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1. Integrated multimodal cell atlas of Alzheimer’s disease

Author

Gabitto, Mariano I., Travaglini, Kyle J., Rachleff, Victoria M., Kaplan, Eitan S., Long, Brian, Ariza, Jeanelle, Ding, Yi, Mahoney, Joseph T., Dee, Nick, Goldy, Jeff, Melief, Erica J., Agrawal, Anamika, Kana, Omar, Zhen, Xingjian, Barlow, Samuel T., Brouner, Krissy, Campos, Jazmin, Campos, John, Carr, Ambrose J., Casper, Tamara, Chakrabarty, Rushil, Clark, Michael, Cool, Jonah, Dalley, Rachel, Darvas, Martin, Ding, Song-Lin, Dolbeare, Tim, Egdorf, Tom, Esposito, Luke, Ferrer, Rebecca, Fleckenstein, Lynn E., Gala, Rohan, Gary, Amanda, Gelfand, Emily, Gloe, Jessica, Guilford, Nathan, Guzman, Junitta, Hirschstein, Daniel, Ho, Windy, Hupp, Madison, Jarsky, Tim, Johansen, Nelson, Kalmbach, Brian E., Keene, Lisa M., Khawand, Sarah, Kilgore, Mitchell D., Kirkland, Amanda, Kunst, Michael, Lee, Brian R., Leytze, Mckaila, Mac Donald, Christine L., Malone, Jocelin, Maltzer, Zoe, Martin, Naomi, McCue, Rachel, McMillen, Delissa, Mena, Gonzalo, Meyerdierks, Emma, Meyers, Kelly P., Mollenkopf, Tyler, Montine, Mark, Nolan, Amber L., Nyhus, Julie K., Olsen, Paul A., Pacleb, Maiya, Pagan, Chelsea M., Peña, Nicholas, Pham, Trangthanh, Pom, Christina Alice, Postupna, Nadia, Rimorin, Christine, Ruiz, Augustin, Saldi, Giuseppe A., Schantz, Aimee M., Shapovalova, Nadiya V., Sorensen, Staci A., Staats, Brian, Sullivan, Matt, Sunkin, Susan M., Thompson, Carol, Tieu, Michael, Ting, Jonathan T., Torkelson, Amy, Tran, Tracy, Valera Cuevas, Nasmil J., Walling-Bell, Sarah, Wang, Ming-Qiang, Waters, Jack, Wilson, Angela M., Xiao, Ming, Haynor, David, Gatto, Nicole M., Jayadev, Suman, Mufti, Shoaib, Ng, Lydia, Mukherjee, Shubhabrata, Crane, Paul K., Latimer, Caitlin S., Levi, Boaz P., Smith, Kimberly A., Close, Jennie L., Miller, Jeremy A., Hodge, Rebecca D., Larson, Eric B., Grabowski, Thomas J., Hawrylycz, Michael, Keene, C. Dirk, and Lein, Ed S.

Published
2024
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2. Association of day-of-injury plasma glial fibrillary acidic protein concentration and six-month posttraumatic stress disorder in patients with mild traumatic brain injury

Author

Kulbe, Jacqueline R, Jain, Sonia, Nelson, Lindsay D, Korley, Frederick K, Mukherjee, Pratik, Sun, Xiaoying, Okonkwo, David O, Giacino, Joseph T, Vassar, Mary J, Robertson, Claudia S, McCrea, Michael A, Wang, Kevin KW, Temkin, Nancy, Mac Donald, Christine L, Taylor, Sabrina R, Ferguson, Adam R, Markowitz, Amy J, Diaz-Arrastia, Ramon, Manley, Geoffrey T, and Stein, Murray B

Subjects
Brain Disorders, Clinical Research, Physical Injury - Accidents and Adverse Effects, Neurosciences, Traumatic Brain Injury (TBI), Post-Traumatic Stress Disorder (PTSD), Traumatic Head and Spine Injury, Humans, Glial Fibrillary Acidic Protein, Brain Concussion, Stress Disorders, Post-Traumatic, Prospective Studies, Longitudinal Studies, C-Reactive Protein, Brain Injuries, Traumatic, Biomarkers, TRACK-TBI Investigators, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Several proteins have proven useful as blood-based biomarkers to assist in evaluation and management of traumatic brain injury (TBI). The objective of this study was to determine whether two day-of-injury blood-based biomarkers are predictive of posttraumatic stress disorder (PTSD). We used data from 1143 individuals with mild TBI (mTBI; defined as admission Glasgow Coma Scale [GCS] score 13-15) enrolled in TRACK-TBI, a prospective longitudinal study of level 1 trauma center patients. Plasma glial fibrillary acidic protein (GFAP) and serum high sensitivity C-reactive protein (hsCRP) were measured from blood collected within 24 h of injury. Two hundred and twenty-seven (19.9% of) patients had probable PTSD (PCL-5 score ≥ 33) at 6 months post-injury. GFAP levels were positively associated (Spearman's rho = 0.35, p

