.

Hill Climbing Optimized Twin Classification Using Resting-State Functional MRI pdf

Moo K. Chung, Martin A. Lindquist, Andrey Gritsenko, 2018, ArXiv.

Generalizability of Neuroimaging Studies in 5 Common Psychiatric Disorders Based on the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).

M. Lindquist, M. Olfson, C. Blanco, 2016, The Journal of clinical psychiatry.

Functional Causal Mediation Analysis With an Application to Brain Connectivity

Martin A. Lindquist, M. Lindquist, 2012, Journal of the American Statistical Association.

Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control

Jessica R. Cohen, M. B. Nebel, J. Pekar, 2021, Developmental Cognitive Neuroscience.

Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis

Jessica R. Cohen, J. Pekar, M. Lindquist, 2021, NeuroImage.

fslr: Connecting the FSL Software with R

John Muschelli, Ciprian Crainiceanu, Martin Lindquist, 2015, R J..

Adaptive spatial smoothing of fMRI images

Martin A. Lindquist, Ji Meng Loh, M. Lindquist, 2010 .

ESTIMATING CAUSAL EFFECTS IN STUDIES OF HUMAN BRAIN FUNCTION: NEW MODELS, METHODS AND ESTIMANDS.

Martin A. Lindquist, Michael E. Sobel, M. Sobel, 2020, The annals of applied statistics.

Handbook of Neuroimaging Data Analysis

Martin A. Lindquist, Hernando Ombao, Wesley K. Thompson, 2016 .

Evaluating the consistency and specificity of neuroimaging data using meta-analysis

Martin A. Lindquist, Thomas E. Nichols, Hedy Kober, 2009, NeuroImage.

Dynamic connectivity regression: Determining state-related changes in brain connectivity

Martin A. Lindquist, Tor D. Wager, Ivor Cribben, 2012, NeuroImage.

Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks.

M. Lindquist, A. Barber, K. Karlsgodt, 2021, Cerebral cortex.

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

Martin A. Lindquist, Brian Caffo, James J. Pekar, 2019, NeuroImage.

Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data

Brian Caffo, Xi Luo, Martin Lindquist, 2018, 1805.06923.

Connectivity in fMRI: Blind Spots and Breakthroughs

Victor Solo, Moo K. Chung, Martin A. Lindquist, 2018, IEEE Transactions on Medical Imaging.

Differences in functional connectivity distribution after transcranial direct‐current stimulation: A connectivity density point of view

Martin A Lindquist, Archana Venkataraman, Brian Caffo, 2020, bioRxiv.

Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling

Martin A. Lindquist, Tor D. Wager, Ji Meng Loh, 2009, NeuroImage.

Rethinking interhemispheric imbalance as a target for stroke neurorehabilitation

Heidi M. Schambra, J. Krakauer, P. Celnik, 2019, Annals of neurology.

Brain mediators of cardiovascular responses to social threat Part I: Reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity

Martin A. Lindquist, Douglas C. Noll, Stephan F. Taylor, 2009, NeuroImage.

Brain mediators of cardiovascular responses to social threat, Part II: Prefrontal-subcortical pathways and relationship with anxiety

Martin A. Lindquist, Kevin N. Ochsner, Tor D. Wager, 2009, NeuroImage.

On statistical tests of functional connectome fingerprinting

Martin A. Lindquist, Brian S. Caffo, Susan M. Resnick, 2018, bioRxiv.

PLUS DISEASE IN RETINOPATHY OF PREMATURITY: Diagnostic Impact of Field of View

Martin A Lindquist, Michael F Chiang, Rony Gelman, 2011, Retina.

An M-estimator for reduced-rank system identification

Kai Liu, Martin A. Lindquist, Seonjoo Lee, 2016, Pattern Recognit. Lett..

An M-Estimator for Reduced-Rank High-Dimensional Linear Dynamical System Identification

Brian S. Caffo, Seonjoo Lee, Joshua T. Vogelstein, 2015, 1509.03927.

