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1. Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark

Author

Katherine E. Link, Zane Schnurman, Chris Liu, Young Joon (Fred) Kwon, Lavender Yao Jiang, Mustafa Nasir-Moin, Sean Neifert, Juan Diego Alzate, Kenneth Bernstein, Tanxia Qu, Viola Chen, Eunice Yang, John G. Golfinos, Daniel Orringer, Douglas Kondziolka, and Eric Karl Oermann

Subjects
Science
Abstract

Abstract The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world’s largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (

Published
2024
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2. Population scale latent space cohort matching for the improved use and exploration of observational trial data

Author

Rachel Gologorsky, Sulaiman S. Somani, Sean N. Neifert, Aly A. Valliani, Katherine E. Link, Viola J. Chen, Anthony B. Costa, and Eric K. Oermann

Subjects
artificial intelligence, autoencoders, cohort matching, data visualization, deep learning, manifold learning, Biotechnology, TP248.13-248.65, Mathematics, QA1-939
Abstract

A significant amount of clinical research is observational by nature and derived from medical records, clinical trials, and large-scale registries. While there is no substitute for randomized, controlled experimentation, such experiments or trials are often costly, time consuming, and even ethically or practically impossible to execute. Combining classical regression and structural equation modeling with matching techniques can leverage the value of observational data. Nevertheless, identifying variables of greatest interest in high-dimensional data is frequently challenging, even with application of classical dimensionality reduction and/or propensity scoring techniques. Here, we demonstrate that projecting high-dimensional medical data onto a lower-dimensional manifold using deep autoencoders and post-hoc generation of treatment/control cohorts based on proximity in the lower-dimensional space results in better matching of confounding variables compared to classical propensity score matching (PSM) in the original high-dimensional space (P

Published
2022
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4. Human neocortical expansion involves glutamatergic neuron diversification

Author

Tim S. Heistek, Thomas Braun, Natalia A. Goriounova, Michael Tieu, Lindsay Ng, Michael Hawrylycz, Kris Bickley, Anton Arkhipov, Colin Farrell, Trangthanh Pham, Alexandra Glandon, Daniel Park, Gábor Molnár, Herman Tung, Allan R. Jones, Lisa Keene, Gáspár Oláh, Thomas Chartrand, Amy Torkelson, Jae Geun Yoon, Rachel A. Dalley, Aaron Szafer, Nick Dee, Brian E. Kalmbach, Eliza Barkan, Allison Beller, Krissy Brouner, Andrew L. Ko, Alex M. Henry, Viktor Szemenyei, Julie Nyhus, Staci A. Sorensen, Samuel Dingman Lee, Norbert Mihut, Amy Bernard, Lisa Kim, Anatoly Buchin, Melissa Gorham, Lucas T. Graybuck, Lydia Potekhina, Katelyn Ward, Caitlin S. Latimer, Aaron Oldre, Gabe J. Murphy, Boaz P. Levi, Trygve E. Bakken, René Wilbers, Jonathan T. Ting, Kimberly A. Smith, Amanda Gary, Songlin Ding, Alice Mukora, Matthew Kroll, Anoop P. Patel, Wayne Wakeman, Hongkui Zeng, Nadezhda Dotson, Rusty Mann, Victoria Omstead, Leona Mezei, Desiree A. Marshall, Shea Ransford, Lydia Ng, Sara Kebede, Gábor Tamás, Jeffrey G. Ojemann, Stephanie Mok, Nathan Hansen, Christina A. Pom, Brian Lee, Jim Berg, Ramkumar Rajanbabu, John W. Phillips, Philip R. Nicovich, Matthew Mallory, Richard G. Ellenbogen, Rachel Enstrom, Luke Esposito, Tim Jarsky, Di Jon Hill, Idan Segev, Darren Bertagnolli, Agata Budzillo, Sander Idema, Daniel L. Silbergeld, Costas A. Anastassiou, Chris Hill, Michelle Maxwell, Mean Hwan Kim, Charles Cobbs, Delissa McMillen, Bosiljka Tasic, Olivia Fong, Medea McGraw, Hong Gu, Kirsten Crichton, David Reid, Kristen Hadley, Lauren Alfiler, Manuel Ferreira, Elliot R. Thomsen, Kiet Ngo, Josef Sulc, Augustin Ruiz, Katherine Baker, Zizhen Yao, Erica J. Melief, Femke Waleboer, Hanchuan Peng, Grace Williams, Rebecca D. Hodge, Kyla Berry, Katherine E. Link, David Sandman, Tsega Desta, Christine Rimorin, Jeff Goldy, Ryder P. Gwinn, Djai B. Heyer, Changkyu Lee, Jeremy A. Miller, Nathan W. Gouwens, Pál Barzó, Attila Ozsvár, Huibert D. Mansvelder, Sergey L. Gratiy, Rafael Yuste, David Feng, Jessica Trinh, Clare Gamlin, Tamara Casper, C. Dirk Keene, Susan M. Sunkin, Tom Egdorf, Philip C. De Witt Hamer, Rebecca de Frates, Peter Chong, Szabina Furdan, Patrick R. Hof, Jasmine Bomben, Christiaan P. J. de Kock, Eline J. Mertens, Ed S. Lein, Anna A. Galakhova, Florence D’Orazi, Christof Koch, Madie Hupp, Neurosurgery, Amsterdam Neuroscience - Systems & Network Neuroscience, Integrative Neurophysiology, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, and Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention

Subjects
Cell type, Multidisciplinary, Neocortex, Neurofilament, Molecular neuroscience, Biology, Article, Cellular neuroscience, chemistry.chemical_compound, Glutamatergic, medicine.anatomical_structure, chemistry, Biocytin, medicine, Neuron, Neuroscience
Abstract

The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease., Combined patch clamp recording, biocytin staining and single-cell RNA-sequencing of human neurocortical neurons shows an expansion of glutamatergic neuron types relative to mouse that characterizes the greater complexity of the human neocortex.

Published
2021
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5. Classification of electrophysiological and morphological neuron types in the mouse visual cortex

Author

David Sandman, Brian Lee, Michael Hawrylycz, Sara Kebede, Tom Egdorf, David Reid, Rob Young, Nivretta Thatra, Stefan Mihalas, David Feng, John W. Phillips, Rebecca de Frates, DiJon Hill, Cliff Slaughterbeck, Samuel R Josephsen, Tamara Casper, Xiaoxiao Liu, Hanchuan Peng, Peter Chong, Colin Farrell, Zhi Zhou, Sheana Parry, Jed Perkins, Brian Long, Susan M. Sunkin, Matthew Kroll, Krissy Brouner, Melissa Gorham, Aaron Szafer, Wayne Wakeman, Hong Gu, Marissa Garwood, Daniel Park, Kristen Hadley, Michael S. Fisher, Lydia Potekhina, Ed Lein, Alice Mukora, Hongkui Zeng, Nick Dee, Aaron Oldre, Lindsay Ng, Thomas Braun, Grace Williams, Tracy Lemon, Julie A. Harris, Medea McGraw, Nadezhda Dotson, Philip R. Nicovich, Amanda Gary, Rusty Mann, Alex M. Henry, Caroline Habel, Samuel Dingman, Katherine E. Link, Nathalie Gaudreault, Gilberto J. Soler-Llavina, Thuc Nghi Nguyen, Nicole Blesie, Bosiljka Tasic, Lydia Ng, Christine Cuhaciyan, Tim Jarsky, Keith B. Godfrey, Costas A. Anastassiou, Kirsten Crichton, Josef Sulc, Martin Schroedter, Dan Castelli, Miranda Robertson, Amy Bernard, Lisa Kim, Songlin Ding, Alyse Doperalski, Nathan W. Gouwens, Herman Tung, Tsega Desta, Corinne Teeter, James Harrington, Jonathan T. Ting, Kris Bickley, Anton Arkhipov, Kiet Ngo, Changkyu Lee, Jim Berg, Agata Budzillo, Emma Garren, Tanya L. Daigle, Christof Koch, Rachel A. Dalley, Eliza Barkan, Staci A. Sorensen, Gabe J. Murphy, Shiella Caldejon, and Naz Taskin

Subjects
0301 basic medicine, Genetically modified mouse, Cell type, Patch-Clamp Techniques, Databases, Factual, Action Potentials, Datasets as Topic, Mice, Transgenic, Biology, Article, Neuron types, Mice, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Genes, Reporter, Biocytin, medicine, Animals, Cell shape, Cell Shape, Visual Cortex, Neurons, General Neuroscience, Laboratory mouse, Electrophysiology, 030104 developmental biology, Visual cortex, medicine.anatomical_structure, chemistry, Transcriptome, Neuroscience, 030217 neurology & neurosurgery
Abstract