Published
2022

3. Prognostic Value of Hemorrhagic Brainstem Injury on Early Computed Tomography: A TRACK-TBI Study.

Author

Williams, John R, Nieblas-Bedolla, Edwin, Feroze, Abdullah, Young, Christopher, Temkin, Nancy R, Giacino, Joseph T, Okonkwo, David O, Manley, Geoffrey T, Barber, Jason, Durfy, Sharon, Markowitz, Amy J, Yuh, Esther L, Mukherjee, Pratik, Mac Donald, Christine L, and and The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Investigators

Subjects
and The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Investigators, Brain Stem, Humans, Tomography, X-Ray Computed, Prognosis, Glasgow Coma Scale, Retrospective Studies, Prospective Studies, Brain Injuries, Traumatic, Brainstem injury, Computed tomography, Outcomes, Traumatic axonal injury, Traumatic brain injury, Biomedical Imaging, Neurosciences, Physical Injury - Accidents and Adverse Effects, Traumatic Head and Spine Injury, Brain Disorders, Traumatic Brain Injury (TBI), Injuries and accidents, Clinical Sciences, Neurology & Neurosurgery
Abstract

BackgroundTraumatic brainstem injury has yet to be incorporated into widely used imaging classification systems for traumatic brain injury (TBI), and questions remain regarding prognostic implications for this TBI subgroup. To address this, retrospective data on patients from the multicenter prospective Transforming Research and Clinical Knowledge in TBI study were studied.MethodsPatients with brainstem and cerebrum injury (BSI+) were matched by age, sex, and admission Glasgow Coma Scale (GCS) score to patients with cerebrum injuries only. All patients had an interpretable head computed tomography (CT) scan from the first 48 hours after injury and a 6-month Glasgow Outcome Scale Extended (GOSE) score. CT scans were reviewed for brainstem lesions and, when present, characterized by location, size, and type (traumatic axonal injury, contusion, or Duret hemorrhage). Clinical, demographic, and outcome data were then compared between the two groups.ResultsMann-Whitney U-tests showed no significant difference in 6-month GOSE scores in patients with BSI+ (mean 2.7) compared with patients with similar but only cerebrum injuries (mean 3.9), although there is a trend (p = 0.10). However, subclassification by brainstem lesion type, traumatic axonal injury (mean 4.0) versus Duret hemorrhage or contusion (mean 1.4), did identify a proportion of BSI+ with significantly less favorable outcome (p = 0.002). The incorporation of brainstem lesion type (traumatic axonal injury vs. contusion/Duret), along with GCS into a multivariate logistic regression model of favorable outcome (GOSE score 4-8) did show a significant contribution to the prognostication of this brainstem injury subgroup (odds ratio 0.08, 95% confidence interval 0.00-0.67, p = 0.01).ConclusionsThese findings suggest two groups of patients with brainstem injuries may exist with divergent recovery potential after TBI. These data support the notion that newer CT imaging classification systems may augment traditional clinical measures, such as GCS in identifying those patients with TBI and brainstem injuries that stand a higher chance of favorable outcome.

Published
2021

4. Tractography-Pathology Correlations in Traumatic Brain Injury: A TRACK-TBI Study

Author

Nolan, Amber L, Petersen, Cathrine, Iacono, Diego, Mac Donald, Christine L, Mukherjee, Pratik, van der Kouwe, Andre, Jain, Sonia, Stevens, Allison, Diamond, Bram R, Wang, Ruopeng, Markowitz, Amy J, Fischl, Bruce, Perl, Daniel P, Manley, Geoffrey T, Keene, C Dirk, Diaz-Arrastia, Ramon, Edlow, Brian L, Adeoye, Opeolu, Badjatia, Neeraj, Boase, Kim, Barber, Jason, Bodien, Yelena, Bullock, M Ross, Chesnut, Randall, Corrigan, John D, Crawford, Karen, Dikmen, Sureyya, Duhaime, Ann-Christine, Ellenbogen, Richard, Feeser, V Ramana, Ferguson, Adam R, Foreman, Brandon, Gardner, Raquel, Gaudette, Etienne, Giacino, Joseph, Goldman, Dana, Gonzalez, Luis, Gopinath, Shankar, Gullapalli, Rao, Hemphill, J Claude, Hotz, Gillian, Korley, Frederick K, Kramer, Joel, Kreitzer, Natalie, Levin, Harvey, Lindsell, Chris, Machamer, Joan, Madden, Christopher, Martin, Alastair, McAllister, Thomas, McCrea, Michael, Merchant, Randall, Nelson, Lindsay, Ngwenya, Laura B, Noel, Florence, Okonkwo, David, Palacios, Eva, Puccio, Ava, Rabinowitz, Miri, Robertson, Claudia, Rosand, Jonathan, Sander, Angelle, Satris, Gabriella, Schnyer, David, Seabury, Seth, Sherer, Mark, Stein, Murray, Taylor, Sabrina, Temkin, Nancy, Toga, Arthur, Valadka, Alex, Vassar, Mary, Vespa, Paul, Wang, Kevin, Yue, John K, Yuh, Esther, and Zafonte, Ross

