Cited by 407Medical Image Segmentation TechniquesAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

The DOI matches its Crossref record; 33 of the 37 references it cites were found in a registry or an index.

A new method for improving functional-to-structural MRI alignment using local Pearson correlation

Open access

Saad, Ziad S., Glen, Daniel R., Chen, Gang, Beauchamp, Michael S., Desai, Rutvik, Cox, Robert W.·2009·NeuroImage

DOI 10.1016/j.neuroimage.2008.09.037PMC PMC2649831

Open this paper in CoChat — free account

Abstract

Accurate registration of Functional Magnetic Resonance Imaging (FMRI) T2-weighted volumes to same-subject high-resolution T1-weighted structural volumes is important for Blood Oxygenation Level Dependent (BOLD) FMRI and crucial for applications such as cortical surface-based analyses and pre-surgical planning. Such registration is generally implemented by minimizing a cost functional, which measures the mismatch between two image volumes over the group of proper affine transformations. Widely used cost functionals, such as mutual information (MI) and correlation ratio (CR), appear to yield decent alignments when visually judged by matching outer brain contours. However, close inspection reveals that internal brain structures are often significantly misaligned. Poor registration is most evident in the ventricles and sulcal folds, where CSF is concentrated. This observation motivated our development of an improved modality-specific cost functional which uses a weighted local Pearson coefficient (LPC) to align T2- and T1-weighted images. In the absence of an alignment gold standard, we used three human observers blinded to registration method to provide an independent assessment of the quality of the registration for each cost functional. We found that LPC performed significantly better (p<0.001) than generic cost functionals including MI and CR. Generic cost functionals were very often not minimal near the best alignment, thereby suggesting that optimization is not the cause of their failure. Lastly, we emphasize the importance of precise visual inspection of alignment quality and present an automated method for generating composite images that help capture errors of misalignment.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2008.09.037

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Self-citation
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Untitled work
(1932) · Medical Entomology and Zoology · Cited by 9,568
Categorical Data Analysis
Hapeman, D. E., & Agresti, A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,407
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 1×
“The use of crosscorrelation is common as a measure of matching in image processing and in MRI in particular (Collins et al., 1994); however, the LPC approach differs by its use of the weighting function and of localized estimates that are later combined nonlinearly.”
Background
An overlap invariant entropy measure of 3D medical image alignment
Studholme, C., Hill, D., & Hawkes, D. J. (1999) · Pattern Recognition · doi:10.1016/s0031-3203(98)00091-0 · Cited by 2,167
Multi-modal volume registration by maximization of mutual information
Wells, W. M., Viola, P. A., Atsumi, H., Nakajima, S., & Kikinis, R. (1996) · Medical Image Analysis · doi:10.1016/s1361-8415(01)80004-9 · Cited by 1,961

Show all 37 references

Research works printed but not linked: 1

Taneja, 2005, Refinement inequalities among symmetric divergence measures, Aust. J. Math. Anal. Appl., 2, 1

