Cited by 196Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesMultiple Sclerosis Research Studies

The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.

Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity

Open access

Lowe, Mark J., Beall, Erik B., Sakaie, Ken E., Koenig, Katherine A., Stone, Lael, Marrie, Ruth Ann, Phillips, Micheal D.·2008·Human Brain Mapping

DOI 10.1002/hbm.20576PMC PMC6871176

Open this paper in CoChat — free account Open PDF

Abstract

Recent studies indicate that functional connectivity using low‐frequency BOLD fluctuations (LFBFs) is reduced between the bilateral primary sensorimotor regions in multiple sclerosis. In addition, it has been shown that pathway‐dependent measures of the transverse diffusivity of water in white matter correlate with related clinical measures of functional deficit in multiple sclerosis. Taken together, these methods suggest that MRI methods can be used to probe both functional connectivity and anatomic connectivity in subjects with known white matter impairment. We report the results of a study comparing anatomic connectivity of the transcallosal motor pathway, as measured with diffusion tensor imaging (DTI) and functional connectivity of the bilateral primary sensorimotor cortices (SMC), as measured with LFBFs in the resting state. High angular resolution diffusion imaging was combined with functional MRI to define the transcallosal white matter pathway connecting the bilateral primary SMC. Maps were generated from the probabilistic tracking employed and these maps were used to calculate the mean pathway diffusion measures fractional anisotropy 〈FA〉, mean diffusivity 〈MD〉, longitudinal diffusivity 〈λ 1〉, and transverse diffusivity 〈λ 2〉. These were compared with LFBF‐based functional connectivity measures (Fc) obtained at rest in a cohort of 11 multiple sclerosis patients and ∼10 age‐ and gender‐matched control subjects. The correlation between 〈FA〉 and Fc for MS patients was r = −0.63, P < 0.04. The correlation between all subjects 〈λ 2〉 and Fc was r = 0.42, P < 0.05. The correlation between all subjects 〈λ 2〉 and Fc was r = −0.50, P < 0.02. None of the control subject correlations were significant, nor were 〈FA〉, 〈λ 1〉, or 〈MD〉 significantly correlated with Fc for MS patients. This constitutes the first in vivo observation of a correlation between measures of anatomic connectivity and functional connectivity using spontaneous LFBFs. Hum Brain Mapp, 2008. © 2008 Wiley‐Liss, Inc.

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 · 10.1002/hbm.20576

Cites

68 of 68 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.

The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
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
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
Numerical Recipes, The Art of Scientific Computing
Press, W. H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T., & Gould, H. (1987) · American Journal of Physics · doi:10.1119/1.14981 · Cited by 10,032
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Numerical Recipes: The Art of Scientific Computing
Ziegel, E. R., Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1987) · Technometrics · doi:10.2307/1269484 · Cited by 6,704
Axonal transection in the lesions of multiple sclerosis
Trapp, B. D., Peterson, J., Ransohoff, R. M., Rudick, R., Mörk, S., & Bö, L. (1998) · New England Journal of Medicine · doi:10.1056/nejm199801293380502 · Cited by 4,029
Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water
Song, S.-K., Sun, S.-W., Ramsbottom, M. J., Chang, C., Russell, J., & Cross, A. H. (2002) · NeuroImage · doi:10.1006/nimg.2002.1267 · Cited by 2,582
Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia
Song, S.-K., Sun, S.-W., Ju, W.-K., Lin, S.-J., Cross, A. H., & Neufeld, A. H. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.005 · Cited by 1,738