Subjects
Biomedical Imaging, Physical Injury - Accidents and Adverse Effects, Acquired Cognitive Impairment, Neurosciences, Traumatic Brain Injury (TBI), Traumatic Head and Spine Injury, Brain Disorders, Aetiology, 2.1 Biological and endogenous factors, Neurological, Brain Injuries, Traumatic, Connectome, Diffusion Tensor Imaging, Humans, Male, Middle Aged, Neural Pathways, contusion, MRI, neuropathology, tractography, traumatic axonal injury, traumatic brain injury, TRACK-TBI Investigators, Clinical Sciences, Neurology & Neurosurgery
Abstract

Diffusion tractography magnetic resonance imaging (MRI) can infer changes in network connectivity in patients with traumatic brain injury (TBI), but the pathological substrates of disconnected tracts have not been well defined because of a lack of high-resolution imaging with histopathological validation. We developed an ex vivo MRI protocol to analyze tract terminations at 750-μm isotropic resolution, followed by histopathological evaluation of white matter pathology, and applied these methods to a 60-year-old man who died 26 days after TBI. Analysis of 74 cerebral hemispheric white matter regions revealed a heterogeneous distribution of tract disruptions. Associated histopathology identified variable white matter injury with patchy deposition of amyloid precursor protein (APP), loss of neurofilament-positive axonal processes, myelin dissolution, astrogliosis, microgliosis, and perivascular hemosiderin-laden macrophages. Multiple linear regression revealed that tract disruption strongly correlated with the density of APP-positive axonal swellings and neurofilament loss. Ex vivo diffusion MRI can detect tract disruptions in the human brain that reflect axonal injury.

Published
2021

5. Protocol for the Systematic Fixation, Circuit-Based Sampling, and Qualitative and Quantitative Neuropathological Analysis of Human Brain Tissue

Author

Latimer, Caitlin S., primary, Melief, Erica J., additional, Ariza-Torres, Jeanelle, additional, Howard, Kim, additional, Keen, Amanda R., additional, Keene, Lisa M., additional, Schantz, Aimee M., additional, Sytsma, Trevor M., additional, Wilson, Angela M., additional, Grabowski, Thomas J., additional, Darvas, Martin, additional, O’Connor, Kristen Dams, additional, Nolan, Amber L., additional, Edlow, Brian L., additional, Mac Donald, Christine L., additional, and Keene, C. Dirk, additional

Published
2022
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6. Environmental Enrichment After Primary Glioma Radiation Therapy.

Author

Figuracion, Karl Cristie F., Mac Donald, Christine L., Hunt, David, McGranahan, Tresa Michelle, Lewis, Frances Marcus, Rockhill, Jason, Goldberg, Myron, Halasz, Lia, and Thompson, Hilaire J.

Abstract

OBJECTIVES: To explore and describe the health outcomes and levels of environmental enrichment (EE) in participants with brain tumors after radiation therapy (RT). SAMPLE & SETTING: 39 participants with glioma (grades 2–3) treated with RT were recruited within five years of original diagnosis from a neuro-oncology clinic. METHODS & VARIABLES: A cross-sectional design was employed. EE measures included social connectedness, physical activity, employment, and financial stability. Health outcome measures included the Montreal Cognitive Assessment, Symbol Digit Modalities Test, global cortical atrophy rate scale, Karnofsky Performance Status Scale, and MD Anderson Symptom Inventory–Brain Tumor Module. Descriptive statistics characterized the health outcomes between participants who received photon RT and proton beam RT. RESULTS: Participants who received photon RT with high EE showed higher scores on the Montreal Cognitive Assessment, Symbol Digit Modalities Test, and Karnofsky Performance Status Scale, and lower scores on the global cortical atrophy rate scale and MD Anderson Symptom Inventory–Brain Tumor Module. IMPLICATIONS FOR NURSING: EE may reduce symptom burden, promote healthy brain aging, and improve cognitive and functional status in glioma survivors receiving photon RT. [ABSTRACT FROM AUTHOR]

Published
2025
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7. The Traumatic Brain Injury Endpoints Development (TED) Initiative: Progress on a Public-Private Regulatory Collaboration To Accelerate Diagnosis and Treatment of Traumatic Brain Injury

Author

Manley, Geoffrey T, Mac Donald, Christine L, Markowitz, Amy J, Stephenson, Diane, Robbins, Ann, Gardner, Raquel C, Winkler, Ethan, Bodien, Yelena G, Taylor, Sabrina R, Yue, John K, Kannan, Lakshmi, Kumar, Allison, McCrea, Michael A, Wang, Kevin K, and the TED Investigators

Subjects
Physical Injury - Accidents and Adverse Effects, Traumatic Brain Injury (TBI), Traumatic Head and Spine Injury, Clinical Research, Brain Disorders, Neurosciences, Clinical Trials and Supportive Activities, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Injuries and accidents, biomarkers, clinical outcome measures, CT, FDA, MRI, traumatic brain injury, TED Investigators, Clinical Sciences, Neurology & Neurosurgery
Abstract