Sorted by citations

Items 301–400 of 407

Brain regions supporting retrieval of words drawn at encoding: fMRI evidence for multimodal reactivation
Roberts, B. R. T., Meade, M. E., & Fernandes, M. A. (2024) · Memory & Cognition · doi:10.3758/s13421-024-01591-y · Cited by 6
Individual Differences in Visual versus Semantic Neural Reactivation: Evidence from Severely Deficient Autobiographical Memory
Bone, M. B., Levine, B., & Buchsbaum, B. R. (2025) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02317 · Cited by 4
Oxygen-enhanced MRI in temporal lobe epilepsy: Diagnosis and lateralization
Kalamangalam, G. P., Nelson, J. T., Ellmore, T. M., & Narayana, P. A. (2011) · Epilepsy Research · doi:10.1016/j.eplepsyres.2011.08.014 · Cited by 5
Cost function evaluation for the registration of clinical DTI images onto the ICBM DTI81 white matter atlas
Karmonik, C., Dulay, M. F., Verma, A. K., & Grossman, R. G. (2010) · Technology and Health Care · doi:10.3233/thc-2010-0578 · Cited by 5
Dynamic Time Warping Identifies Functionally Distinct fMRI Resting State Cortical Networks Specific to VTA and SNc: A Proof of Concept
Philips, R. T., Torrisi, S. J., Gorka, A. X., Grillon, C., & Monique Ernst, D. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab273 · Cited by 5
Real-time Online Prediction of Data Driven Bearing Residual Life
Wang, H., Yu, Z., & Guo, L. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1437/1/012025 · Cited by 5
Sex hormones partially explain the sex-dependent effect of lifetime alcohol use on adolescent white matter microstructure
Jones, S. A., Kliamovich, D., & Nagel, B. J. (2020) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2020.111230 · Cited by 5
Rigid-Body Registration
Tohka, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00299-2 · Cited by 5
A novel framework for groupwise registration of fMRI images based on common functional networks
Zhao, Y., Zhang, S., Chen, H., Zhang, W., Lv, J., Jiang, X., Shen, D. G., & Liu, T. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950566 · Cited by 5
Defining region-specific masks for reliable depth-dependent analysis of fMRI dataPreprint
Weldon, K. B., Burton, P. C., Grant, A., Yacoub, E. S., & Olman, C. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/557363 · Cited by 5
On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study
Damiani, S., La‐Torraca‐Vittori, P., Tarchi, L., Tosi, E., Ricca, V., Scalabrini, A., Politi, P., & Fusar‐Poli, P. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120585 · Cited by 4
Characterization of brain network supporting episodic memory in the absence of one medial temporal lobe
Jeong, W., Lee, H., Kim, J. S., & Chung, C. K. (2019) · Human Brain Mapping · doi:10.1002/hbm.24516 · Cited by 5
Imaging Somatosensory Cortex: Human Functional Magnetic Resonance Imaging (fMRI)
Puckett, A. M., & Panchuelo, R. M. S. (2023) · Neuromethods · doi:10.1007/978-1-0716-3068-6_18 · Cited by 6
Lessons learned from an fMRI-guided rTMS study on performance in a numerical Stroop task
Beynel, L., Gura, H., Rezaee, Z., Ekpo, E. C., Deng, Z., Joseph, J. O., Taylor, P. A., Luber, B. M., & Lisanby, S. H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0302660 · Cited by 4
Examining putamen resting-state connectivity markers of suicide attempt history in depressed adolescents
Tymofiyeva, O., Ho, T. C., Connolly, C. G., Gorrell, S., Rampersaud, R., Darrow, S. M., Max, J. E., & Yang, T. T. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1364271 · Cited by 4
A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging
Kwon, O.-H., Park, H., Seo, S.-W., Na, D. L., & Lee, J. M. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00236 · Cited by 5
Regions of Mid-level Human Visual Cortex Sensitive to the Global Coherence of Local Image Patches
Mannion, D. J., Kersten, D., & Olman, C. A. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00588 · Cited by 4
Elaborative Feedback: Engaging reward and task-relevant brain regions promotes learning in pseudoword reading aloud
Mattheiss, S. R., Alexander, E. J., & Graves, W. W. (2017) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-017-0553-5 · Cited by 8
The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO)
Tarchi, L., Damiani, S., La‐Torraca‐Vittori, P., Marini, S., Nazzicari, N., Castellini, G., Pisano, T., Politi, P., & Ricca, V. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00584-8 · Cited by 4
