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Showing 1 to 12 of 53 entries
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Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI.

Frontiers in neuroscience

Degryse J, Seurinck R, Durnez J, Gonzalez-Castillo J, Bandettini PA, Moerkerke B.
PMID: 28484367
Front Neurosci. 2017 Apr 21;11:222. doi: 10.3389/fnins.2017.00222. eCollection 2017.

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important...

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Degryse J, Seurinck R, Durnez J, et al. Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI. Front Neurosci. 2017;11:222doi: 10.3389/fnins.2017.00222.
Degryse, J., Seurinck, R., Durnez, J., Gonzalez-Castillo, J., Bandettini, P. A., & Moerkerke, B. (2017). Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI. Frontiers in neuroscience, 11222. https://doi.org/10.3389/fnins.2017.00222
Degryse, Jasper, et al. "Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI." Frontiers in neuroscience vol. 11 (2017): 222. doi: https://doi.org/10.3389/fnins.2017.00222
Degryse J, Seurinck R, Durnez J, Gonzalez-Castillo J, Bandettini PA, Moerkerke B. Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI. Front Neurosci. 2017 Apr 21;11:222. doi: 10.3389/fnins.2017.00222. eCollection 2017. PMID: 28484367; PMCID: PMC5399022.
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Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI.

Journal of applied mathematics

Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A, Cox RW, Saad ZS.
PMID: 24415902
J Appl Math. 2013 May 21;2013. doi: 10.1155/2013/935154.

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance....

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Jo HJ, Gotts SJ, Reynolds RC, et al. Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI. J Appl Math. 2013;2013doi: 10.1155/2013/935154.
Jo, H. J., Gotts, S. J., Reynolds, R. C., Bandettini, P. A., Martin, A., Cox, R. W., & Saad, Z. S. (2013). Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI. Journal of applied mathematics, 2013. https://doi.org/10.1155/2013/935154
Jo, Hang Joon, et al. "Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI." Journal of applied mathematics vol. 2013 (2013). doi: https://doi.org/10.1155/2013/935154
Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A, Cox RW, Saad ZS. Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI. J Appl Math. 2013 May 21;2013. doi: 10.1155/2013/935154. PMID: 24415902; PMCID: PMC3886863.
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The effects of aerobic activity on brain structure.

Frontiers in psychology

Thomas AG, Dennis A, Bandettini PA, Johansen-Berg H.
PMID: 22470361
Front Psychol. 2012 Mar 23;3:86. doi: 10.3389/fpsyg.2012.00086. eCollection 2012.

Aerobic activity is a powerful stimulus for improving mental health and for generating structural changes in the brain. We review the literature documenting these structural changes and explore exactly where in the brain these changes occur as well as...

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Thomas AG, Dennis A, Bandettini PA, et al. The effects of aerobic activity on brain structure. Front Psychol. 2012;3:86doi: 10.3389/fpsyg.2012.00086.
Thomas, A. G., Dennis, A., Bandettini, P. A., & Johansen-Berg, H. (2012). The effects of aerobic activity on brain structure. Frontiers in psychology, 386. https://doi.org/10.3389/fpsyg.2012.00086
Thomas, Adam G, et al. "The effects of aerobic activity on brain structure." Frontiers in psychology vol. 3 (2012): 86. doi: https://doi.org/10.3389/fpsyg.2012.00086
Thomas AG, Dennis A, Bandettini PA, Johansen-Berg H. The effects of aerobic activity on brain structure. Front Psychol. 2012 Mar 23;3:86. doi: 10.3389/fpsyg.2012.00086. eCollection 2012. PMID: 22470361; PMCID: PMC3311131.
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Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation.

Frontiers in psychology

Mur M, Meys M, Bodurka J, Goebel R, Bandettini PA, Kriegeskorte N.
PMID: 23525516
Front Psychol. 2013 Mar 22;4:128. doi: 10.3389/fpsyg.2013.00128. eCollection 2013.

Primate inferior temporal (IT) cortex is thought to contain a high-level representation of objects at the interface between vision and semantics. This suggests that the perceived similarity of real-world objects might be predicted from the IT representation. Here we...