The Traumatic Brain Injury Endpoints Development (TED) Initiative is a 5-year, Department of Defense-funded project that is working toward the ultimate goal of developing better designed clinical trials, leading to more precise diagnosis, and effective treatments for traumatic brain injury (TBI). TED is comprised of leading academic clinician-scientists, along with innovative industry leaders in biotechnology and imaging technology, patient advocacy organizations, and philanthropists, working collaboratively with regulatory authorities, specifically the U.S. Food and Drug Administration (FDA). The goals of the TED Initiative are to gain consensus and validation of TBI clinical outcome assessment measures and biomarkers for endorsement by global regulatory agencies for use in drug and device development processes. This article summarizes the Initiative's Stage I progress over the first 18 months, including intensive engagement with a number of FDA divisions responsible for review and validation of biomarkers and clinical outcome assessments, progression into the prequalification phase of the FDA's Medical Device Development Tool program for a candidate set of neuroimaging biomarkers, and receipt of the FDA's Recognition of Research Importance Letter and a Letter of Support regarding TBI. Other signal achievements relate to the creation of the TED Metadataset, harmonizing study measures across eight major TBI studies, and the leadership role played by TED investigators in the conversion of the NINDS TBI Common Data Elements to Clinical Data Interchange Standards Consortium standards. This article frames both the near-term expectations and the Initiative's long-term vision to accelerate approval of treatments for patients affected by TBI in urgent need of effective therapies.

Published
2017

9. Smaller Regional Brain Volumes Predict Posttraumatic Stress Disorder at 3 Months After Mild Traumatic Brain Injury

Author

Adeoye, Opeolu, Badjatia, Neeraj, Boase, Kim, Barber, Jason, Bodien, Yelena, Bullock, M. Ross, Chesnut, Randall, Corrigan, John D., Crawford, Karen, Duhaime, Ann-Christine, Ellenbogen, Richard, Feeser, V. Ramana, Ferguson, Adam R., Foreman, Brandon, Gardner, Raquel, Gaudette, Etienne, Goldman, Dana, Gonzalez, Luis, Gopinath, Shankar, Gullapalli, Rao, Hemphill, J. Claude, Hotz, Gillian, Jain, Sonia, Keene, C. Dirk, Korley, Frederick K., Kramer, Joel, Kreitzer, Natalie, Lindsell, Chris, Machamer, Joan, Madden, Christopher, Martin, Alastair, McAllister, Thomas, Merchant, Randall, Ngwenya, Laura B., Noel, Florence, Nolan, Amber, Palacios, Eva, Perl, Daniel, Puccio, Ava, Rabinowitz, Miri, Robertson, Claudia, Rosand, Jonathan, Sander, Angelle, Satris, Gabriella, Schnyer, David, Seabury, Seth, Toga, Arthur, Valadka, Alex, Vespa, Paul, Wang, Kevin, Yue, John K., Zafonte, Ross, Stein, Murray B., Yuh, Esther, Okonkwo, David O., Mac Donald, Christine L., Levin, Harvey, Giacino, Joseph T., Dikmen, Sureyya, Vassar, Mary J., Diaz-Arrastia, Ramon, Robertson, Claudia S., Nelson, Lindsay D., McCrea, Michael, Sun, Xiaoying, Temkin, Nancy, Taylor, Sabrina R., Markowitz, Amy J., Manley, Geoffrey T., and Mukherjee, Pratik

Published
2021
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11. Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer’s disease subgroups

Author

Groot, Colin, Grothe, Michel J., Mukherjee, Shubhabrata, Jelistratova, Irina, Jansen, Iris, van Loenhoud, Anna Catharina, Risacher, Shannon L., Saykin, Andrew J., Mac Donald, Christine L., Mez, Jesse, Trittschuh, Emily H., Gryglewski, Gregor, Lanzenberger, Rupert, Pijnenburg, Yolande A.L., Barkhof, Frederik, Scheltens, Philip, van der Flier, Wiesje M., Crane, Paul K., and Ossenkoppele, Rik

Published
2021
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12. Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

Author

Vande Vyvere, Thijs, Pisică, Dana, Wilms, Guido, Claes, Lene, Van Dyck, Pieter, Snoeckx, Annemiek, van den Hauwe, Luc, Pullens, Pim, Verheyden, Jan, Wintermark, Max, Dekeyzer, Sven, Mac Donald, Christine L., Maas, Andrew I.R., and Parizel, Paul M.

Published
2024
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13. Neuroinflammation at the Grey-White Matter Interface in Active-Duty United States Special Operations Forces

Author

Edlow, Brian L, primary, Tseng, Chieh-En J, additional, Gilmore, Natalie, additional, McKinney, Isabella R, additional, Tromly, Samantha L, additional, Deary, Katryna B, additional, Hu, Collin G, additional, Healy, Brian C, additional, Mac Donald, Christine L, additional, Dams-O'Connor, Kristen, additional, Greve, Douglas N, additional, Bodien, Yelena G, additional, Perl, Daniel P, additional, Hooker, Jacob M, additional, and Zürcher, Nicole R, additional

Published
2024
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14. Imaging findings in acute traumatic brain injury: a NINDS common data element-based pictorial review and analysis of over 4,000 admission brain CT scans from the CENTER-TBI study