Flagged first, then by citations

Show all 69 references

1 of the 69 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

Adams RD, 1997, Principles Of Neurology, 903

Sorted by citations

Items 101–196 of 196

Higher Frequency Network Activity Flow Predicts Lower Frequency Node Activity in Intrinsic Low-Frequency BOLD Fluctuations
Bajaj, S., Adhikari, B. M., & Dhamala, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064466 · Cited by 26
Biclustering fMRI time series: a comparative study
Castanho, E. N., Aidos, H., & Madeira, S. C. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04733-8 · Cited by 31
Three approaches to investigating functional compromise to the default mode network after traumatic axonal injury
Arenivas, A., Diaz-Arrastia, R. R., Spence, J. S., Cullum, C. M., Krishnan, K., Bosworth, C. C., Culver, C., Kennard, B. D., & de la Plata, C. M. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9191-2 · Cited by 23
The Contribution of Resting State Networks to the Study of Cortical Reorganization in MS
Sacco, R., Bonavita, S., Esposito, F., Tedeschi, G., & Gallo, A. (2013) · Multiple Sclerosis International · doi:10.1155/2013/857807 · Cited by 22
Position Sense Deficits at the Lower Limbs in Early Multiple Sclerosis: Clinical and Neural Correlates
Iandolo, R., Bommarito, G., Falcitano, L., Schiavi, S., Piaggio, N., Mancardi, G., Casadio, M., & Inglese, M. (2020) · Neurorehabilitation and neural repair · doi:10.1177/1545968320902126 · Cited by 25
Brain Connections – Resting State fMRI Functional Connectivity
de la Iglesia-Vayá, M., Molina‐Mateo, J., Jose, M., Ahmad, S., & Martí‐Bonmatí, L. (2013) · InTech eBooks · doi:10.5772/52273 · Cited by 19
Physical exercise keeps the brain connected by increasing white matter integrity in healthy controls
Bashir, S., Alsultan, F., Abujamea, A. H., Almousa, A., Alnafisah, M., Alzahrani, M., Abualait, T., & Yoo, W. (2021) · Medicine · doi:10.1097/md.0000000000027015 · Cited by 19
Exploración de la red cerebral: una revisión de la conectividad funcional en la RMf en estado de reposo
van den Heuvel, M. P., & Pol, H. E. H. (2011) · Psiquiatría Biológica · doi:10.1016/j.psiq.2011.05.001 · Cited by 25
Novel MRI approaches to assess patients with multiple sclerosis
Filippi, M., & Rocca, M. A. (2010) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32833787b0 · Cited by 18
Increased functional homotopy of the prefrontal cortex is associated with corpus callosum degeneration and working memory decline
Avelar‐Pereira, B., Bäckman, L., Wåhlin, A., Nyberg, L. H., & Salami, A. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.08.008 · Cited by 22
Imaging white matter diffusion changes with development and recovery from brain injury
Bosnell, R., Giorgio, A., & Johansen‐Berg, H. (2008) · Developmental Neurorehabilitation · doi:10.1080/17518420802289065 · Cited by 18
Changes in brain functional connectivity and cognition related to white matter lesion burden in hypertensive patients from SPRINT
Shah, C., Srinivasan, D., Erus, G., Schmitt, J. E., Agarwal, A., Cho, M. E., Lerner, A. J., Haley, W. E., Tamura, M. K., Davatzikos, C. A., et al. (2021) · Neuroradiology · doi:10.1007/s00234-020-02614-6 · Cited by 17
Anatomic Connectivity Assessed Using Pathway Radial Diffusivity Is Related to Functional Connectivity in Monosynaptic Pathways
Lowe, M. J., Koenig, K. A., Beall, E. B., Sakaie, K., Stone, L. A., Bermel, R. A., & Phillips, M. D. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0265 · Cited by 16
Self-citation
How people with multiple sclerosis cope with a sustained finger motor task: A behavioural and fMRI study
Bonzano, L., Pardini, M., Roccatagliata, L., Mancardi, G., & Bove, M. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.02.008 · Cited by 15
Multimodal fusion analysis of structural connectivity and gray matter morphology in migraine
Planchuelo‐Gómez, Á., García‐Azorín, D., Peral, Á. L. G., Aja‐Fernández, S., Rodríguez, M., & de Luis-Garcı́a, R. (2020) · Human Brain Mapping · doi:10.1002/hbm.25267 · Cited by 14
Altered neural mechanisms of cognitive control in patients with primary progressive multiple sclerosis: An effective connectivity study
Dobryakova, E., Rocca, M. A., Valsasina, P., DeLuca, J., & Filippi, M. (2017) · Human Brain Mapping · doi:10.1002/hbm.23542 · Cited by 14
Subject-specific functional parcellation via Prior Based Eigenanatomy
Dhillon, P. S., Wolk, D. A., Das, S. R., Ungar, L., Gee, J. C., & Avants, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.026 · Cited by 13
Determination of Dominant Frequency of Resting-State Brain Interaction within One Functional System
Zhang, Y.-J., Duan, L. F., Zhang, H., Biswal, B. B., Lu, C., & Zhu, C.-Z. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051584 · Cited by 15
Advanced Magnetic Resonance Imaging in Pediatric Multiple Sclerosis
Verhey, L. H., & Sled, J. G. (2013) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2012.12.011 · Cited by 12