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

Published
2019
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6. Remote Patient Monitoring Identifies the Need for Triage in Patients with Acute COVID-19 Infection

Author

Jamie Wood, Katherine E. Link, Leila Nasr, Sophie Dewil, Jenna Tosto-Mancuso, Laura Tabacof, Erica Breyman, David Putrino, Nicki Mohammadi, and Christopher P. Kellner

Subjects
Telemedicine, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), business.industry, Remote patient monitoring, SARS-CoV-2, COVID-19, Health Informatics, General Medicine, Telehealth, equipment and supplies, medicine.disease, Triage, Health Information Management, Pandemic, Medicine, Humans, In patient, Medical emergency, business, Pandemics, Monitoring, Physiologic, Retrospective Studies
Abstract

Background: Telehealth was frequently used in the provision of care and remote patient monitoring (RPM) during the COVID-19 pandemic. The Precision Recovery Program (PRP) remotely monitored and sup...

Published
2021

7. RadImageNet: A Large-scale Radiologic Dataset for Enhancing Deep Learning Transfer Learning Research

Author

Katherine E. Link, B. Marinelli, Yang Yang, Hayit Greenspan, Ying Wang, Zahi A. Fayad, Amish H. Doshi, Xueyan Mei, Adam Jacobi, Thomas Yang, Chendi Cao, Philip M. Robson, Mingqian Huang, and Timothy W. Deyer

Subjects
Scale (ratio), Computer science, business.industry, Deep learning, Artificial intelligence, Machine learning, computer.software_genre, Transfer of learning, business, computer
Abstract

Most current medical imaging Artificial Intelligence (AI) relies upon transfer learning using convolutional neural networks (CNNs) created using ImageNet, a large database of natural world images, including cats, dogs, and vehicles. Size, diversity, and similarity of the source data determine the success of the transfer learning on the target data. ImageNet is large and diverse, but there is a significant dissimilarity between its natural world images and medical images, leading Cheplygina to pose the question, “Why do we still use images of cats to help Artificial Intelligence interpret CAT scans?”. We present an equally large and diversified database, RadImageNet, consisting of 5 million annotated medical images consisting of CT, MRI, and ultrasound of musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, and pulmonary pathologies over 450,000 patients. The database is unprecedented in scale and breadth in the medical imaging field, constituting a more appropriate basis for medical imaging transfer learning applications. We found that RadImageNet transfer learning outperformed ImageNet in multiple independent applications, including improvements for bone age prediction from hand and wrist x-rays by 1.75 months (p

Published
2021
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8. Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons

Author

Sergey L. Gratiy, Sara Kebede, Chris Hill, Clare Gamlin, Jeffrey G. Ojemann, Tom Egdorf, Ed S. Lein, Lydia Potekhina, Alice Mukora, Shea Ransford, Matthew Mallory, Tim S. Heistek, Jonathan T. Ting, Gábor Tamás, Philip C. De Witt Hamer, Rebecca de Frates, Medea McGraw, Gábor Molnár, Jim Berg, Szabina Furdan, Patrick R. Hof, Natalia A. Goriounova, David Feng, David Reid, Elliot R. Thomsen, Michael Tieu, Katelyn Ward, C. Dirk Keene, Florence D’Orazi, Mean Hwan Kim, Daniel Park, Amy Torkelson, Agata Budzillo, Katherine Baker, Michael Hawrylycz, Krissy Brouner, Andrew L. Ko, DiJon Hill, Kyla Berry, Peter Chong, Jessica Trinh, Desiree A. Marshall, Katherine E. Link, Brian Lee, Jasmine Bomben, Aaron Szafer, Gabe J. Murphy, Viktor Szemenyei, Madie Hupp, Lauren Alfiler, Nick Dee, Zizhen Yao, Luke Esposito, Tamara Casper, Erica J. Melief, Susan M. Sunkin, Lindsay Ng, Hongkui Zeng, Pál Barzó, Allison Beller, Lydia Ng, Charles Cobbs, Darren Bertagnolli, Kiet Ngo, Bosiljka Tasic, John W. Phillips, Christine Rimorin, Alex M. Henry, Aaron Oldre, Michelle Maxwell, Wayne Wakeman, Delissa McMillen, Amanda Gary, Tsega Desta, Nathan Hansen, Hong Gu, Julie Nyhus, Staci A. Sorensen, Gáspár Oláh, Thomas Chartrand, Kirsten Crichton, Matthew Kroll, Josef Sulc, Jeremy A. Miller, Amy Bernard, Lisa Kim, Herman Tung, Idan Segev, Kristen Hadley, David Sandman, Anoop P. Patel, Colin Farrell, Allan R. Jones, Lisa Keene, Sander Idema, Changkyu Lee, Stephanie Mok, Augustin Ruiz, Caitlin S. Latimer, Tim Jarsky, Kris Bickley, Anton Arkhipov, Ramkumar Rajanbabu, Thomas Braun, Costas A. Anastassiou, Anatoly Buchin, Nathan W. Gouwens, Philip R. Nicovich, Richard G. Ellenbogen, Olivia Fong, Grace Williams, Rachel Enstrom, Rachel A. Dalley, Daniel L. Silbergeld, Attila Ozsvár, Kimberly A. Smith, Ryder P. Gwinn, Songlin Ding, Rafael Yuste, Manuel Ferreira, Victoria Omstead, Samuel Dingman Lee, Norbert Mihut, Hanchuan Peng, Brian E. Kalmbach, Eliza Barkan, Melissa Gorham, Boaz P. Levi, Trygve E. Bakken, Jeff Goldy, Djai B. Heyer, Nadezhda Dotson, Rusty Mann, Rebecca D. Hodge, Christof Koch, René Wilbers, Leona Mezei, Eline J. Mertens, Jae-Geun Yoon, Anna A. Galakhova, Christina A. Pom, Trangthanh Pham, Alexandra Glandon, Christiaan P. J. de Kock, Lucas T. Graybuck, and Huibert D. Mansvelder

Subjects
0303 health sciences, Cell type, Neocortex, Neurofilament, Biology, Transcriptome, 03 medical and health sciences, Glutamatergic, 0302 clinical medicine, medicine.anatomical_structure, Cortex (anatomy), Specialization (functional), medicine, Neuroscience, 030217 neurology & neurosurgery, Function (biology), 030304 developmental biology
Abstract

The neocortex is disproportionately expanded in human compared to mouse, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers that selectively make connections within the cortex and other telencephalic structures. Single-cell transcriptomic analyses of human and mouse cortex show an increased diversity of glutamatergic neuron types in supragranular cortex in human and pronounced gradients as a function of cortical depth. To probe the functional and anatomical correlates of this transcriptomic diversity, we describe a robust Patch-seq platform using neurosurgically-resected human tissues. We characterize the morphological and physiological properties of five transcriptomically defined human glutamatergic supragranular neuron types. Three of these types have properties that are specialized compared to the more homogeneous properties of transcriptomically defined homologous mouse neuron types. The two remaining supragranular neuron types, located exclusively in deep layer 3, do not have clear mouse homologues in supragranular cortex but are transcriptionally most similar to deep layer mouse intratelencephalic-projecting neuron types. Furthermore, we reveal the transcriptomic types in deep layer 3 that express high levels of non-phosphorylated heavy chain neurofilament protein that label long-range neurons known to be selectively depleted in Alzheimer’s disease. Together, these results demonstrate the power of transcriptomic cell type classification, provide a mechanistic underpinning for increased complexity of cortical function in human cortical evolution, and implicate discrete transcriptomic cell types as selectively vulnerable in disease.

Published
2020
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9. Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons

Author

David Feng, Jessica Trinh, Tamara Casper, Lisa Kim, Rohan Gala, Clare Gamlin, Matthew Kroll, Uygar Sümbül, Lauren Alfiler, Thomas Braun, Jasmine Bomben, Bosiljka Tasic, Colin Farrell, Hongkui Zeng, Lydia Potekhina, Tsega Desta, Kiet Ngo, Lydia Ng, Alice Mukora, Fahimeh Baftizadeh, Aaron Szafer, Rachel A. Dalley, Shea Ransford, Changkyu Lee, Nick Dee, Brian Lee, Kirsten Crichton, Luke Esposito, Miranda Robertson, Josef Sulc, Alex M. Henry, Darren Bertagnolli, Tom Egdorf, Nadezhda Dotson, Zhi Zhou, Jim Berg, Philip R. Nicovich, Rusty Mann, Madie Hupp, Daniel Park, Delissa McMillen, Samuel Dingman Lee, Agata Budzillo, Eliza Barkan, Olivia Fong, Thanh Pham, Jeff Goldy, Ed S. Lein, Rebecca de Frates, Kimberly A. Smith, Amy Torkelson, Tim Jarsky, Michelle Maxwell, Michael Tieu, Susan M. Sunkin, Michael Hawrylycz, Lucas T. Graybuck, Herman Tung, David Reid, DiJon Hill, Alexandra Glandon, Kara Ronellenfitch, Aaron Oldre, Amanda Gary, Nathan W. Gouwens, Christof Koch, Alice Pom, Wayne Wakeman, Sara Kebede, Matthew Mallory, Tae Kyung Kim, Tanya L. Daigle, Kris Bickley, Anton Arkhipov, Osnat Penn, Staci A. Sorensen, Rachel Enstrom, Hanchuan Peng, Ramkumar Rajanbabu, Jonathan T. Ting, Zizhen Yao, Lauren Ellingwood, Medea McGraw, Gabe J. Murphy, Katherine Baker, Krissy Brouner, Hong Gu, David Sandman, Katelyn Ward, Kyla Berry, Katherine E. Link, Lindsay Ng, Christine Rimorin, Kristen Hadley, Augustin Ruiz, Grace Williams, and Melissa Gorham

Subjects
Transcriptome, Electrophysiology, Cell type, medicine.anatomical_structure, Visual cortex, Interneuron, nervous system, genetic structures, medicine, GABAergic, Cortical neurons, Biology, Neuroscience
Abstract

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t-types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.HighlightsPatch-seq data obtained from >3,700 GABAergic cortical interneuronsComprehensive characterization of morpho-electric features of transcriptomic types20 interneuron met-types that have congruent properties across data modalitiesDifferent Sst met-types preferentially innervate different cortical layers

Published
2020
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10. Toward an Integrated Classification of Cell Types: Morphoelectric and Transcriptomic Characterization of Individual GABAergic Cortical Neurons

Author

Kimberly A. Smith, Matthew Kroll, Sara Kebede, Susan M. Sunkin, David Reid, Nadezhda Dotson, Rusty Mann, DiJon Hill, Kara Ronellenfitch, Shea Ransford, Hongkui Zeng, David Feng, Jasmine Bomben, Bosiljka Tasic, Rachel Enstrom, Jessica Trinh, Matthew Mallory, Aaron Szafer, Rachel A. Dalley, Aaron Oldre, Amanda Gary, Eliza Barkan, Nick Dee, Lydia Ng, Tae Kyung Kim, Ed S. Lein, Colin Farrell, Tamara Casper, Tom Egdorf, Kirsten Crichton, Josef Sulc, Fahimeh Baftizadeh, Katelyn Ward, Kirsten Hadley, Alex M. Henry, Alice Pom, Brian Lee, Uygar Sümbül, Lisa Kim, Tim Jarsky, Madie Happ, Wayne Wakeman, Lauren Ellingwood, Luke Esposito, Daniel Park, Tanya L. Daigle, Darren Bertagnolli, Lucas T. Graybuck, Olivia Fong, Philip R. Nicovich, Gabe J. Murphy, Michelle Maxwell, Lindsay Ng, Rebeeca de Frates, Rohan Gala, Alice Mukora, Delissa McMillen, Miranda Robertson, Thanh Pham, Samuel Dingman Lee, Kris Bickley, Anton Arkhipov, Osnat Penn, Staci A. Sorensen, Alexandra Glandon, Zizhen Yao, Amy Torkelson, Jonathan T. Ting, Lauren Alfiler, Ramkumar Rajanbabu, Kiet Ngo, Kirssy Brouner, David Sandman, Michael Tieu, Michael Hawrylycz, Nathan W. Gouwens, Hanchuan Peng, Zhi Zhou, Jeff Goldy, Hong Gu, Herman Tung, Medea McGraw, Lyida Potekhina, Katherine Baker, Tsega Desta, Christof Koch, Changkyu Lee, Melissa Gorham, Clare Gamlin, Augustin Ruiz, Grace Williams, Jim Berg, Kyla Berry, Katherine E. Link, Agata Budzillo, Christine Rimorin, and Thomas Braun

Subjects
Cell type, genetic structures, Interneuron, Cortical neurons, Biology, Transcriptome, Electrophysiology, medicine.anatomical_structure, Visual cortex, nervous system, medicine, biology.protein, GABAergic, Neuroscience, Parvalbumin
Abstract

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t- types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.