Author

Vande Vyvere, Thijs, primary, Pisica, Dana, additional, Wilms, Guido, additional, Claes, Lene, additional, Van Dyck, Pieter, additional, Snoeckx, Annemiek, additional, van den Hauwe, Luc, additional, Pullens, Pim, additional, Verheyden, Jan, additional, Wintermark, Max, additional, Dekeyzer, Sven, additional, Mac Donald, Christine L, additional, Maas, Andrew, additional, and Parizel, Paul, additional

Published
2024
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15. Correction to: Association of day-of-injury plasma glial fibrillary acidic protein concentration and six-month posttraumatic stress disorder in patients with mild traumatic brain injury

Author

Kulbe, Jacqueline R., Jain, Sonia, Nelson, Lindsay D., Korley, Frederick K., Mukherjee, Pratik, Sun, Xiaoying, Okonkwo, David O., Giacino, Joseph T., Vassar, Mary J., Robertson, Claudia S., McCrea, Michael A., Wang, Kevin K. W., Temkin, Nancy, Mac Donald, Christine L., Taylor, Sabrina R., Ferguson, Adam R., Markowitz, Amy J., Diaz-Arrastia, Ramon, Manley, Geoffrey T., and Stein, Murray B.

Published
2022
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17. Author Response: Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology

Author

Gazula, Harshvardhan, primary, Tregidgo, Henry F. J., additional, Billot, Benjamin, additional, Balbastre, Yael, additional, William-Ramirez, Jonathan, additional, Herisse, Rogeny, additional, Deden-Binder, Lucas J, additional, Casamitjana, Adrià, additional, Melief, Erica J., additional, Latimer, Caitlin S., additional, Kilgore, Mitchell D., additional, Montine, Mark, additional, Robinson, Eleanor, additional, Blackburn, Emily, additional, Marshall, Michael S., additional, Connors, Theresa R., additional, Oakley, Derek H., additional, Frosch, Matthew P., additional, Young, Sean I., additional, Leemput, Koen Van, additional, Dalca, Adrian V., additional, Fischl, Bruce, additional, Mac Donald, Christine L., additional, Keene, C. Dirk, additional, Hyman, Bradley T., additional, and Iglesias, Juan Eugenio, additional

Published
2024
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18. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology

Author

Gazula, Harshvardhan, primary, Tregidgo, Henry F. J., additional, Billot, Benjamin, additional, Balbastre, Yael, additional, William-Ramirez, Jonathan, additional, Herisse, Rogeny, additional, Deden-Binder, Lucas J, additional, Casamitjana, Adrià, additional, Melief, Erica J., additional, Latimer, Caitlin S., additional, Kilgore, Mitchell D., additional, Montine, Mark, additional, Robinson, Eleanor, additional, Blackburn, Emily, additional, Marshall, Michael S., additional, Connors, Theresa R., additional, Oakley, Derek H., additional, Frosch, Matthew P., additional, Young, Sean I., additional, Leemput, Koen Van, additional, Dalca, Adrian V., additional, Fischl, Bruce, additional, Mac Donald, Christine L., additional, Keene, C. Dirk, additional, Hyman, Bradley T., additional, and Iglesias, Juan Eugenio, additional

Published
2024
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19. Comparison of brain imaging and physical health between research and clinical neuroimaging cohorts of aging

Author

Mossa-Basha, Mahmud, primary, Andre, Jalal B, additional, Yuh, Esther, additional, Hunt, David, additional, Lapiana, Nina, additional, Howlett, Bradley, additional, Krakauer, Chloe, additional, Crane, Paul, additional, Nelson, Jennifer, additional, DeZelar, Margaret, additional, Meyers, Kelly, additional, Larson, Eric, additional, Ralston, James, additional, and Mac Donald, Christine L, additional

Published
2024
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20. Intimate Partner Violence and Other Trauma Exposures in Females With Traumatic Brain Injury

Author

de Souza, Nicola L., primary, Kumar, Raj G., additional, Pruyser, Ariel, additional, Blunt, Emily E., additional, Sanders, William, additional, Meydan, Anogue, additional, Lawrence, Phoebe, additional, Venkatesan, Umesh M., additional, Mac Donald, Christine L., additional, Hoffman, Jeanne M., additional, Bodien, Yelena G., additional, Edlow, Brian L., additional, and Dams-O'Connor, Kristen, additional

Published
2024
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21. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology

Author

Gazula, Harshvardhan, primary, Tregidgo, Henry F. J., additional, Billot, Benjamin, additional, Balbastre, Yael, additional, William-Ramirez, Jonathan, additional, Herisse, Rogeny, additional, Deden-Binder, Lucas J, additional, Casamitjana, Adrià, additional, Melief, Erica J., additional, Latimer, Caitlin S., additional, Kilgore, Mitchell D., additional, Montine, Mark, additional, Robinson, Eleanor, additional, Blackburn, Emily, additional, Marshall, Michael S., additional, Connors, Theresa R., additional, Oakley, Derek H., additional, Frosch, Matthew P., additional, Young, Sean I., additional, Van Leemput, Koen, additional, Dalca, Adrian V., additional, Fischl, Bruce, additional, Mac Donald, Christine L., additional, Keene, C. Dirk, additional, Hyman, Bradley T., additional, and Iglesias, Juan Eugenio, additional