Functional, but minimal microstructural brain changes present in aging Canadian football league players years after retirement
Danielli, E., Simard, N., Sharma, B., Doughty, M., & Noseworthy, M. D. (2022) · Brain Disorders · doi:10.1016/j.dscb.2022.100036 · Cited by 4
ERNet
Su, Y., Qian, Z., He, L., & Kong, X. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539227 · Cited by 4
SAMCOR: A robust and precise co-registration algorithm for brain CT and MR imaging
Kadipasaoglu, C. M., Morse, C. W., Pham, K., Donos, C., & Tandon, N. (2022) · Interdisciplinary Neurosurgery · doi:10.1016/j.inat.2022.101637 · Cited by 4
Amphetamine alters neural response to sucrose in healthy women
Melrose, A. J., Bailer, U. F., Wierenga, C. E., Bischoff‐Grethe, A., Paulus, M. P., & Kaye, W. H. (2016) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2016.04.017 · Cited by 4
Human visual cortical responses to specular and matte motion flows
Kam, T., Mannion, D. J., Lee, S., Doerschner, K., & Kersten, D. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00579 · Cited by 4
Fast Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI
Almodóvar‐Rivera, I., & Maitra, R. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2915052 · Cited by 4
Investigation of layer specific BOLD in the human visual cortex during visual attentionPreprint
van Mourik, T., Koopmans, P. J., Bains, L. J., Norris, D. G., & Jehee, J. F. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.07.430129 · Cited by 4
Semantic network activation facilitates oral word reading in chronic aphasia
Pillay, S. B., Gross, W. L., Heffernan, J., Book, D., & Binder, J. (2022) · Brain and Language · doi:10.1016/j.bandl.2022.105164 · Cited by 4
Asynchronous firing and off states in working memory maintenance
Mozumder, R., Wang, Z., Dang, W., Zhu, J., Hammond, B. M., Machado, A., & Constantinidis, C. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116764 · Cited by 3
Optimizing data processing to improve the reproducibility of single‐subject functional magnetic resonance imaging
Soltysik, D. A. (2020) · Brain and Behavior · doi:10.1002/brb3.1617 · Cited by 3
The brain landscape of the two-hit model of posttraumatic stress disorder
James, L. M., Engdahl, B. E., Christova, P. S., Lewis, S. M., & Georgopoulos, A. P. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00340.2022 · Cited by 3
Using camera-guided electrode microdrive navigation for precise 3D targeting of macaque brain sites
Crayen, M. A., Kagan, I., Esghaei, M., Hoehl, D., Thomas, U., Prückl, R., Schaffelhofer, S., & Treue, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301849 · Cited by 3
Cortical volumetric changes after cochlear implantation in postlingually deaf adults: correlation with speech perception abilities
Lee, Y. J., Heo, H., Joo, H. A., Choi, Y., Shim, W. H., & Park, H. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-68002-9 · Cited by 3
Groupwise Multimodal Image Registration Using Joint Total Variation
Brudfors, M., Balbastre, Y., & Ashburner, J. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-52791-4_15 · Cited by 3
An inclusive multivariate approach to neural localization of language components
Graves, W. W., Levinson, H., Staples, R., Boukrina, O. V., Rothlein, D., & Purcell, J. J. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02800-9 · Cited by 3
Functional resilience of the neural visual recognition system post-pediatric occipitotemporal resection
Granovetter, M. C., Maallo, A. M. S., Ling, S., Robert, S., Freud, E., Patterson, C., & Behrmann, M. (2024) · iScience · doi:10.1016/j.isci.2024.111440 · Cited by 3
Normal Retinotopy in Primary Visual Cortex in a Congenital Complete Unilateral Lesion of Lateral Geniculate Nucleus in Human: A Case Study
Bhat, A., Kurzawski, J. W., Anobile, G., Tinelli, F., Biagi, L., & Morrone, M. C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031055 · Cited by 3
The effect of feature image on sensitivity of the statistical analysis in the pipeline of a tractography atlas-based analysis
Mu, J., Xu, Q., Tian, J., & Liu, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-12965-5 · Cited by 3
Altered Resting-State Functional Connectivity in Wernicke's Encephalopathy With Vestibular Impairment
Oh, S., Lee, J. H., Kang, J., Park, Y.-H., Kim, K. W., Lee, J. M., Kim, J., & Dieterich, M. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01035 · Cited by 4
The coupling of global brain activity and cerebrospinal fluid inflow is correlated with Alzheimer’s disease related pathologyPreprint
