Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

Advances in functional and structural MR image analysis and implementation as FSL

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630

Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888

Fast robust automated brain extraction

Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496

The human brain is intrinsically organized into dynamic, anticorrelated functional networks

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The fusiform face area: a module in human extrastriate cortex specialized for face perception

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Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628

Fast and robust fixed-point algorithms for independent component analysis

Hyvärinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,304

Untitled work

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Movement‐Related effects in fMRI time‐series

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Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI

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Chronic Pain Patients Exhibit Individually Unique Cortical Signatures of PainPreprint

Mayr, A., Jahn, P., Stankewitz, A., Deak, B., Winkler, A. M., Witkovský, V., Eren, O. E., Straube, A. M., & Schulz, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.05.284117 · Cited by 2

Identification and Automatic ICA-PSO Two-Class Classification of Time Series RSN in Shaky Hand Syndrome

Thaiyalnayaki, S., & Maheswari, O. U. (2016) · Circuits and Systems · doi:10.4236/cs.2016.78161 · Cited by 1

Template-guided Functional Network Identification via Supervised Dictionary Learning

Zhao, Y., Li, X., Makkie, M., Quinn, S. P., Lin, B., Ye, J., & Liu, T. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950471 · Cited by 1

Louvain clustering integration within density-based graph classification (Louvain dbGC) in Schizophrenia

Abdulla, M., & Khasawneh, M. T. (2021) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2021.1933268 · Cited by 1

Conscious but not thinking —Mind‐blanks during visuomotor tracking: An fMRI study of endogenous attention lapses

Zaky, M. H., Shoorangiz, R., Poudel, G. R., Yang, L., Innes, C., & Jones, R. D. (2024) · Human Brain Mapping · doi:10.1002/hbm.26781

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

Mehta, U. M., Roy, N., Bahuguna, A., Kotambail, A., Arunachal, G., Venkatasubramanian, G., & Thirthalli, J. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116201 · Cited by 1

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates

Steele, J., Bush, K., Stowe, Z. N., James, G. A., Smitherman, S., Kilts, C., & Cisler, J. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192318 · Cited by 1

Neural Substrates of Phonological Processing in Chronic Aphasia from Stroke

DeMarco, A. T. (2016) · UA Campus Repository (The University of Arizona) · Cited by 1

The experience of vivid autobiographical reminiscence is supported by personal semantic representations in the precuneusPreprint

Sreekumar, V., Nielson, D. M., Smith, T. A., Dennis, S. J., & Sederberg, P. B. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/197665 · Cited by 1

Functional motor disorders: mechanism, prognosis and treatment

Gelauff, J. (2020) · doi:10.33612/diss.136733110 · Cited by 2

Imaging the effects of tumour necrosis factor inhibition on pain processing in severe rheumatoid arthritisPreprint

Wartolowska, K. A., Jenkinson, M., Andersson, J., Wordsworth, B. P., & Tracey, I. (2023) · medRxiv · doi:10.1101/2023.02.27.23286245 · Cited by 1

Individual differences in intrinsic connectivity Networks I: retest and validation in resting state.

Wisner, K. (2012) · University of Minnesota Digital Conservancy (University of Minnesota)

Movimento ocular em crianças e adultos: estudo comparativo com uso integrado de RMf e Eye Tracking

Lukášová, K. (2012) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2012.tde-07022013-104141

Studying the spatial distribution of physiological effects on BOLD signals using ultrafast fMRI

Margulies, D. S., Planck, M., & Schaefer, A. (2014)

Toward Clinical Application of Resting-State Functional Magnetic Resonance Imaging to Dementia

Ogata, Y., & Hanakawa, T. (2017) · Springer eBooks · doi:10.1007/978-4-431-55133-1_8

Movie-driven fMRI Reveals Network Asynchrony and Connectivity Alterations in Temporal Lobe Epilepsy

Bullen, A. (2017) · Scholarship@Western (Western University)

Optimizing Correction of Motion and Physiological Artifact in Clinical fMRI

Churchill, N. W. (2013) · TSpace (University of Toronto)

fMRI: preprocessing, classification and pattern recognitionPreprint

Sharaev, M., Andreev, A., Artemov, A., Bernstein, A., Burnaev, E. V., Kondratyeva, E. A., Sushchinskaya, S., & Akzhigitov, R. G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.10167

Opioids for breathlessness: Psychological and neural factors influencing response variabilityPreprint

Abdallah, S. J., Harrison, O. K., Wanigasekera, V., Finnegan, S., Jensen, D., & Pattinson, K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/344523

Brief Overview of Functional Imaging Principles

Habas, C., & de Marco, G. (2018) · Contemporary clinical neuroscience · doi:10.1007/978-3-319-78926-2_2

Identifying neural underpinnings of schizophrenia-specific disruptions in cognition using magnetic resonance imaging, individual differences, and genetics

Rodrigue, A. (2017)

Deep learning frameworks for functional and structural medical image analysis

Zhao, Y. (2018)

Investigating the Neural Networks Involved in Externalizing and Conscientious Behavior

Rueter, A. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)

Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major Depression

Hu, D., & Zeng, L. (2019) · Springer eBooks · doi:10.1007/978-981-32-9523-0_2

Functional and Structural Connectivity of Limbic and Interpersonally Relevant Regions in Non-Suicidal Self-Injury

Schreiner, M. W. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)

Structure-Function Relationships in the Brain: Applications in Neurosurgery

Pur, D. R. (2020) · Scholarship@Western (Western University)

Structural and Functional Brain Markers of Trauma-related Symptoms

Blessington, G. (2020) · CUNY Academic Works (City University of New York)

Fully automated ordering and labeling of ICA components

Salman, M. S., Wager, T. D., Damaraju, E., Abrol, A., & Calhoun, V. D. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313309

Neural dynamics of parametrically modulated human mechanical pain determined using whole-brain quantitative perfusion imagingPreprint

Segerdahl, A. R., Kong, Y., Ho, I., & Tracey, I. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.25.436356

The intrinsic spatiotemporal organization of the human brain - A multi-dimensional functional network atlasPreprint

Li, J., Liu, Y., Wisnowski, J. L., & Leahy, R. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.09.472035

Faculty Opinions recommendation of Resting state functional connectivity of networks associated with reward and habit in anorexia nervosa.

Frank, G. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734076201.793558792

Simultaneous EEG-fMRI using pulse sequences with EPI and Spiral K-Space trajectories: Clinical application in Fixation-off epilepsy

Solana, A. B. (2014) · doi:10.20868/upm.thesis.32659

Dynamic Resting-State Functional Connectivity in Migraineurs

Imai, N. (2022) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2204143

Evaluating brain modularity benefits of an acting intervention: a discriminant-analysis framework

Rajesh, A., Betzel, R. F., Daugherty, A. M., Noice, T., Noice, H., Baniqued, P. L., Voss, M. W., & Kramer, A. F. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1114804

Preprocessing for fMRI (and a little bit for diffusion MRI)

Andersson, J., & Lange, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00117-0

Creating A dynamic cognovisor – Brain activity recognition using principal Component analysis and Machine learning models

Gadzhiev, I. M., Makarov, A. S., Ushakov, V., Orlov, V. A., Ivanitsky, G., & Dolenko, S. A. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101314

Exploring Brain Imaging Analysis With Nilearn and Related Python Packages

Uludağ, K. (2025) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-3840-7.ch007

Motor priming induces changes in resting state dynamics in cerebellum of writer’s cramp dystonia: insights for clinical therapies

Mulcahey, P. J., Fei, M., Voyvodic, J. T., Lutz, M. W., & Bukhari‐Parlakturk, N. (2025) · Dystonia · doi:10.3389/dyst.2025.14533

Improving presurgical language mapping by a method for optimally sorting independent components of resting-state fMRI

Vale, B., Duarte, D., Vigário, R., Benjamin, C., Vilela, P. F., Lauterbach, M. H., & Andrade, A. (2025) · Brain Imaging and Behavior · doi:10.1007/s11682-025-01058-x

Chronicity moderates the impact of severity on central executive-default mode network functional interactions in depression

Zanao, T. A., Salvan, P., Razza, L. B., da Silva, P. H. R., Brunoni, A. R., & O’Shea, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-40364-2

The Default Mode Network and Behavior: a Model to Analyse Psycho-Physiological Interactions in Resting State fMRI

Sanz‐Morales, E., & Melero, H. (2026) · Brain Topography · doi:10.1007/s10548-026-01223-5

Keep Making That Face and It Will Stay That Way: Cross-Cohort Prediction of Empathy from Task-Relevant Connectivity in Task-Free Data

Reggente, N., Lulla, R., Durinski, T., Kaplan, J., Iacoboni, M., & Christov‐Moore, L. (2026) · Neuroimaging · doi:10.3390/neuroimaging1030011

An Exploration of Real-Time Functional Magnetic Resonance Imaging Neurofeedback in Cognition

Dewiputri, W. I. (2014) · doi:10.53846/goediss-4752

Automatic independent component labeling for artifact removal in fMRI

Tohka, J., Foerde, K., Aron, A. R., Tom, S. M., Toga, A. W., & Poldrack, R. A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.013 · Cited by 217

Denoising of functional MRI using ICA

Yang, K., & Rajapakse, J. C. (2004) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa.2003.1296959 · Cited by 3

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

Bhaganagarapu, K., Jackson, G. D., & Abbott, D. F. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00343 · Cited by 62

Denoising the speaking brain: Toward a robust technique for correcting artifact-contaminated fMRI data under severe motion

Xu, Y., Tong, Y., Liu, S., Chow, H. M., AbdulSabur, N. Y., Mattay, G. S., & Braun, A. R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.013 · Cited by 41

Reduction of Motion Artifacts and Noise Using Independent Component Analysis in Task-Based Functional MRI for Preoperative Planning in Patients with Brain Tumor

Middlebrooks, E. H., Frost, C. J., Tuna, İ. S., Schmalfuss, I. M., Rahman, M., & Crow, A. (2016) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4996 · Cited by 14