Published
2023
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28. The CARE4Kids Study: Endophenotypes of Persistent Post-Concussive Symptoms in Adolescents - Study Rationale and Protocol

Author

Giza, Christopher C., primary, Gioia, Gerry, additional, Cook, Lawrence J, additional, Asarnow, Robert F., additional, Snyder, Aliyah, additional, Babikian, Talin, additional, Thompson, Paul, additional, Bazarian, Jeffrey, additional, Whitlow, Christopher T, additional, Miles, Christopher M, additional, Otallah, Scott, additional, Kamins, Joshua, additional, Didehbani, Nyaz, additional, Rosenbaum, Philip, additional, Chrisman, Sara PD, additional, Vaughan, Christopher, additional, Cullum, C. Munro, additional, Popoli, David M, additional, Choe, Meeryo, additional, Gill, Jessica, additional, Dennis, Emily L., additional, Mac Donald, Christine L, additional, and Rivara, Frederick P, additional

Published
2023
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29. 3D analysis of dissection photographs with surface scanning and machine learning for quantitative neuropathology

Author

Gazula, Harshvardhan, Tregidgo, Henry F. J., Billot, Benjamin, Balbastre, Yael, William-Ramirez, Jonathan, Herisse, Rogeny, Casamitjana, Adrià, Melief, Erica J., Latimer, Caitlin S., Kilgore, Mitchell D., Montine, Mark, Robinson, Eleanor, Blackburn, Emily, Marshall, Michael S., Connors, Theresa R., Oakley, Derek H., Frosch, Matthew P., Van Leemput, Koen, Dalca, Adrian V., Fischl, Bruce, Mac Donald, Christine L., Keene, C. Dirk, Hyman, Bradley T., and Iglesias, Juan Eugenio

Subjects
Article
Abstract

Dissection photographs are routinely acquired in brain banks but seldom used for quantitative analysis. We present a publicly available, open-source toolbox of computational methods for 3D analysis of photographs of dissected slices of human brains. The toolbox includes methods for: (i) Geometric preprocessing of the photographs; (ii) 3D volumetric reconstruction of a human brain or hemisphere from the photographs and, optionally, a surface scan; and (iii) automatic 3D segmentation of nine bilateral brain regions using a novel machine learning method (“Photo-SynthSeg”), which is capable of “interpolating” the 3D segmentation across the gaps between photographs with large spacing in between. Our tools can be used for quantitative brain analysis at the macroscopic level (e.g., volumetry), as a substitute for ex vivo magnetic resonance imaging (MRI) – which requires access to an MRI scanner and ex vivo scanning expertise. Our tools are also helpful for computationally guided dissection of brain areas that are difficult to identify from the slices, e.g., defined by functional atlases. Finally, our tools also provide a spatial mapping between the a 3D MRI scan (when available) and the 2D slices, which enables accurate neuropathological-neuroimaging correlation. We evaluate our toolbox on a synthetic dataset and on dissection photographs from two NIH Alzheimer’s Disease Research Centers (ADRC). Our methodology yields accurate 3D reconstruction and automated segmentations that are highly correlated to those obtained from ex vivo MRI, and that can detect expected differences between brains with Alzheimer’s disease and controls. The tools are available as part of our widespread neuroimaging suite “FreeSurfer” ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ). SIGNIFICANCE STATEMENT: When donated human brains are dissected at brain banks, they are first cut into slabs that are routinely photographed just with archiving purposes. Here we present a set of novel, open-source computational tools that enable 3D reconstruction of these photographs into a volumetric dataset, as well as subsequent 3D segmentation of the reconstructed volumes (i.e., assigning neuroanatomical labels to every pixel in the images). Our tools enable quantitative analyses of the brain at the macroscopic level (e.g., measuring volumes of brain regions) without requiring MRI scans of the specimens. The tools also provide a spatial mapping between an MRI (if available) and the photographs, thus enabling the study of correlations between macroscopic and microscopic features (e.g., atrophy vs histology).