Han, F., Chen, J., Belkin-Rosen, A., Gu, Y., Luo, L., Buxton, O. M., Liu, X., & Initiative, A. D. N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.04.134726 · Cited by 3
Improving the Sensitivity of Task-Based Multi-Echo Functional Magnetic Resonance Imaging via T2* Mapping Using Synthetic Data-Driven Deep Learning
Zhao, Y., Yang, Q., Qian, S., Dong, J., Cai, S., Chen, Z., & Cai, C. (2024) · Brain Sciences · doi:10.3390/brainsci14080828 · Cited by 2
Show Me the Brain!!: A modern approach to neuroanatomy education
Hindy, N. C., Bishara, A. J., Lovellette, E., & Pani, J. R. (2026) · Anatomical Sciences Education · doi:10.1002/ase.70223 · Cited by 2
Prior Methamphetamine Use Disorder History Does Not Impair Interoceptive Processing of Soft Touch in HIV Infection
Bischoff‐Grethe, A., Ellis, R. J., Tapert, S. F., Paulus, M. P., & Grant, I. (2021) · Viruses · doi:10.3390/v13122476 · Cited by 2
Spatial functional reorganizations can serve as potential biomarkers of post facial palsy synkinesis
Zhu, H., Cui, T., Xue, Y., Wang, D., Ding, W., & Wu, R. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae184 · Cited by 2
Association between neurovascular coupling and neural signals in the resting state as revealed by a combined fMRI and magnetoencephalography study in humans
Georgopoulos, A. P., Christova, P. S., Leuthold, A. C., & James, L. M. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00360.2024 · Cited by 2
Detection of perivascular spaces at the gray-white matter interface using heavily T2-weighted MRI at 7T
Saïb, G., Demir, Z., Taylor, P. A., Talagala, S. L., & Koretsky, A. P. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121757 · Cited by 2
Predictive Computing: A Technical Insight
Gupta, P. K., Tyagi, V., & Singh, S. K. (2017) · Springer eBooks · doi:10.1007/978-981-10-5107-4_3 · Cited by 2
Graph Matching Between Bipartite and Unipartite Networks: To Collapse, or Not to Collapse, That Is the Question
Arroyo, J., Priebe, C. E., & Lyzinski, V. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3086508 · Cited by 2
Groupwise Multimodal Image Registration Using Joint Total Variation
Brudfors, M., Balbastre, Y., & Ashburner, J. (2020) · UCL Discovery (University College London) · Cited by 2
Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm
Deng, L., Zhang, Y., Qi, J., Huang, S., Yang, X. N., & Wang, J. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023204 · Cited by 2
Comparison of functional localization accuracy with different co‐registration strategies in presurgical fMRI for brain tumor patients
Jen, M., Hassan, I. S. A., Hou, P., Li, G., Kumar, A. J., Prabhu, S. S., Colen, R. R., & Liu, H. (2018) · Medical Physics · doi:10.1002/mp.12999 · Cited by 2
Surface-driven registration method for the structure-informed segmentation of diffusion MR images
Estéban, O., Zosso, D. P., Daducci, A., Bach-Cuadra, M., Carbayo, M. J. L., Thiran, J., & Lleo, A. D. S. (2016) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 2
Detection of Autism Spectrum Disorder Using Graph Representation Learning Algorithms and Deep Neural Network, Based on fMRI SignalsPreprint
Yousedian, A., Shayegh, F., & Maleki, Z. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.23.497324 · Cited by 2
MR Fingerprinting Predicts Gait Improvement After Lumbar Cerebrospinal Fluid Drainage for Normal Pressure Hydrocephalus
Jones, R. S., Chen, J. M., Moon, D., Nittka, M., Ejikeme, T., Ma, D., Griswold, M. A., Nagel, S. J., & Jones, R. S. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29788 · Cited by 1
Stereotactic lesioning of cholinergic cells by injection of ME20.4 Saporin in the nucleus basalis of Meynert in a rhesus monkey ( Macaca mulatta )
Nazmuddin, M., Stammes, M. A., Klink, P. C., Vernes, M. K., Bakker, J., Langermans, J. A. M., van Laar, T., & Philippens, I. H. C. H. M. (2025) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlaf081 · Cited by 1
Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex
Guo, F., Zhao, C., Bhatt, R. R., Liu, Z., Kim, A. J., Yang, Z., Xu, S., Jann, K., Shao, X., Mather, M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76812-w · Cited by 1
Repeatable, low-drift recordings in behaving nonhuman primates using flexible microelectrodes
Woods, D. P., Adams, G., Mozumder, R., Dang, W., Chen, A. Y., Chevée, M., Constantinidis, C., & Gonzales, D. L. (2026) · Nature Communications · doi:10.1038/s41467-026-77421-3 · Cited by 2