Logical Foundations and Fast Implementation of Probabilistic Tractography
Zhang, M., Sakaie, K., & Jones, S. E. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2257179 · Cited by 12
Self-citation
Exploratory MRI measures after intravenous autologous culture-expanded mesenchymal stem cell transplantation in multiple sclerosis
Feng, J. J., Offerman, E. J., Lin, J. Y., Fisher, E., Planchon, S. M., Sakaie, K., Lowe, M. J., Nakamura, K., Cohen, J. A., & Ontaneda, D. (2019) · Multiple Sclerosis Journal - Experimental Translational and Clinical · doi:10.1177/2055217319856035 · Cited by 12
Self-citation
A surface‐based technique for mapping homotopic interhemispheric connectivity: Development, characterization, and clinical application
Tobyne, S. M., Daria, B., Johnson, J., Greve, D. N., Mainero, C., & Klawiter, E. C. (2016) · Human Brain Mapping · doi:10.1002/hbm.23214 · Cited by 11
Brain functional plasticity at rest and during action in multiple sclerosis patients
Faivre, A., Rico, A., Zaaraoui, W., Françoise Reuter, A., Sylviane Confort-gouny, A., Guye, M., Pelletier, J., Ranjeva, J., & Audoin, B. (2015) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2015.02.033 · Cited by 11
Morphological MRI phenotypes of multiple sclerosis differ in resting-state brain function
Pinter, D., Beckmann, C. F., Fazekas, F., Khalil, M., Pichler, A., Gattringer, T., Ropele, S., Fuchs, S., & Enzinger, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-52757-7 · Cited by 11
Associations between corpus callosum damage, clinical disability, and surface-based homologous inter-hemispheric connectivity in multiple sclerosis
Russo, A. W., Stockel, K. E., Tobyne, S. M., Ngamsombat, C., Brewer, K., Nummenmaa, A. R., Huang, S. Y., & Klawiter, E. C. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02498-7 · Cited by 10
Quantitative Assessment of Brain Networks in Children With Sturge-Weber Syndrome Using Resting State Functional Magnetic Resonance Imaging (MRI)
Jeong, J., Chugani, H. T., Behen, M. E., Guy, W., & Juhász, C. (2012) · Journal of Child Neurology · doi:10.1177/0883073812469296 · Cited by 10
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals
Tu, W., Cramer, S. R., & Zhang, N. (2024) · eLife · doi:10.7554/elife.95680.3 · Cited by 9
Functional Connectivity MR Imaging
Hampson, M., Shen, X., & Constable, R. T. (2011) · Springer eBooks · doi:10.1007/978-1-4419-0345-7_21 · Cited by 9
Changes in structural and functional connectivity during two years of fingolimod therapy for multiple sclerosis
Bhattacharyya, P. K., Lowe, M. J., Sakaie, K., Li, H., Lin, J. Y., & Fox, R. J. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.09.015 · Cited by 8
Self-citation
Anatomical Connectivity Between Subcortical Structures
Taljan, K. A. I., McIntyre, C. C., & Sakaie, K. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0011 · Cited by 6
Self-citation
Functional imaging in multiple sclerosis
Kern, K. C., & Sicotte, N. L. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139023986.017 · Cited by 6
Evaluation of a connectivity-based imaging metric that reflects functional decline in Multiple Sclerosis
Koenig, K. A., Beall, E. B., Sakaie, K., Ontaneda, D., Stone, L. A., Rao, S. M., Nakamura, K., Jones, S. E., & Lowe, M. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251338 · Cited by 6
Self-citation
Asymmetric Functional Gradients in the Human SubcortexPreprint
Guell, X., Schmahmann, J. D., Gabrieli, J. D. E., Ghosh, S., & Geddes, M. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.04.283820 · Cited by 6
Neuroimaging in Multiple Sclerosis
Andreadou, E. (2012) · InTech eBooks · doi:10.5772/24069 · Cited by 5
Mapping Brain Networks Using Multimodal Data
Shou, G., Yuan, H., & Ding, L. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_83 · Cited by 4
Characterizing multiple sclerosis disease progression using a combined structural and functional connectivity metric
Bhattacharyya, P. K., Fox, R. J., Sakaie, K., Bena, J., Harvey, T., Raska, P., Lin, J. Y., & Lowe, M. J. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.07.016 · Cited by 4
Self-citation
Hierarchical distributed compressive sensing for simultaneous sparse approximation and common component extraction
Mahyari, A., & Aviyente, S. (2016) · Digital Signal Processing · doi:10.1016/j.dsp.2016.09.010 · Cited by 4
Clinical trial methodology
Cohen, J. A., & Rudick, R. A. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139023986.009 · Cited by 4
Author response: Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals
Tu, W., Cramer, S. R., & Zhang, N. (2024) · eLife · doi:10.7554/elife.95680.2.sa0 · Cited by 3
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals
Tu, W., Cramer, S. R., & Zhang, N. (2024) · eLife · doi:10.7554/elife.95680 · Cited by 13
Advances in Diagnostics for Neurodegenerative Disorders
Bokde, A. L. W., Meaney, J. F. M., Sheehy, N. P., Reilly, R. B., Abrahams, S., & Doherty, C. P. (2011) · Springer eBooks · doi:10.1007/978-1-84996-011-3_2 · Cited by 3