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Mur M, Meys M, Bodurka J, et al. Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation. Front Psychol. 2013;4:128doi: 10.3389/fpsyg.2013.00128.
Mur, M., Meys, M., Bodurka, J., Goebel, R., Bandettini, P. A., & Kriegeskorte, N. (2013). Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation. Frontiers in psychology, 4128. https://doi.org/10.3389/fpsyg.2013.00128
Mur, Marieke, et al. "Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation." Frontiers in psychology vol. 4 (2013): 128. doi: https://doi.org/10.3389/fpsyg.2013.00128
Mur M, Meys M, Bodurka J, Goebel R, Bandettini PA, Kriegeskorte N. Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation. Front Psychol. 2013 Mar 22;4:128. doi: 10.3389/fpsyg.2013.00128. eCollection 2013. PMID: 23525516; PMCID: PMC3605517.
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Holistic segmentation of the lung in cine MRI.

Journal of medical imaging (Bellingham, Wash.)

Kovacs W, Hsieh N, Roth H, Nnamdi-Emeratom C, Bandettini WP, Arai A, Mankodi A, Summers RM, Yao J.
PMID: 29226176
J Med Imaging (Bellingham). 2017 Oct;4(4):041310. doi: 10.1117/1.JMI.4.4.041310. Epub 2017 Nov 30.

Duchenne muscular dystrophy (DMD) is a childhood-onset neuromuscular disease that results in the degeneration of muscle, starting in the extremities, before progressing to more vital areas, such as the lungs. Respiratory failure and pneumonia due to respiratory muscle weakness...

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Kovacs W, Hsieh N, Roth H, et al. Holistic segmentation of the lung in cine MRI. J Med Imaging (Bellingham). 2017;4(4):041310doi: 10.1117/1.JMI.4.4.041310.
Kovacs, W., Hsieh, N., Roth, H., Nnamdi-Emeratom, C., Bandettini, W. P., Arai, A., Mankodi, A., Summers, R. M., & Yao, J. (2017). Holistic segmentation of the lung in cine MRI. Journal of medical imaging (Bellingham, Wash.), 4(4), 041310. https://doi.org/10.1117/1.JMI.4.4.041310
Kovacs, William, et al. "Holistic segmentation of the lung in cine MRI." Journal of medical imaging (Bellingham, Wash.) vol. 4,4 (2017): 041310. doi: https://doi.org/10.1117/1.JMI.4.4.041310
Kovacs W, Hsieh N, Roth H, Nnamdi-Emeratom C, Bandettini WP, Arai A, Mankodi A, Summers RM, Yao J. Holistic segmentation of the lung in cine MRI. J Med Imaging (Bellingham). 2017 Oct;4(4):041310. doi: 10.1117/1.JMI.4.4.041310. Epub 2017 Nov 30. PMID: 29226176; PMCID: PMC5707377.
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Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks.

Frontiers in neuroinformatics

McClure P, Rho N, Lee JA, Kaczmarzyk JR, Zheng CY, Ghosh SS, Nielson DM, Thomas AG, Bandettini P, Pereira F.
PMID: 31749693
Front Neuroinform. 2019 Oct 17;13:67. doi: 10.3389/fninf.2019.00067. eCollection 2019.

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (

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McClure P, Rho N, Lee JA, et al. Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks. Front Neuroinform. 2019;13:67doi: 10.3389/fninf.2019.00067.
McClure, P., Rho, N., Lee, J. A., Kaczmarzyk, J. R., Zheng, C. Y., Ghosh, S. S., Nielson, D. M., Thomas, A. G., Bandettini, P., & Pereira, F. (2019). Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks. Frontiers in neuroinformatics, 1367. https://doi.org/10.3389/fninf.2019.00067
McClure, Patrick, et al. "Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks." Frontiers in neuroinformatics vol. 13 (2019): 67. doi: https://doi.org/10.3389/fninf.2019.00067
McClure P, Rho N, Lee JA, Kaczmarzyk JR, Zheng CY, Ghosh SS, Nielson DM, Thomas AG, Bandettini P, Pereira F. Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks. Front Neuroinform. 2019 Oct 17;13:67. doi: 10.3389/fninf.2019.00067. eCollection 2019. PMID: 31749693; PMCID: PMC6843052.
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Distributed Weight Consolidation: A Brain Segmentation Case Study.

Advances in neural information processing systems

McClure P, Kaczmarzyk JR, Ghosh SS, Bandettini P, Zheng CY, Lee JA, Nielson D, Pereira F.
PMID: 34376963
Adv Neural Inf Process Syst. 2018 Dec;31:4093-4103.

Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case...

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McClure P, Kaczmarzyk JR, Ghosh SS, et al. Distributed Weight Consolidation: A Brain Segmentation Case Study. Adv Neural Inf Process Syst. 2018;31:4093-4103
McClure, P., Kaczmarzyk, J. R., Ghosh, S. S., Bandettini, P., Zheng, C. Y., Lee, J. A., Nielson, D., & Pereira, F. (2018). Distributed Weight Consolidation: A Brain Segmentation Case Study. Advances in neural information processing systems, 314093-4103.
McClure, Patrick, et al. "Distributed Weight Consolidation: A Brain Segmentation Case Study." Advances in neural information processing systems vol. 31 (2018): 4093-4103.
McClure P, Kaczmarzyk JR, Ghosh SS, Bandettini P, Zheng CY, Lee JA, Nielson D, Pereira F. Distributed Weight Consolidation: A Brain Segmentation Case Study. Adv Neural Inf Process Syst. 2018 Dec;31:4093-4103. PMID: 34376963; PMCID: PMC8351531.
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Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing.

NeuroImage

Yu Y, Huber L, Yang J, Fukunaga M, Chai Y, Jangraw DC, Chen G, Handwerker DA, Molfese PJ, Ejima Y, Sadato N, Wu J, Bandettini PA.
PMID: 34974114
Neuroimage. 2021 Dec 30;248:118867. doi: 10.1016/j.neuroimage.2021.118867. Epub 2021 Dec 30.

The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory...

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Yu Y, Huber L, Yang J, et al. Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing. Neuroimage. 2021;248:118867doi: 10.1016/j.neuroimage.2021.118867.
Yu, Y., Huber, L., Yang, J., Fukunaga, M., Chai, Y., Jangraw, D. C., Chen, G., Handwerker, D. A., Molfese, P. J., Ejima, Y., Sadato, N., Wu, J., & Bandettini, P. A. (2021). Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing. NeuroImage, 248118867. https://doi.org/10.1016/j.neuroimage.2021.118867
Yu, Yinghua, et al. "Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing." NeuroImage vol. 248 (2021): 118867. doi: https://doi.org/10.1016/j.neuroimage.2021.118867
Yu Y, Huber L, Yang J, Fukunaga M, Chai Y, Jangraw DC, Chen G, Handwerker DA, Molfese PJ, Ejima Y, Sadato N, Wu J, Bandettini PA. Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing. Neuroimage. 2021 Dec 30;248:118867. doi: 10.1016/j.neuroimage.2021.118867. Epub 2021 Dec 30. PMID: 34974114.
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Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing.

NeuroImage

Yu Y, Huber L, Yang J, Fukunaga M, Chai Y, Jangraw DC, Chen G, Handwerker DA, Molfese PJ, Ejima Y, Sadato N, Wu J, Bandettini PA.
PMID: 34974114
Neuroimage. 2021 Dec 30;118867. doi: 10.1016/j.neuroimage.2021.118867. Epub 2021 Dec 30.

The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory...

Cite
Yu Y, Huber L, Yang J, et al. Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing. Neuroimage. 2021;118867doi: 10.1016/j.neuroimage.2021.118867.
Yu, Y., Huber, L., Yang, J., Fukunaga, M., Chai, Y., Jangraw, D. C., Chen, G., Handwerker, D. A., Molfese, P. J., Ejima, Y., Sadato, N., Wu, J., & Bandettini, P. A. (2021). Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing. NeuroImage, 118867. https://doi.org/10.1016/j.neuroimage.2021.118867
Yu, Yinghua, et al. "Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing." NeuroImage vol. (2021): 118867. doi: https://doi.org/10.1016/j.neuroimage.2021.118867
Yu Y, Huber L, Yang J, Fukunaga M, Chai Y, Jangraw DC, Chen G, Handwerker DA, Molfese PJ, Ejima Y, Sadato N, Wu J, Bandettini PA. Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing. Neuroimage. 2021 Dec 30;118867. doi: 10.1016/j.neuroimage.2021.118867. Epub 2021 Dec 30. PMID: 34974114.
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Correction to: The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas.

Pediatric radiology

Molfese PJ, Glen D, Mesite L, Cox RW, Hoeft F, Frost SJ, Mencl WE, Pugh KR, Bandettini PA.
PMID: 33464361
Pediatr Radiol. 2021 Apr;51(4):671-672. doi: 10.1007/s00247-020-04958-w.

No abstract available.