Development of the Complex General Linear Model in the Fourier Domain: Application to fMRI Multiple Input-Output Evoked Responses for Single Subjects
Rio, D. E., Rawlings, R. R., Woltz, L. A., Gilman, J. M., & Hommer, D. W. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/645043 · Cited by 1
Kidnapped laser-scanner for evaluation of RFEC tool
Falque, R., Vidal‐Calleja, T., & Miró, J. V. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353391 · Cited by 1
A Customizable Multimodality Imaging Compound That Relates External Landmarks to Internal Structures
Semework, M. (2015) · Journal of Nuclear Medicine Technology · doi:10.2967/jnmt.115.162404 · Cited by 1
GPLFR—Global perspective and local flow registration-for forward-looking sonar images
Huang, P. F., Guo, C., Fu, X., Xu, L., & Zhou, D. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07113-8 · Cited by 1
A practical model‐based segmentation approach for improved activation detection in single‐subject functional magnetic resonance imaging studies
Chen, W., & Maitra, R. (2023) · Human Brain Mapping · doi:10.1002/hbm.26425 · Cited by 1
Spatiotemporal characteristics of the neural representation of event concepts
Desai, R. H., Hackett, C., Johari, K., Lai, V. T., & Riccardi, N. (2023) · Brain and Language · doi:10.1016/j.bandl.2023.105328 · Cited by 1
Self-citation
Design and methodology for a proof of mechanism study of individualized neuronavigated continuous Theta burst stimulation for auditory processing in adolescents with autism spectrum disorder
Oberman, L. M., Francis, S. M., Beynel, L., Hynd, M., Jaime, M., Robins, P. L., Deng, Z., Stout, J., van der Veen, J. W. C., & Lisanby, S. H. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1304528 · Cited by 1
Neural correlates of reading aloud on the autism spectrum
McCabe, C., Cahalan, S., Pincus, M., Rosenberg‐Lee, M., & Graves, W. W. (2025) · Scientific Reports · doi:10.1038/s41598-025-88903-7 · Cited by 1
A Simple Structural Magnetic Resonance Imaging (MRI) Method for 3D Mapping between Head Skin Tattoos and Brain Landmarks
Semework, M. (2010) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-14998-6_126 · Cited by 1
Peeking into the Black Box of Coregistration in Clinical fMRI: Which Registration Methods Are Used and How Well Do They Perform?
Raslau, F. D., Lin, L. Y., Andersen, A. H., Powell, D. K., Smith, C. D., & Escott, E. J. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5846 · Cited by 1
Can a Theater Acting Intervention Enhance Inhibitory Control in Older Adults? A Brain-Behavior Investigation
Rajesh, A., Noice, T., Noice, H., Jahn, A., Daugherty, A. M., Heller, W., & Kramer, A. F. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.583220 · Cited by 2
Distortion, motion artifacts and how to address them
Tisdall, M. D. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00002-8 · Cited by 1
Network and Cluster Analysis on Bridge Inspection Reports Using Text Mining Algorithms
Jung, Y. E., Kang, M., Jeong, M. G., & Ahn, J. (2022) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784483961.052 · Cited by 1
NORDIC Increases the Sensitivity and Preserves the Spatiotemporal Precision of fMRI ResponsesPreprint
Dowdle, L. T., Vizioli, L., Moeller, S., Akçakaya, M., Olman, C. A., Ghose, G. M., Yacoub, E. S., & Uǧurbil, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.26.457833 · Cited by 1
Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI
Estéban, O. (2013) · Hispana · Cited by 1
The Dissociation of Location and Object Working Memory using fMRI and MEG
Passaro, A. D. (2011) · Digital Commons-TMC (Texas Medical Center)
The Neural Substrates of Multisensory Speech Perception
Nath, A. R. (2011) · Digital Commons-TMC (Texas Medical Center)
Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition
Iacovella, V. (2012) · Unitn-eprints PhD (University of Trento) · doi:10.15168/11572_368089
Grundlagen der Morphometrie
Pieperhoff, P., Dickscheid, T., & Amunts, K. (2013) · Springer eBooks · doi:10.1007/978-3-642-29800-4_5
Neural activation within components of verbal working memory following sleep loss
McKenna, B. S. (2011) · eScholarship (California Digital Library)
Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI
Estéban, O. (2015) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.38431 · Cited by 1
Effects of Exercise Therapy on Functional Connectivity in Parkinson's Disease
Shah, C. (2013) · OhioLink ETD Center (Ohio Library and Information Network)
