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19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium.

Neurology. Genetics

[No authors listed]
PMID: 28428977
Neurol Genet. 2017 Mar 30;3(1):S2-S11. doi: 10.1212/NXG.0000000000000100. eCollection 2017 Mar.

No abstract available.

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19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium. Neurol Genet. 2017;3(1):S2-S11doi: 10.1212/NXG.0000000000000100.
(2017). 19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium. Neurology. Genetics, 3(1), S2-S11. https://doi.org/10.1212/NXG.0000000000000100
"19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium." Neurology. Genetics vol. 3,1 (2017): S2-S11. doi: https://doi.org/10.1212/NXG.0000000000000100
19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium. Neurol Genet. 2017 Mar 30;3(1):S2-S11. doi: 10.1212/NXG.0000000000000100. eCollection 2017 Mar. PMID: 28428977; PMCID: PMC5390739.
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Optical coherence tomography visualizes neurons in human entorhinal cortex.

Neurophotonics

Magnain C, Augustinack JC, Konukoglu E, Frosch MP, Sakadžić S, Varjabedian A, Garcia N, Wedeen VJ, Boas DA, Fischl B.
PMID: 25741528
Neurophotonics. 2015 Feb 09;2(1):015004. doi: 10.1117/1.NPh.2.1.015004.

The cytoarchitecture of the human brain is of great interest in diverse fields: neuroanatomy, neurology, neuroscience, and neuropathology. Traditional histology is a method that has been historically used to assess cell and fiber content in the

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Magnain C, Augustinack JC, Konukoglu E, et al. Optical coherence tomography visualizes neurons in human entorhinal cortex. Neurophotonics. 2015;2(1):015004doi: 10.1117/1.NPh.2.1.015004.
Magnain, C., Augustinack, J. C., Konukoglu, E., Frosch, M. P., Sakadžić, S., Varjabedian, A., Garcia, N., Wedeen, V. J., Boas, D. A., & Fischl, B. (2015). Optical coherence tomography visualizes neurons in human entorhinal cortex. Neurophotonics, 2(1), 015004. https://doi.org/10.1117/1.NPh.2.1.015004
Magnain, Caroline, et al. "Optical coherence tomography visualizes neurons in human entorhinal cortex." Neurophotonics vol. 2,1 (2015): 015004. doi: https://doi.org/10.1117/1.NPh.2.1.015004
Magnain C, Augustinack JC, Konukoglu E, Frosch MP, Sakadžić S, Varjabedian A, Garcia N, Wedeen VJ, Boas DA, Fischl B. Optical coherence tomography visualizes neurons in human entorhinal cortex. Neurophotonics. 2015 Feb 09;2(1):015004. doi: 10.1117/1.NPh.2.1.015004. PMID: 25741528; PMCID: PMC4346095.
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Epidemiology of Stroke: Legacy of the Framingham Heart Study.

Global heart

Romero JR, Wolf PA.
PMID: 23527318
Glob Heart. 2013 Mar 01;8(1):67-75. doi: 10.1016/j.gheart.2012.12.007. Epub 2013 Mar 15.

In the present historical review, we highlight several articles outlining contributions of the Framingham Heart Study over the span of nearly seven decades to our understanding of the epidemiology of blood pressure (BP), atrial fibrillation and genetic factors as...

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Romero JR, Wolf PA. Epidemiology of Stroke: Legacy of the Framingham Heart Study. Glob Heart. 2013;8(1):67-75doi: 10.1016/j.gheart.2012.12.007.
Romero, J. R., & Wolf, P. A. (2013). Epidemiology of Stroke: Legacy of the Framingham Heart Study. Global heart, 8(1), 67-75. https://doi.org/10.1016/j.gheart.2012.12.007
Romero, Jose R, and Wolf, Philip A. "Epidemiology of Stroke: Legacy of the Framingham Heart Study." Global heart vol. 8,1 (2013): 67-75. doi: https://doi.org/10.1016/j.gheart.2012.12.007
Romero JR, Wolf PA. Epidemiology of Stroke: Legacy of the Framingham Heart Study. Glob Heart. 2013 Mar 01;8(1):67-75. doi: 10.1016/j.gheart.2012.12.007. Epub 2013 Mar 15. PMID: 23527318; PMCID: PMC3601756.
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Aging Well: Using Precision to Drive Down Costs and Increase Health Quality.

Advances in geriatric medicine and research

Au R, Ritchie M, Hardy S, Ang TFA, Lin H.
PMID: 31342014
Adv Geriatr Med Res. 2019;1. doi: 10.20900/agmr20190003. Epub 2019 Jun 05.