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Molfese PJ, Glen D, Mesite L, et al. Correction to: The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas. Pediatr Radiol. 2021;51(4):671-672doi: 10.1007/s00247-020-04958-w.
Molfese, P. J., Glen, D., Mesite, L., Cox, R. W., Hoeft, F., Frost, S. J., Mencl, W. E., Pugh, K. R., & Bandettini, P. A. (2021). Correction to: The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas. Pediatric radiology, 51(4), 671-672. https://doi.org/10.1007/s00247-020-04958-w
Molfese, Peter J, et al. "Correction to: The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas." Pediatric radiology vol. 51,4 (2021): 671-672. doi: https://doi.org/10.1007/s00247-020-04958-w
Molfese PJ, Glen D, Mesite L, Cox RW, Hoeft F, Frost SJ, Mencl WE, Pugh KR, Bandettini PA. Correction to: The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas. Pediatr Radiol. 2021 Apr;51(4):671-672. doi: 10.1007/s00247-020-04958-w. PMID: 33464361.
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Antibiotic susceptibility of .

Journal of chemotherapy (Florence, Italy)

Lezzi M, Bandettini R, Ugolotti E, Saffioti C, Mesini A, Pastorino C, Manunza F, Ferretti M, Brisca G, Castagnola E.
PMID: 33272149
J Chemother. 2021 Oct;33(6):361-364. doi: 10.1080/1120009X.2020.1852832. Epub 2020 Dec 04.

Antibiotic susceptibility of

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Lezzi M, Bandettini R, Ugolotti E, et al. Antibiotic susceptibility of . J Chemother. 2020;33(6):361-364doi: 10.1080/1120009X.2020.1852832.
Lezzi, M., Bandettini, R., Ugolotti, E., Saffioti, C., Mesini, A., Pastorino, C., Manunza, F., Ferretti, M., Brisca, G., & Castagnola, E. (2021). Antibiotic susceptibility of . Journal of chemotherapy (Florence, Italy), 33(6), 361-364. https://doi.org/10.1080/1120009X.2020.1852832
Lezzi, Marilea, et al. "Antibiotic susceptibility of ." Journal of chemotherapy (Florence, Italy) vol. 33,6 (2021): 361-364. doi: https://doi.org/10.1080/1120009X.2020.1852832
Lezzi M, Bandettini R, Ugolotti E, Saffioti C, Mesini A, Pastorino C, Manunza F, Ferretti M, Brisca G, Castagnola E. Antibiotic susceptibility of . J Chemother. 2021 Oct;33(6):361-364. doi: 10.1080/1120009X.2020.1852832. Epub 2020 Dec 04. PMID: 33272149.
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Aortic pulse wave velocity in children with Cushing syndrome: A window into a marker of early cardiovascular disease.

Endocrinology, diabetes & metabolism

Blain H, Sinaii N, Zeltser D, Lyssikatos C, Belyavskaya E, Keil M, Bluemke DA, Stratakis C, Bandettini WP, Lodish M.
PMID: 31008362
Endocrinol Diabetes Metab. 2019 Jan 15;2(2):e00054. doi: 10.1002/edm2.54. eCollection 2019 Apr.

OBJECTIVE: To investigate early signs of cardiovascular arterial remodelling in paediatric patients with Cushing syndrome (CS) in comparison with normative values from healthy children.STUDY DESIGN: The metrics used to assess cardiac health were from thoracic aorta and carotid MRI....

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Blain H, Sinaii N, Zeltser D, et al. Aortic pulse wave velocity in children with Cushing syndrome: A window into a marker of early cardiovascular disease. Endocrinol Diabetes Metab. 2019;2(2):e00054doi: 10.1002/edm2.54.
Blain, H., Sinaii, N., Zeltser, D., Lyssikatos, C., Belyavskaya, E., Keil, M., Bluemke, D. A., Stratakis, C., Bandettini, W. P., & Lodish, M. (2019). Aortic pulse wave velocity in children with Cushing syndrome: A window into a marker of early cardiovascular disease. Endocrinology, diabetes & metabolism, 2(2), e00054. https://doi.org/10.1002/edm2.54
Blain, Hailey, et al. "Aortic pulse wave velocity in children with Cushing syndrome: A window into a marker of early cardiovascular disease." Endocrinology, diabetes & metabolism vol. 2,2 (2019): e00054. doi: https://doi.org/10.1002/edm2.54
Blain H, Sinaii N, Zeltser D, Lyssikatos C, Belyavskaya E, Keil M, Bluemke DA, Stratakis C, Bandettini WP, Lodish M. Aortic pulse wave velocity in children with Cushing syndrome: A window into a marker of early cardiovascular disease. Endocrinol Diabetes Metab. 2019 Jan 15;2(2):e00054. doi: 10.1002/edm2.54. eCollection 2019 Apr. PMID: 31008362; PMCID: PMC6458483.
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Showing 1 to 12 of 53 entries