Published
2020
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11. The Neural Basis of Approach-Avoidance Conflict: A Model Based Analysis

Author

Alik S. Widge, Diego A. Pizzagalli, Darin D. Dougherty, Alexander P. Rockhill, Katherine E. Link, Todd M. Herrington, Kristen K. Ellard, Thilo Deckersbach, and Samuel Zorowitz

Subjects
Adult, Male, Models, Neurological, Individuality, Approach-avoidance conflict, Inferior frontal gyrus, psychology, Bayesian inference, Choice Behavior, 050105 experimental psychology, decision making, Task (project management), cognitive, Conflict, Psychological, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Reward, Avoidance Learning, Humans, 0501 psychology and cognitive sciences, Set (psychology), Methods/New Tools, Brain Mapping, Basis (linear algebra), General Neuroscience, 05 social sciences, fMRI, 1.2, Brain, Cognition, Bayes Theorem, General Medicine, Magnetic Resonance Imaging, psychiatry, Cognition and Behavior, Female, Psychology, approach-avoidance, 030217 neurology & neurosurgery, Cognitive psychology
Abstract

Approach-avoidance conflict arises when the drives to pursue reward and avoid harm are incompatible. Previous neuroimaging studies of approach-avoidance conflict have shown large variability in reported neuroanatomical correlates. These prior studies have generally neglected to account for potential sources of variability, such as individual differences in choice preferences and modeling of hemodynamic response during conflict. In the present study, we controlled for these limitations using a hierarchical Bayesian model (HBM). This enabled us to measure participant-specific per-trial estimates of conflict during an approach-avoidance task. We also employed a variable epoch method to identify brain structures specifically sensitive to conflict. In a sample of 28 human participants, we found that only a limited set of brain structures [inferior frontal gyrus (IFG), right dorsolateral prefrontal cortex (dlPFC), and right pre-supplementary motor area (pre-SMA)] are specifically correlated with approach-avoidance conflict. These findings suggest that controlling for previous sources of variability increases the specificity of the neuroanatomical correlates of approach-avoidance conflict.

Published
2019

12. Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells

Author

Kris Bickley, Anton Arkhipov, Osnat Penn, Hanchuan Peng, Shea Ransford, Sara Kebede, Kara Ronellenfitch, Matthew Mallory, Krissy Brouner, Madie Hupp, Lydia Ng, Daniel Park, Staci A. Sorensen, Alice Pom, Susan M. Sunkin, Tanya L. Daigle, Fahimeh Baftizadeh, Wayne Wakeman, Aaron Oldre, Amanda Gary, Herman Tung, Brian Lee, Ed S. Lein, Medea McGraw, Rachel A. Dalley, Bosiljka Tasic, Hong Gu, Miranda Robertson, Katherine Baker, Lindsay Ng, David Sandman, Jasmine Bomben, Uygar Sümbül, Tae Kyung Kim, David Reid, Eliza Barkan, Luke Esposito, Kirsten Crichton, DiJon Hill, Zoran Popović, Josef Sulc, Nathan W. Gouwens, Ramkumar Rajanbabu, Lydia Potekhina, Thomas Braun, Alexandra Glandon, Tim Jarsky, Darren Bertagnolli, Tom Egdorf, Olivia Fong, Alice Mukora, Rebecca de Frates, Lauren Ellingwood, Jonathan T. Ting, Gabe J. Murphy, Katelyn Ward, Delissa McMillen, Samuel Dingman Lee, Melissa Gorham, Michelle Maxwell, Clare Gamlin, Zhi Zhou, Jeff Goldy, Rachel Enstrom, Kyla Berry, Colin Farrell, Katherine E. Link, Christine Rimorin, Zizhen Yao, Hongkui Zeng, Kristen Hadley, Augustin Ruiz, Grace Williams, Amy Torkelson, Kimberly A. Smith, Lisa Kim, Aaron Szafer, Nick Dee, Alex M. Henry, Rohan Gala, David Feng, Jessica Trinh, Tamara Casper, Matthew Kroll, Christof Koch, Michael Tieu, Michael Hawrylycz, Lauren Alfiler, Kiet Ngo, Philip R. Nicovich, Thanh Pham, Nadezhda Dotson, Rusty Mann, Tsega Desta, Lucas T. Graybuck, Changkyu Lee, Jim Berg, and Agata Budzillo

Subjects
0303 health sciences, Cell type, biology, Interneuron, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, Electrophysiology, 0302 clinical medicine, medicine.anatomical_structure, Visual cortex, medicine, biology.protein, GABAergic, Axon, Neuroscience, 030217 neurology & neurosurgery, Parvalbumin, 030304 developmental biology
Abstract

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

Published
2020
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13. Echoplanar Magnetic Resonance Spectroscopic Imaging Before and Following Radiation Therapy in Patients With High-Grade Glioma

Author

S.M. Usama, A. Zessler, Sulaiman Sheriff, Anouk Marsman, Lawrence Kleinberg, Y. Lin, Doris D. M. Lin, Andrew A. Maudsley, Peter B. Barker, and Katherine E. Link

Subjects
Cancer Research, Radiation, business.industry, medicine.medical_treatment, Magnetic resonance spectroscopic imaging, 030218 nuclear medicine & medical imaging, Radiation therapy, 03 medical and health sciences, 0302 clinical medicine, Oncology, medicine, Radiology, Nuclear Medicine and imaging, In patient, Nuclear medicine, business, 030217 neurology & neurosurgery, High-Grade Glioma
Published
2016

14. Imaging and Clinical Profile Following Concurrent Stereotactic Radiation and Immune Therapy for Melanoma Brain Metastases: Preliminary Results

Author

Katherine E. Link, Chetan Bettegowda, Kristin J. Redmond, Jacqueline Douglass, Doris D. M. Lin, Michael Lim, Evan J. Lipson, Megan N. Kummerlowe, Lawrence Kleinberg, Colette J. Shen, and William H. Sharfman

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Radiation, business.industry, Melanoma, medicine.disease, Immune therapy, 03 medical and health sciences, 0302 clinical medicine, Text mining, Stereotactic radiation, 030220 oncology & carcinogenesis, Internal medicine, medicine, Radiology, Nuclear Medicine and imaging, business, 030217 neurology & neurosurgery
Published
2016

15. Imaging and Clinical Profile of Concurrent Stereotactic Radiation and Immune Therapy for Melanoma Brain Metastases

Author

Katherine E. Link, L.R. Kleinberg, Colette J. Shen, J. Hoare, K.J. Redmond, Doris D. M. Lin, Michael Lim, Chetan Bettegowda, Jimm Grimm, and L. Sloan

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Radiation, business.industry, Melanoma, medicine.disease, Immune therapy, Stereotactic radiation, Internal medicine, medicine, Radiology, Nuclear Medicine and imaging, business
Published
2017
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16. Rate of Intralesional Hemorrhage in Hypofractionated Stereotactic Radiosurgery for Brain Metastases in Patients with Metastatic Melanoma Treated with Concurrent Immunotherapy

Author

K.J. Redmond, Jimm Grimm, Chetan Bettegowda, Katherine E. Link, L. Sloan, Colette J. Shen, Doris D. M. Lin, Michael Lim, and L.R. Kleinberg

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Radiation, Metastatic melanoma, business.industry, medicine.medical_treatment, Immunotherapy, Radiosurgery, Internal medicine, medicine, Radiology, Nuclear Medicine and imaging, In patient, business
Published
2017
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17. Experimental and subject factors determining responses to sensory deprivation, social isolation, and confinement

Author

Marvin Zuckerman, Harold Persky, Katherine E. Link, and Gopak K. Basu

Subjects
Male, Subject (philosophy), Spatial Behavior, Anxiety, Developmental psychology, Clinical Psychology, Psychiatry and Mental health, Sex Factors, Social Isolation, Time Perception, Set, Psychology, medicine, Humans, Female, Sensory deprivation, Sensory Deprivation, Social isolation, medicine.symptom, Psychology, Biological Psychiatry, Personality
Published
1968
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