Published
2023

33. Integrated multimodal cell atlas of Alzheimer’s disease

Author

Gabitto, Mariano I., primary, Travaglini, Kyle J., additional, Rachleff, Victoria M., additional, Kaplan, Eitan S., additional, Long, Brian, additional, Ariza, Jeanelle, additional, Ding, Yi, additional, Mahoney, Joseph T., additional, Dee, Nick, additional, Goldy, Jeff, additional, Melief, Erica J., additional, Brouner, Krissy, additional, Campos, Jazmin, additional, Campos, John, additional, Carr, Ambrose J., additional, Casper, Tamara, additional, Chakrabarty, Rushil, additional, Clark, Michael, additional, Cool, Jonah, additional, Valera Cuevas, Nasmil J., additional, Dalley, Rachel, additional, Darvas, Martin, additional, Ding, Song-Lin, additional, Dolbeare, Tim, additional, Mac Donald, Christine L., additional, Egdorf, Tom, additional, Esposito, Luke, additional, Ferrer, Rebecca, additional, Gala, Rohan, additional, Gary, Amanda, additional, Gloe, Jessica, additional, Guilford, Nathan, additional, Guzman, Junitta, additional, Hirschstein, Daniel, additional, Ho, Windy, additional, Jarksy, Tim, additional, Johansen, Nelson, additional, Kalmbach, Brian E., additional, Keene, Lisa M., additional, Khawand, Sarah, additional, Kilgore, Mitch, additional, Kirkland, Amanda, additional, Kunst, Michael, additional, Lee, Brian R., additional, Malone, Jocelin, additional, Maltzer, Zoe, additional, Martin, Naomi, additional, McCue, Rachel, additional, McMillen, Delissa, additional, Meyerdierks, Emma, additional, Meyers, Kelly P., additional, Mollenkopf, Tyler, additional, Montine, Mark, additional, Nolan, Amber L., additional, Nyhus, Julie, additional, Olsen, Paul A., additional, Pacleb, Maiya, additional, Peña, Nicholas, additional, Pham, Thanh, additional, Pom, Christina Alice, additional, Postupna, Nadia, additional, Ruiz, Augustin, additional, Schantz, Aimee M., additional, Shapovalova, Nadiya V., additional, Sorensen, Staci A., additional, Staats, Brian, additional, Sullivan, Matt, additional, Sunkin, Susan M., additional, Thompson, Carol, additional, Tieu, Michael, additional, Ting, Jonathan, additional, Torkelson, Amy, additional, Tran, Tracy, additional, Wang, Ming-Qiang, additional, Waters, Jack, additional, Wilson, Angela M., additional, Haynor, David, additional, Gatto, Nicole, additional, Jayadev, Suman, additional, Mufti, Shoaib, additional, Ng, Lydia, additional, Mukherjee, Shubhabrata, additional, Crane, Paul K., additional, Latimer, Caitlin S., additional, Levi, Boaz P., additional, Smith, Kimberly, additional, Close, Jennie L., additional, Miller, Jeremy A., additional, Hodge, Rebecca D., additional, Larson, Eric B., additional, Grabowski, Thomas J., additional, Hawrylycz, Michael, additional, Dirk Keene, C., additional, and Lein, Ed S., additional

Published
2023
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34. Optimizing Brain Health of United States Special Operations Forces

Author

Edlow, Brian L, primary, Gilmore, Natalie, additional, Tromly, Samantha L, additional, Deary, Katryna B, additional, McKinney, Isabella R, additional, Hu, Collin G, additional, Kelemen, Jessica N, additional, Maffei, Chiara, additional, Tseng, Chieh-En J, additional, Llorden, Gabriel R, additional, Healy, Brian C, additional, Masood, Maryam, additional, Cali, Ryan J, additional, Baxter, Timothy, additional, Yao, Eveline F, additional, Belanger, Heather G, additional, Benjamini, Dan, additional, Basser, Peter J, additional, Priemer, David S, additional, Kimberly, W. T, additional, Polimeni, Jonathan R, additional, Rosen, Bruce R, additional, Fischl, Bruce, additional, Zurcher, Nicole R, additional, Greve, Douglas N, additional, Hooker, Jacob M, additional, Huang, Susie Y, additional, Caruso, Adam, additional, Smith, Gregory A, additional, Szymanski, Timothy G, additional, Perl, Daniel P, additional, Dams-O'Connor, Kristen, additional, Mac Donald, Christine L, additional, and Bodien, Yelena G, additional

Published
2023
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38. Diffusion Tensor Imaging Reveals Elevated Diffusivity of White Matter Microstructure that Is Independently Associated with Long-Term Outcome after Mild Traumatic Brain Injury: A TRACK-TBI Study

Author

Palacios, Eva M, Yuh, Esther L, Mac Donald, Christine L, Bourla, Ioanna, Wren-Jarvis, Jamie, Sun, Xiaoying, Vassar, Mary J, Diaz-Arrastia, Ramon, Giacino, Joseph T, Okonkwo, David O, Robertson, Claudia S, Stein, Murray B, Temkin, Nancy, McCrea, Michael A, Levin, Harvey S, Markowitz, Amy J, Jain, Sonia, Manley, Geoffrey T, Mukherjee, Pratik, and TRACK-TBI Investigators

Subjects
Adult, Traumatic, Physical Injury - Accidents and Adverse Effects, Adolescent, TRACK-TBI Investigators, Clinical Sciences, Glasgow Outcome Scale, Traumatic Brain Injury (TBI), Cohort Studies, Young Adult, Clinical Research, Humans, Brain Concussion, Traumatic Head and Spine Injury, Neurology & Neurosurgery, traumatic brain injury, Neurosciences, Brain, Injuries and accidents, Middle Aged, diffusion tensor imaging, White Matter, Brain Disorders, Diffusion Magnetic Resonance Imaging, Brain Injuries, Neurological, concussion, Biomedical Imaging, MRI
Abstract

Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE

Published
2022

40. Long-Term Effects of Repeated Blast Exposure in United States Special Operations Forces Personnel: A Pilot Study Protocol

Author

Edlow, Brian L., primary, Bodien, Yelena G., additional, Baxter, Timothy, additional, Belanger, Heather G., additional, Cali, Ryan J., additional, Deary, Katryna B., additional, Fischl, Bruce, additional, Foulkes, Andrea S., additional, Gilmore, Natalie, additional, Greve, Douglas N., additional, Hooker, Jacob M., additional, Huang, Susie Y., additional, Kelemen, Jessica N., additional, Kimberly, W. Taylor, additional, Maffei, Chiara, additional, Masood, Maryam, additional, Perl, Daniel P., additional, Polimeni, Jonathan R., additional, Rosen, Bruce R., additional, Tromly, Samantha L., additional, Tseng, Chieh-En J., additional, Yao, Eveline F., additional, Zürcher, Nicole R., additional, Mac Donald, Christine L., additional, and Dams-O'Connor, Kristen, additional

Published
2022
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42. Integrating Neuroimaging Measures in Nursing Research.

Author

Figuracion, Karl Cristie F., Thompson, Hilaire, and Mac Donald, Christine L.

Subjects
BRAIN physiology, COGNITION disorder risk factors, BRAIN anatomy, CINAHL database, ONLINE information services, DIGITAL image processing, BRAIN, EVALUATION of medical care, OXYGEN, MOLECULAR diagnosis, SYSTEMATIC reviews, QUANTITATIVE research, MAGNETIC resonance imaging, MEDICAL research personnel, WHITE matter (Nerve tissue), ATROPHY, DIAGNOSTIC imaging, CHOLINE, QUALITATIVE research, NURSES, NURSING research, SIGNAL processing, AUTOMATION, RESEARCH funding, COGNITIVE testing, MEDLINE, GLUTAMINE, COMPUTED tomography, NEURORADIOLOGY, INOSITOL
Abstract

Background: Medical and scientific advancement worldwide has led to a longer lifespan. With the population aging comes the risk of developing cognitive decline. The incorporation of neuroimaging measures in evaluating cognitive changes is limited in nursing research. The aim of this review is to introduce nurse scientists to neuroimaging measures employed to assess the association between brain and cognitive changes. Methods: Relevant literature was identified by searching CINAHL, Web of Science, and PubMed databases using the following keywords: "neuroimaging measures," "aging," "cognition," "qualitative scoring," "cognitive ability," "molecular," "structural," and "functional." Results: Neuroimaging measures can be categorized into structural, functional, and molecular imaging approaches. The structural imaging technique visualizes the anatomical regions of the brain. Visual examination and volumetric segmentation of select structural sequences extract information such as white matter hyperintensities and cerebral atrophy. Functional imaging techniques evaluate brain regions and underlying processes using blood-oxygen-dependent signals. Molecular imaging technique is the real-time visualization of biological processes at the cellular and molecular levels in a given region. Examples of biological measures associated with neurodegeneration include decreased glutamine level, elevated total choline, and elevated Myo-inositol. Discussion: Nursing is at the forefront of addressing upstream factors impacting health outcomes across a lifespan of a population at increased risk of progressive cognitive decline. Nurse researchers can become more facile in using these measures both in qualitative and quantitative methodology by leveraging previously gathered neuroimaging clinical data for research purposes to better characterize the associations between symptom progression, disease risk, and health outcomes. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Acute Postoperative Seizures and Engel Class Outcome at 1 Year Postselective Laser Amygdalohippocampal Ablation for Mesial Temporal Lobe Epilepsy

Author

Barkley, Ariana S., primary, Sullivan, Liam T., additional, Gibson, Alec W., additional, Zalewski, Kody, additional, Mac Donald, Christine L., additional, Barber, Jason K., additional, Hakimian, Shahin, additional, Ko, Andrew L., additional, Ojemann, Jeffrey G., additional, and Hauptman, Jason S., additional

Published
2022
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46. Interrater Reliability of National Institutes of Health Traumatic Brain Injury Imaging Common Data Elements for Brain Magnetic Resonance Imaging in Mild Traumatic Brain Injury

Author

Rincon, Sandra P, Mukherjee, Pratik, Levin, Harvey S, Temkin, Nancy R, Mac Donald, Christine L, Krainak, Daniel M, Sun, Xiaoying, Jain, Sonia, Taylor, Sabrina R, Markowitz, Amy J, Kumar, Allison, Manley, Geoffrey T, Yuh, Esther L, and TRACK-TBI Investigators

Subjects
Adult, Male, Traumatic, Physical Injury - Accidents and Adverse Effects, Adolescent, FDA Medical Device Development Tool, TRACK-TBI Investigators, Clinical Sciences, Diffuse Axonal Injury, Traumatic Brain Injury (TBI), Young Adult, Clinical Research, interrater reliability, Humans, Brain Concussion, Traumatic Head and Spine Injury, Aged, Observer Variation, screening and diagnosis, Common Data Elements, Neurology & Neurosurgery, Neurosciences, Reproducibility of Results, imaging, Brain Contusion, Middle Aged, Magnetic Resonance Imaging, United States, radiology, Brain Disorders, 4.1 Discovery and preclinical testing of markers and technologies, Detection, Brain Injuries, Neurological, Biomedical Imaging, Female, Artifacts, Biomarkers, MRI, 4.2 Evaluation of markers and technologies
Abstract

The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI.

Published
2021

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