Detection of time-varying signals in event-related fMRI designs

Martin A. Lindquist, Vincent P. Ferrera, Tor D. Wager, 2008, NeuroImage.

Discriminating Neural Representations of Physical and Social Pains: How Multivariate Statistics Challenge the 'shared Representation' Theory of Pain Rogachov a Hanna Jr, and Wager Td. Separate Neural Representations for Physical Pain and Social Rejection

Ethan Kross, Leonie Koban, Martin A Lindquist, 2022 .

Altered resting state functional connectivity of fear and reward circuitry in comorbid PTSD and major depression

J. Markowitz, M. Lindquist, F. Schneier, 2017, Depression and anxiety.

Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials

Heidi M. Schambra, J. Krakauer, P. Celnik, 2019, Neurorehabilitation and neural repair.

Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI

Martin A. Lindquist, Ciprian M. Crainiceanu, Amanda F. Mejia, 2014, NeuroImage.

Emerging Shifts in Neuroimaging Data Analysis in the Era of “Big Data”

Martin A. Lindquist, Danilo Bzdok, Marc Andre Schulz, 2019, Personalized Psychiatry.

Quantifying the reliability of image replication studies: The image intraclass correlation coefficient (I2C2)

M. B. Nebel, C. Crainiceanu, B. Caffo, 2013, Cognitive, Affective, & Behavioral Neuroscience.

A human colliculus-pulvinar-amygdala pathway encodes negative emotion

L. F. Barrett, M. Lindquist, T. Wager, 2021, Neuron.

BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.

Martin A. Lindquist, Abhirup Datta, Guoqing Wang, 2021, The annals of applied statistics.

Estimating and testing variance components in a multi-level GLM

Martin A. Lindquist, Tor D. Wager, Julie Spicer, 2012, NeuroImage.

Using network parcels and resting state networks to estimate correlates of mood disorder and related research domain criteria constructs of reward responsiveness and inhibitory control.

Lisanne M. Jenkins, M. Paulus, S. Langenecker, 2021, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Neural changes in extinction recall following prolonged exposure treatment for PTSD: A longitudinal fMRI study☆

J. Markowitz, M. Lindquist, F. Schneier, 2016, NeuroImage: Clinical.

Quantifying cerebral contributions to pain beyond nociception

Martin A Lindquist, Anjali Krishnan, Tor D Wager, 2017, Nature Communications.

Sparse principal component based high-dimensional mediation analysis

Martin A. Lindquist, Brian S. Caffo, Yi Zhao, 2018, Comput. Stat. Data Anal..

High-dimensional multivariate mediation with application to neuroimaging data.

Elizabeth L. Ogburn, Brian S Caffo, Martin A Lindquist, 2015, Biostatistics.

Big Data and Neuroimaging

Martin A. Lindquist, Amanda F. Mejia, Shaojie Chen, 2017, Statistics in biosciences.

Methodological considerations in analyzing synchronization of resting-state brain networks with the intrinsic electrical rhythm of the stomach: Advantages of weighted phase-locking

Ann S. Choe, J. Pekar, B. Caffo, 2021, bioRxiv.

Evaluation of prefrontal–hippocampal effective connectivity following 24 hours of estrogen infusion: An FDG-PET study

Karen L. Siedlecki, A. Fischman, M. Lindquist, 2008, Psychoneuroendocrinology.

A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

Martin A. Lindquist, David Bolin, Amanda F. Mejia, 2017, Journal of the American Statistical Association.

The Pain of Sleep Loss: A Brain Characterization in Humans

M. Walker, M. Lindquist, T. Wager, 2019, The Journal of Neuroscience.

Graphical models, potential outcomes and causal inference: Comment on Ramsey, Spirtes and Glymour

Martin A. Lindquist, Michael E. Sobel, M. Sobel, 2011, NeuroImage.

Brain mediators of the effects of noxious heat on pain

Martin A. Lindquist, Tor D. Wager, Niall Bolger, 2014, PAIN®.

Big Data and Neuroimaging

Amanda F. Mejia, Aaron J. Fisher, B. Caffo, 2017, Statistics in Biosciences.

Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging

Martin A Lindquist, Ann S Choe, Brian Caffo, 2017, Brain Connect..

Parallel group independent component analysis for massive fMRI data sets

Mary Beth Nebel, Brian S Caffo, Martin A Lindquist, 2017, PloS one.

PCA leverage: outlier detection for high‐dimensional functional magnetic resonance imaging data

Amanda F. Mejia, Mary Beth Nebel, Martin A Lindquist, 2015, Biostatistics.

Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators

Martin A. Lindquist, Ciprian M. Crainiceanu, Amanda F. Mejia, 2014, NeuroImage.

Analysis of prefrontal-hippocampal effective connectivity during estrogen infusion: An FDG-PET study

A. Fischman, M. Lindquist, D. Dougherty, 2008 .

Relationship between TSH levels and cognition in the young adult: An analysis of the Human Connectome Project Data.

M. Lindquist, D. Cooper, P. Santhanam, 2022, Journal of Clinical Endocrinology and Metabolism.

Handbook of Psychophysiology: Elements of Functional Neuroimaging

Martin A. Lindquist, Tor D. Wager, John Jonides, 2007 .

Principles of Functional Magnetic Resonance Imaging

Martin A. Lindquist, Tor D. Wager, M. Lindquist, 2016 .

Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions

Martin A. Lindquist, Kathleen M. Gates, Peter C. M. Molenaar, 2021, Brain Connect..

Multiple brain networks mediating stimulus-pain relationships in humans

Martin A. Lindquist, Leonie Koban, Anjali Krishnan, 2018, bioRxiv.

The benefits of rapid 3D fMRI

Martin A. Lindquist, M. Lindquist, 2010, Int. J. Imaging Syst. Technol..

A generalization of the two-dimensional prolate spheroidal wave function method for nonrectilinear MRI data acquisition methods

Gary H. Glover, Martin A. Lindquist, Larry A. Shepp, 2006, IEEE Transactions on Image Processing.

Neuroimaging results altered by varying analysis pipelines

Martin Lindquist, M. Lindquist, 2020, Nature.

Slow-5 dynamic functional connectivity reflects the capacity to sustain cognitive performance during pain

J.C. Cheng, R.L. Bosma, K.S. Hemington, 2017, NeuroImage.

Single-index models with functional connectivity network predictors.

Luo Xiao, M. Lindquist, Caleb Weaver, 2021, Biostatistics.

Zen and the Art of Multiple Comparisons

Amanda F. Mejia, Martin A Lindquist, Amanda Mejia, 2015, Psychosomatic medicine.

Moderated t-tests for group-level fMRI analysis

Martin A. Lindquist, John Muschelli, Guoqing Wang, 2021, NeuroImage.

A dorsomedial prefrontal cortex-based dynamic functional connectivity model of rumination

J. Andrews-Hanna, M. Lindquist, T. Wager, 2023, Nature communications.

Change point estimation in multi-subject fMRI studies

Martin A. Lindquist, Tor D. Wager, Lucy F. Robinson, 2010, NeuroImage.

Modeling Brain Pathways using Functional Mediation Analysis

M. Lindquist, L. Atlas, T. Wager, 2009, NeuroImage.

Cloak and DAG: A response to the comments on our comment

Martin A. Lindquist, Michael E. Sobel, M. Sobel, 2013, NeuroImage.

Genetic risk shared across 24 chronic pain conditions: identification and characterization with genomic structural equation modeling.

M. Lindquist, T. Wager, M. Keller, 2023, Pain.

Dynamic Functional Brain Connectivity Underlying Temporal Summation of Pain in Fibromyalgia

V. Napadow, M. Lindquist, R. Edwards, 2021, Arthritis & rheumatology.

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

O. Fiehn, A. Kahn, B. Caffo, 2023, Pain.

resting state

M. Lindquist, Ivor Cribben, B. Cornils, 2020, Catalysis from A to Z.

Validity and power in hemodynamic response modeling: A comparison study and a new approach

M. Lindquist, T. Wager, 2007, Human brain mapping.

Twin Classification in Resting-State Brain Connectivity

Moo K. Chung, Andrey Gritsenko, Martin Lindquist, 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

Three Dimensions of Association Link Migraine Symptoms and Functional Connectivity

M. Lindquist, D. Seminowicz, J. Haythornthwaite, 2021, The Journal of Neuroscience.

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage

Martin A. Lindquist, Amanda F. Mejia, Mary Beth Nebel, 2018, NeuroImage.

Handbook of Psychophysiology: Elements of Functional Neuroimaging

J. Jonides, M. Lindquist, T. Wager, 2007 .

Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI

V D Calhoun, B. Caffo, M. Lindquist, 2017, American Journal of Neuroradiology.

Circular Pearson Correlation Using Cosine Series Expansion

Moo K. Chung, Martin A. Lindquist, Shih-Gu Huang, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Regression models for partially localized fMRI connectivity analyses

B. Caffo, M. Lindquist, Bonnie Smith, 2023, bioRxiv.

Graph-constrained Analysis for Multivariate Functional Data

M. Lindquist, A. Datta, S. Banerjee, 2022, 2209.06294.

Modular preprocessing pipelines can reintroduce artifacts into fMRI data

B. Caffo, M. Lindquist, T. Wager, 2018, bioRxiv.

Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation

Brent L. Hughes, Matthew L. Davidson, M. Lindquist, 2008, Neuron.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

M. Lindquist, T. Wager, N. Friedman, 2023, bioRxiv.

Brain mediators of negative affect-induced physical symptom reporting in patients with functional somatic syndromes

O. Van den Bergh, I. Van Diest, M. Lindquist, 2023, Translational psychiatry.

Graphical models, potential outcomes and causal inference: Comment on Ramsey, Spirtes and Glymour

M. Sobel, M. Lindquist, 2011, NeuroImage.

Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis

Martin A. Lindquist, James J. Pekar, Jessica R. Cohen, 2021, NeuroImage.

Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling

M. Lindquist, T. Wager, J. Loh, 2009, NeuroImage.

A Bayesian heteroscedastic GLM with application to fMRI data with motion spikes

Martin A. Lindquist, Anders Eklund, Mattias Villani, 2016, NeuroImage.

Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach

M. B. Nebel, B. Caffo, M. Lindquist, 2014, NeuroImage.

A tensor based varying-coefficient model for multi-modal neuroimaging data analysis

Pratim Guha Niyogi, T. Maiti, M. Lindquist, 2023, 2303.16443.

Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.

Luo Xiao, Ciprian M Crainiceanu, Martin A Lindquist, 2016, Biostatistics.

A functional mixed model for scalar on function regression with application to a functional MRI study.

Luo Xiao, Martin A Lindquist, Bowen Liu, 2019, Biostatistics.

Regression models for partially localized fMRI connectivity analyses

B. Caffo, M. Lindquist, Bonnie Smith, 2023, Frontiers in neuroimaging.

Quantifying the Association between Objectively Measured Physical Activity and Multiple Sclerosis in the UK Biobank

C. Crainiceanu, M. Lindquist, E. Mowry, 2023, Medicine and science in sports and exercise.

Demonstration of Brain Tumor-Induced Neurovascular Uncoupling in Resting-State fMRI at Ultrahigh Field

Martin A. Lindquist, Hye-Young Heo, James J. Pekar, 2016, Brain Connect..

The Pain of Sleep Loss: A Brain Characterization in Humans

M. Walker, M. Lindquist, T. Wager, 2019, The Journal of Neuroscience.

Assessing uncertainty in dynamic functional connectivity

Martin A. Lindquist, Jaroslaw Harezlak, Maria Kudela, 2017, NeuroImage.

Association between Neighborhood Socioeconomic Status and Executive System Activation in Youth

Adam R. Pines, R. Gur, R. Gur, 2021, bioRxiv.

Correlations and Multiple Comparisons in Functional Imaging: A Statistical Perspective (Commentary on Vul et al., 2009)

A. Gelman, M. Lindquist, 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.