Detecção de correlação e causalidade em séries temporais não categóricas
Falcão, A. J. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Investigation of the Effects of Aging and Small Vessel Disease on Cardiac Frequency Signal in Cerebral White Matter as Imaged by Echo Planar Imaging using Magnetic Resonance
Makedonov, I. (2012) · TSpace (University of Toronto)
Development of new computational methods for non-invasive brain stimulation
Opitz, A. (2014) · doi:10.53846/goediss-4604
Article Open Access
Lawford‐Smith, H. (2015)
Nonmodel-based Dynamic Contrast-enhanced Magnetic Resonance Imaging for the Assessment of High versus Low Risk Carotid Atherosclerosis
MacLean, D. B. (2011) · TSpace (University of Toronto)
A New Registration Algorithm Combined MRF with BBR
Yan, J., & Yang, J. (2016) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itme.2016.0016
Detection of the Structural Failing Point Using Correlation and Local Correlation Analysis
Oh, H., Jung, Y. E., Ahn, J., Kim, S.-J., & Jeong, M. G. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482858.026
A Practical Model-based Segmentation Approach for Accurate Activation Detection in Single-Subject functional Magnetic Resonance Imaging StudiesPreprint
Chen, W.-C., & Maitra, R. (2021) · arXiv (Cornell University)
Lifetime Alcohol Use Influences the Association Between Future-Oriented Thought and White Matter Microstructure in Adolescents
Jones, S. A., Kazakova, N., & Nagel, B. J. (2020) · Alcohol and Alcoholism · doi:10.1093/alcalc/agaa149
Repeated unilateral handgrip contractions alter functional connectivity and improve contralateral limb response times: A neuroimaging studyPreprint
Andrushko, J. W., Levenstein, J. M., Zich, C., Edmond, E., Campbell, J., Clarke, W. T., Emir, U. E., Farthing, J. P., & Stagg, C. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.23.464569
Current Awareness in NMR in Biomedicine
(2009) · NMR in Biomedicine · doi:10.1002/nbm.1335
Peer Review #1 of "Sensitivity to the visual field origin of natural image patches in human low-level visual cortex (v0.1)"
(2015) · PeerJ · doi:10.7287/peerj.1038v0.1/reviews/1
Correction: Quantifying Agreement between Anatomical and Functional Interhemispheric Correspondences in the Resting Brain
Jo, H. J., Saad, Z. S., Gotts, S. J., Martin, A., & Cox, R. W. (2013) · PLoS ONE · doi:10.1371/annotation/cb15c6af-2153-49a9-8330-45e40e6c296d
Self-citation
Peer Review #1 of "Sensitivity to the visual field origin of natural image patches in human low-level visual cortex (v0.2)"
(2015) · PeerJ · doi:10.7287/peerj.1038v0.2/reviews/1
Ultra-high field brain perfusion MRI
Shao, X., Rooney, W. D., & Wang, D. J. J. (2023) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-323-99898-7.00037-7
Modeling the association between functional connectivity and lateralization with the activity flow framework
Zhan, X., Lang, J., Yang, L., & Li, H. (2024) · Brain Research · doi:10.1016/j.brainres.2024.148831
Arousal threshold reveals novel neural markers of sleep depth independently from the conventional sleep stagesPreprint
Picchioni, D., Yang, F. N., de Zwart, J. A., Wang, Y., Mandelkow, H., Özbay, P. S., Chen, G., Taylor, P. A., Lam, N., Chappel-Farley, M. G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.09.607376
Self-citation
Distributionally-Adaptive Variational Meta Learning for Brain Graph Classification
Du, J., Dong, G., Ma, C., Xue, S., Wu, J. X., Yang, J. Y., Beheshti, A., Sheng, Q. Z., & Giral, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_22
The human brain mechanisms of afterimages: From networks to cortical layersPreprint
Kronemer, S. I., Akın, B., Holness, M., Morgan, A., Huber, L., Taylor, P. A., González-Castillo, J., Handwerker, D. A., & Bandettini, P. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.30.673266
Age-dependent brain neuroimaging signatures in rhesus monkeys
Lee, B., Laux, J., Cong, Y., Worwa, G., Castro, M. A., Byrum, R. A., Cooper, K., Kuhn, J. H., Holbrook, M. R., Calcagno, C., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121586
A History of Methamphetamine Use Disorder in People with HIV Is Associated with Altered Functional Response to Risky Choice
Happer, J. P., Tapert, S. F., Grant, I., & Bischoff‐Grethe, A. (2026) · Viruses · doi:10.3390/v18030369
Field-validated underwater microcavity acoustic sensor
Tang, X., Yao, H., Yang, Z., Qu, Z., Zhang, Y., He, T., Huo, Y., Wang, Z., Shi, W., Lin, C., et al. (2026) · Applied Physics Letters · doi:10.1063/5.0313316