Influence of equipment changes on MRI measures of brain atrophy and brain microstructure in a placebo-controlled trial of ibudilast in progressive multiple sclerosis
Sakaie, K., Fedler, J. K., Yankey, J. W., Nakamura, K., Debbins, J. P., Lowe, M. J., Raska, P., & Fox, R. J. (2021) · Multiple Sclerosis Journal - Experimental Translational and Clinical · doi:10.1177/20552173211010843 · Cited by 3
Self-citation
Cohesive parcellation of the human brain using resting-state fMRI
Nemani, A., & Lowe, M. J. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109629 · Cited by 3
Self-citation
Active and Passive fMRI for Presurgical Mapping of Motor and Language Cortex
Goodyear, B. G., Liebenthal, E., & Mosher, V. (2014) · InTech eBooks · doi:10.5772/58269 · Cited by 3
Individual differences in white and grey matter structure associated with verbal habits of thought
Hayes, J. C., Alfred, K. L., Pizzie, R. G., Cetron, J. S., & Kraemer, D. J. M. (2020) · Brain Research · doi:10.1016/j.brainres.2020.146890 · Cited by 2
Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis
Riazi, A. H., Rabbani, H., & Kafieh, R. (2022) · Diagnostics · doi:10.3390/diagnostics12092263 · Cited by 2
Toward whole-brain maps of neural connections: Logical framework and fast implementation
Zhang, M., Sakaie, K. E., & Jones, S. E. (2012) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.2012.6164755 · Cited by 2
Self-citation
Joint Modelling of Structural and Functional Brain Networks
Andersen, K. W., Herlau, T., Mørup, M., Schmidt, M. N., Madsen, K. H., Lyksborg, M., Dyrby, T. B., Siebner, H. R., & Hansen, L. K. (2012) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 2
Network Imaging
Guo, J. N., & Blumenfeld, H. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-415804-7.00006-x · Cited by 3
Functional Connectivity MR Imaging
Horien, C. L., Shen, X., Scheinost, D., Constable, R. T., & Hampson, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-10909-6_24 · Cited by 2
Multiple Sclerosis
Wahl, M. (2012) · Springer eBooks · doi:10.1007/978-3-642-32767-4_14 · Cited by 1
Diffusion tensor imaging in the SPRINT-MS clinical trial: Advancing trial methodology
Sakaie, K., Du, M., Obuchowski, N. A., Lowe, M. J., Lin, J. Y., & Fox, R. J. (2025) · Multiple Sclerosis Journal - Experimental Translational and Clinical · doi:10.1177/20552173251361225 · Cited by 1
Self-citation
Brain Reserve in Multiple Sclerosis: The Impact of Maximal Lifetime Brain Growth on Fine Motor Functioning
Plunkett, L. G. (2016) · doi:10.7916/d8th8mx7 · Cited by 1
Diffusion imaging in multiple sclerosis
Jones, S. E., & Phillips, M. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139023986.018 · Cited by 1
Self-citation
Investigations of Anatomical Connectivity in the Internal Capsule of Macaques with Diffusion Magnetic Resonance Imaging
Taljan, K. A. I. (2011) · EngagedScholarship @ Cleveland State University (Cleveland State University) · Cited by 1
Multiple Sclerosis
Carotenuto, A., Iodice, R., & Arcara, G. (2021) · Springer eBooks · doi:10.1007/978-3-030-74985-9_16 · Cited by 1
Functional Connectivity MR Imaging
Hampson, M., Shen, X., & Constable, R. T. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1995-6_6 · Cited by 1
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signalsPreprint
Tu, W., Cramer, S. R., & Zhang, N. (2024) · eLife · doi:10.7554/elife.95680.1 · Cited by 1
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signalsPreprint
Tu, W., Cramer, S. R., & Zhang, N. (2024) · Research Square · doi:10.21203/rs.3.rs-3251741/v3 · Cited by 1
Functional MRI of Multiple Sclerosis
Wishart, H. A. (2011) · Springer eBooks · doi:10.1007/978-1-4419-0345-7_13 · Cited by 1
In vivo characterization of cerebral networks with functional and structural magnetic resonance techniques
Mastropasqua, C. (2014) · OpenstarTs (Univeristy of Trieste https://www.units.it/)
Multivariate data analysis applied to MRS and MRI studies of aging and spinal cord injury
Öberg, J. (2011) · Figshare
POSSIBLE CLINICAL APPLICATION OF RESTING-STATE fMRI IN NEUROPSYCHIATRIC DISORDERS
Rikani, A. A., Choudhry, Z., Choudhry, A. M., Ikram, H., Kajal, D., & Sanghamitra, N. J. M. (2013) · Pakistan Journal of Radiology
Traces of disease in amyotrophic lateral sclerosis
Verstraete, E. (2012) · Utrecht University Repository (Utrecht University)
Effects of Exercise Therapy on Functional Connectivity in Parkinson's Disease
Shah, C. (2013) · OhioLink ETD Center (Ohio Library and Information Network)
Combining DTI and fMRI to investigate language lateralisation
Barsaglini, A. (2013) · Padua@research (University of Padova)
Functional Magnetic Resonance Imaging in Pediatric-onset Multiple Sclerosis: Relation to Structural Damage and Cognition
Akbar, N. (2015) · TSpace (University of Toronto)
Functional Connectivity of Entorhinal Cortex in Alzheimer's Disease
Long, S. F. (2008)
Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain
Good, T. J. (2015) · TSpace (University of Toronto)
High-homogeneity functional parcellation of human brain for investigating robust functional connectivity
Liu, X., Xie, H., Nutter, B. S., & Mitra, S. D. (2018) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai.2018.8470321
Rat Model of Pre-Motor Parkinson's Disease: Behavioral and MRI Characterization.
Rane, P. (2011) · Digital WPI
Longitudinal Changes of Structural and Functional Connectivity and Correlations with Neurocognitive Metrics
Zhou, Y. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.86641
The application of multimodal MRI in the identification of neurological biomarkers for Parkinson's disease and the investigation of the role of glymphatic system in the development of Parkinsonism
Cai, X. (2020) · Digital Repository Service (Northeastern University) · doi:10.17760/d20398331
Current Awareness in NMR in Biomedicine
(2009) · NMR in Biomedicine · doi:10.1002/nbm.1330
Mapping Brain Networks Using Multimodal Data
Shou, G., Yuan, H., & Ding, L. (2022) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_83-1
Functional MRI of Multiple Sclerosis
Wishart, H. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-10909-6_34
Resting state brain networks arise from electrophysiology-invisible signalsPreprint
Zhang, N., Tu, W., & Cramer, S. R. (2023) · Research Square · doi:10.21203/rs.3.rs-3251741/v1
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signalsPreprint
Tu, W., Cramer, S. R., & Zhang, N. (2024) · Research Square · doi:10.21203/rs.3.rs-3251741/v2
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signalsPreprint
Tu, W., Cramer, S. R., & Zhang, N. (2024) · Research Square · doi:10.21203/rs.3.rs-3251741/v4
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signalsPreprint
Tu, W., Cramer, S. R., & Zhang, N. (2024) · Research Square · doi:10.21203/rs.3.rs-3251741/v5 · Cited by 1
Reviewer #1 (Public Review): Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals
(2024) · eLife · doi:10.7554/elife.95680.2.sa2
Reviewer #2 (Public Review): : Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals
(2024) · eLife · doi:10.7554/elife.95680.2.sa1
eLife Assessment: Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals
Keilholz, S. D. (2024) · eLife · doi:10.7554/elife.95680.2.sa3
Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signalsPreprint
Tu, W., Cramer, S. R., & Zhang, N. (2024) · eLife · doi:10.7554/elife.95680.2
Investigation of neuro‐vascular reactivity on fMRI study during visual activation in people with multiple sclerosis using EEG and hypercapnia challenge
Shin, W., Krishnan, B., Nemani, A., Ontaneda, D., & Lowe, M. J. (2025) · Medical Physics · doi:10.1002/mp.17772
Self-citation
A New Method for Dynamic Brain Connectivity Analysis Based on Tensor Decomposition in Tinnitus Using High-density Electroencephalogram in Source Domain
Bahman, M., Sajadi, S. S., Toostani, I. G., & Makkiabadi, B. (2025) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_75_24
Functional connectivity in neuroinflammatory conditions
Filippi, M., Valsasina, P., & Rocca, M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19099-5.00011-8
Functional Connectivity in Human-Derived Neurospheroids
Parodi, G., Zanini, G., Collo, L., Chiappalone, M., & Martinoia, S. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252878
Etude de la réorganisation de la connectivité cérébrale au repos dans la sclérose en plaques
Faivre, A. (2014) · theses.fr (ABES) · doi:10.70675/e50f72d6z6aaaz4457za932z19affe0fe4dc
Réorganisation des systèmes anatomo-fonctionnels et de la topologie cérébrale entre les formes à début précoce et tardif de maladie d'Alzheimer. : approche comportementale et en IRMf de repos
Gour, N. (2013) · theses.fr (ABES) · doi:10.70675/ae7413f4z289fz49bez93c4zc085fb1b9c34
Etude et modélisation de la plasticité cérébrale chez des patients porteurs de lésions gliales de bas grade opérés en chirurgie éveillée
Coget, A. (2020) · doi:10.70675/0a8b5072zbdb2z4a29zadb1zf35c18b2f887
Paradigm free regularization for fMRI brain activation recovery
Costantini, I. (2020) · theses.fr (ABES) · doi:10.70675/9c93f23cza1e7z49e8zb02dz0f0b5204502f
Regulation of Alzheimer pathology by amyloid seeds : from toxic effects to therapeutic opportunities, in mouse model of amyloidosis
Célestine, M. (2023) · theses.fr (ABES) · doi:10.70675/873ca377z9a97z43f6z8051zdab4082686ba
Heightened respiratory brain pulsations indicate fluid dynamic dysfunction in early multiple sclerosis
Haverinen, J., Ryytty, M., Rusanen, H., Krüger, J., Martikainen, M. H., Kananen, J., Raitamaa, L., Huotari, N., Järvelä, M., Tuunanen, J., et al. (2026) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x261453802
Resting-state functional Magnetic Resonance Imaging
Choe, A. S. (2026) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-443-21425-7.00001-4
Cognitive assessment and quantitative MRI in systemic lupus erythematosus
Haynes, R. I. (2012) · University of Brighton Repository (University of Brighton)

Authors: Lowe, M. J., Beall, E. B., Sakaie, K. E., Koenig, K. A., Stone, L., Marrie, R. A., & Phillips, M. D.
Affiliations: Cleveland Clinic (US)
University of Manitoba (CA)
ORCID: Lowe, Mark J.
Beall, Erik B.
Sakaie, Ken E.
Koenig, Katherine A.
Marrie, Ruth Ann
Phillips, Micheal D.
Venue: Human Brain Mapping
Publisher: Wiley
Published: 2008-04-25
Volume: 29
Issue: 7
Pages: 818-827
ISSN: 1065-9471 (ISSN-L)
1097-0193
DOI: 10.1002/hbm.20576
PMC: PMC6871176
OpenAlex: W2022349962
Cited by: 196
References: 69
Citations by year: 2026: 2 · 2025: 5 · 2024: 12 · 2023: 7 · 2022: 5 · 2021: 6 · 2020: 10 · 2019: 5 · 2018: 5 · 2017: 6 · 2016: 12 · 2015: 10 · 2014: 13 · 2013: 21 · 2012: 18
FWCI: 15.15
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Human Brain Mapping (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Clinical Neurology › Multiple Sclerosis Research Studies
Keywords: multiple sclerosis, resting-state functional connectivity, sensorimotor cortex, fractional anisotropy, mean diffusivity, white matter connectivity, probabilistic tractography, high angular resolution diffusion imaging
MeSH terms: Brain Mapping, Corpus Callosum, Efferent Pathways, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Motor Cortex, Multiple Sclerosis

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

Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity

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