Efforts to provide patients with individualized treatments have led to tremendous breakthroughs in healthcare. However, a precision medicine approach alone will not offset the rapid increase in prevalence and burden of chronic non-communicable illnesses that is continuing to pervade...

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Au R, Ritchie M, Hardy S, et al. Aging Well: Using Precision to Drive Down Costs and Increase Health Quality. Adv Geriatr Med Res. 2019;1doi: 10.20900/agmr20190003.
Au, R., Ritchie, M., Hardy, S., Ang, T. F. A., & Lin, H. (2019). Aging Well: Using Precision to Drive Down Costs and Increase Health Quality. Advances in geriatric medicine and research, 1. https://doi.org/10.20900/agmr20190003
Au, Rhoda, et al. "Aging Well: Using Precision to Drive Down Costs and Increase Health Quality." Advances in geriatric medicine and research vol. 1 (2019). doi: https://doi.org/10.20900/agmr20190003
Au R, Ritchie M, Hardy S, Ang TFA, Lin H. Aging Well: Using Precision to Drive Down Costs and Increase Health Quality. Adv Geriatr Med Res. 2019;1. doi: 10.20900/agmr20190003. Epub 2019 Jun 05. PMID: 31342014; PMCID: PMC6656386.
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A Fast Approach to Automatic Detection of Brain Lesions.

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)

Koley S, Chakraborty C, Mainero C, Fischl B, Aganj I.
PMID: 29082383
Brainlesion. 2016;10154:52-61. doi: 10.1007/978-3-319-55524-9_6. Epub 2017 Apr 12.

Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates...

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Koley S, Chakraborty C, Mainero C, et al. A Fast Approach to Automatic Detection of Brain Lesions. Brainlesion. 2017;10154:52-61doi: 10.1007/978-3-319-55524-9_6.
Koley, S., Chakraborty, C., Mainero, C., Fischl, B., & Aganj, I. (2016). A Fast Approach to Automatic Detection of Brain Lesions. Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop), 1015452-61. https://doi.org/10.1007/978-3-319-55524-9_6
Koley, Subhranil, et al. "A Fast Approach to Automatic Detection of Brain Lesions." Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) vol. 10154 (2016): 52-61. doi: https://doi.org/10.1007/978-3-319-55524-9_6
Koley S, Chakraborty C, Mainero C, Fischl B, Aganj I. A Fast Approach to Automatic Detection of Brain Lesions. Brainlesion. 2016;10154:52-61. doi: 10.1007/978-3-319-55524-9_6. Epub 2017 Apr 12. PMID: 29082383; PMCID: PMC5654618.
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Differential gene expression in the cortical sulcus compared to the gyral crest within the early stages of chronic traumatic encephalopathy.

Free neuropathology

Cherry JD, Agus F, Dixon E, Huber B, Alvarez VE, Mez J, McKee AC, Labadorf A, Stein TD.
PMID: 34485990
Free Neuropathol. 2021;2. doi: 10.17879/freeneuropathology-2021-3453. Epub 2021 Aug 17.

Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative tauopathy found in individuals with a history of repetitive head impacts (RHI). Previous work has demonstrated that neuroinflammation is involved in CTE pathogenesis, however, the specific inflammatory mechanisms are still unclear....

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Cherry JD, Agus F, Dixon E, et al. Differential gene expression in the cortical sulcus compared to the gyral crest within the early stages of chronic traumatic encephalopathy. Free Neuropathol. 2021;2doi: 10.17879/freeneuropathology-2021-3453.
Cherry, J. D., Agus, F., Dixon, E., Huber, B., Alvarez, V. E., Mez, J., McKee, A. C., Labadorf, A., & Stein, T. D. (2021). Differential gene expression in the cortical sulcus compared to the gyral crest within the early stages of chronic traumatic encephalopathy. Free neuropathology, 2. https://doi.org/10.17879/freeneuropathology-2021-3453
Cherry, Jonathan D, et al. "Differential gene expression in the cortical sulcus compared to the gyral crest within the early stages of chronic traumatic encephalopathy." Free neuropathology vol. 2 (2021). doi: https://doi.org/10.17879/freeneuropathology-2021-3453
Cherry JD, Agus F, Dixon E, Huber B, Alvarez VE, Mez J, McKee AC, Labadorf A, Stein TD. Differential gene expression in the cortical sulcus compared to the gyral crest within the early stages of chronic traumatic encephalopathy. Free Neuropathol. 2021;2. doi: 10.17879/freeneuropathology-2021-3453. Epub 2021 Aug 17. PMID: 34485990; PMCID: PMC8415801.
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Identification of digital voice biomarkers for cognitive health.

Exploration of medicine

Lin H, Karjadi C, Ang TFA, Prajakta J, McManus C, Alhanai TW, Glass J, Au R.
PMID: 33665648
Explor Med. 2020;1:406-417. doi: 10.37349/emed.2020.00028. Epub 2020 Dec 31.

AIM: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia.METHODS: Participants...

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Lin H, Karjadi C, Ang TFA, et al. Identification of digital voice biomarkers for cognitive health. Explor Med. 2020;1:406-417doi: 10.37349/emed.2020.00028.
Lin, H., Karjadi, C., Ang, T. F. A., Prajakta, J., McManus, C., Alhanai, T. W., Glass, J., & Au, R. (2020). Identification of digital voice biomarkers for cognitive health. Exploration of medicine, 1406-417. https://doi.org/10.37349/emed.2020.00028
Lin, Honghuang, et al. "Identification of digital voice biomarkers for cognitive health." Exploration of medicine vol. 1 (2020): 406-417. doi: https://doi.org/10.37349/emed.2020.00028
Lin H, Karjadi C, Ang TFA, Prajakta J, McManus C, Alhanai TW, Glass J, Au R. Identification of digital voice biomarkers for cognitive health. Explor Med. 2020;1:406-417. doi: 10.37349/emed.2020.00028. Epub 2020 Dec 31. PMID: 33665648; PMCID: PMC7929495.
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A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images.

NeuroImage

Greve DN, Billot B, Cordero D, Hoopes A, Hoffmann M, Dalca AV, Fischl B, Iglesias JE, Augustinack JC.
PMID: 34571161
Neuroimage. 2021 Dec 01;244:118610. doi: 10.1016/j.neuroimage.2021.118610. Epub 2021 Sep 25.

A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, and fornix) using only a T1-weighted MRI as input. This tool fills an unmet need...

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Greve DN, Billot B, Cordero D, et al. A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images. Neuroimage. 2021;244:118610doi: 10.1016/j.neuroimage.2021.118610.
Greve, D. N., Billot, B., Cordero, D., Hoopes, A., Hoffmann, M., Dalca, A. V., Fischl, B., Iglesias, J. E., & Augustinack, J. C. (2021). A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images. NeuroImage, 244118610. https://doi.org/10.1016/j.neuroimage.2021.118610
Greve, Douglas N, et al. "A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images." NeuroImage vol. 244 (2021): 118610. doi: https://doi.org/10.1016/j.neuroimage.2021.118610
Greve DN, Billot B, Cordero D, Hoopes A, Hoffmann M, Dalca AV, Fischl B, Iglesias JE, Augustinack JC. A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images. Neuroimage. 2021 Dec 01;244:118610. doi: 10.1016/j.neuroimage.2021.118610. Epub 2021 Sep 25. PMID: 34571161; PMCID: PMC8643077.
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COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Aganj I, Frau-Pascual A, Iglesias JE, Yendiki A, Augustinack JC, Salat DH, Fischl B.
PMID: 32587665
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:283-287. doi: 10.1109/ISBI45749.2020.9098440. Epub 2020 May 22.

Identification of the specific brain networks that are vulnerable or resilient in neurodegenerative diseases can help to better understand the disease effects and derive new connectomic imaging biomarkers. In this work, we use brain connectivity to find pairs of...

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Aganj I, Frau-Pascual A, Iglesias JE, et al. COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2020;2020:283-287doi: 10.1109/ISBI45749.2020.9098440.
Aganj, I., Frau-Pascual, A., Iglesias, J. E., Yendiki, A., Augustinack, J. C., Salat, D. H., & Fischl, B. (2020). COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2020283-287. https://doi.org/10.1109/ISBI45749.2020.9098440
Aganj, Iman, et al. "COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2020 (2020): 283-287. doi: https://doi.org/10.1109/ISBI45749.2020.9098440
Aganj I, Frau-Pascual A, Iglesias JE, Yendiki A, Augustinack JC, Salat DH, Fischl B. COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:283-287. doi: 10.1109/ISBI45749.2020.9098440. Epub 2020 May 22. PMID: 32587665; PMCID: PMC7316404.
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COVID-19 Outpatient Screening: a Prediction Score for Adverse Events.

medRxiv : the preprint server for health sciences

Sun H, Jain A, Leone MJ, Alabsi HS, Brenner LN, Ye E, Ge W, Shao YP, Boutros CL, Wang R, Tesh RA, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu JT, Dougan M, Stratton L, Rosand J, Fischl B, Das S, Mukerji SS, Robbins GK, Westover MB.
PMID: 32607523
medRxiv. 2020 Jun 22; doi: 10.1101/2020.06.17.20134262.

BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak.METHODS: Single-center study of adult outpatients seen in respiratory illness clinics...

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Sun H, Jain A, Leone MJ, et al. COVID-19 Outpatient Screening: a Prediction Score for Adverse Events. medRxiv. 2020;doi: 10.1101/2020.06.17.20134262.
Sun, H., Jain, A., Leone, M. J., Alabsi, H. S., Brenner, L. N., Ye, E., Ge, W., Shao, Y. P., Boutros, C. L., Wang, R., Tesh, R. A., Magdamo, C., Collens, S. I., Ganglberger, W., Bassett, I. V., Meigs, J. B., Kalpathy-Cramer, J., Li, M. D., Chu, J. T., Dougan, M., Stratton, L., Rosand, J., Fischl, B., Das, S., Mukerji, S. S., Robbins, G. K., & Westover, M. B. (2020). COVID-19 Outpatient Screening: a Prediction Score for Adverse Events. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.06.17.20134262
Sun, Haoqi, et al. "COVID-19 Outpatient Screening: a Prediction Score for Adverse Events." medRxiv : the preprint server for health sciences vol. (2020). doi: https://doi.org/10.1101/2020.06.17.20134262
Sun H, Jain A, Leone MJ, Alabsi HS, Brenner LN, Ye E, Ge W, Shao YP, Boutros CL, Wang R, Tesh RA, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu JT, Dougan M, Stratton L, Rosand J, Fischl B, Das S, Mukerji SS, Robbins GK, Westover MB. COVID-19 Outpatient Screening: a Prediction Score for Adverse Events. medRxiv. 2020 Jun 22; doi: 10.1101/2020.06.17.20134262. PMID: 32607523; PMCID: PMC7325189.
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Expected Label Value Computation for Atlas-Based Image Segmentation.

Proceedings. IEEE International Symposium on Biomedical Imaging

Aganj I, Fischl B.
PMID: 31341547
Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:334-338. doi: 10.1109/ISBI.2019.8759484. Epub 2019 Jul 11.

The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We...

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Aganj I, Fischl B. Expected Label Value Computation for Atlas-Based Image Segmentation. Proc IEEE Int Symp Biomed Imaging. 2019;2019:334-338doi: 10.1109/ISBI.2019.8759484.
Aganj, I., & Fischl, B. (2019). Expected Label Value Computation for Atlas-Based Image Segmentation. Proceedings. IEEE International Symposium on Biomedical Imaging, 2019334-338. https://doi.org/10.1109/ISBI.2019.8759484
Aganj, Iman, and Fischl, Bruce. "Expected Label Value Computation for Atlas-Based Image Segmentation." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2019 (2019): 334-338. doi: https://doi.org/10.1109/ISBI.2019.8759484
Aganj I, Fischl B. Expected Label Value Computation for Atlas-Based Image Segmentation. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:334-338. doi: 10.1109/ISBI.2019.8759484. Epub 2019 Jul 11. PMID: 31341547; PMCID: PMC6656371.
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Back to the future: Alzheimer's disease heterogeneity revisited.

Alzheimer's & dementia (Amsterdam, Netherlands)

Au R, Piers RJ, Lancashire L.
PMID: 27077132
Alzheimers Dement (Amst). 2015 Sep;1(3):368-370. doi: 10.1016/j.dadm.2015.05.006.

No abstract available.

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Au R, Piers RJ, Lancashire L. Back to the future: Alzheimer's disease heterogeneity revisited. Alzheimers Dement (Amst). 2015;1(3):368-370doi: 10.1016/j.dadm.2015.05.006.
Au, R., Piers, R. J., & Lancashire, L. (2015). Back to the future: Alzheimer's disease heterogeneity revisited. Alzheimer's & dementia (Amsterdam, Netherlands), 1(3), 368-370. https://doi.org/10.1016/j.dadm.2015.05.006
Au, Rhoda, et al. "Back to the future: Alzheimer's disease heterogeneity revisited." Alzheimer's & dementia (Amsterdam, Netherlands) vol. 1,3 (2015): 368-370. doi: https://doi.org/10.1016/j.dadm.2015.05.006
Au R, Piers RJ, Lancashire L. Back to the future: Alzheimer's disease heterogeneity revisited. Alzheimers Dement (Amst). 2015 Sep;1(3):368-370. doi: 10.1016/j.dadm.2015.05.006. PMID: 27077132; PMCID: PMC4827150.
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Showing 1 to 12 of 52 entries