Authors: Saad, Z. S., Glen, D. R., Chen, G., Beauchamp, M. S., Desai, R., & Cox, R. W.
Affiliations: National Institutes of Health (US)
The University of Texas Health Science Center at Houston (US)
Medical College of Wisconsin (US)
Corresponding author: Cox, Robert W.
ORCID: Glen, Daniel R.
Chen, Gang
Beauchamp, Michael S.
Desai, Rutvik
Cox, Robert W.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2009-02
Volume: 44
Issue: 3
Pages: 839-848
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2008.09.037
PMC: PMC2649831
OpenAlex: W2018731294
Cited by: 405
References: 37
Citations by year: 2026: 12 · 2025: 11 · 2024: 21 · 2023: 18 · 2022: 21 · 2021: 30 · 2020: 30 · 2019: 16 · 2018: 23 · 2017: 27 · 2016: 21 · 2015: 37 · 2014: 33 · 2013: 36 · 2012: 26
FWCI: 20.56
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Scholar Commons (University of South Carolina) (submittedVersion)
Funders: National Science Foundation
National Institutes of Health
National Institute of Mental Health (Z01MH002888)
National Institute on Deafness and Other Communication Disorders (R01DC010783, R03DC008416)
National Institute of Neurological Disorders and Stroke (R01NS033576, 2R01NS33576-11)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: cost functional, mutual information, correlation ratio, BOLD fMRI, surgical planning, composite images, CsF
MeSH terms: Algorithms, Artificial Intelligence, Brain, Data Interpretation, Statistical, Humans, Image Enhancement, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Statistics as Topic, Subtraction Technique, Reproducibility of Results, Echo-Planar Imaging, Imaging, Three-Dimensional

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A new method for improving functional-to-structural MRI alignment using local Pearson correlation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks