Search Results - "Holstein, Vincent"

PHOTONAI—A Python API for rapid machine learning model development by Leenings, Ramona, Winter, Nils Ralf, Plagwitz, Lucas, Holstein, Vincent, Ernsting, Jan, Sarink, Kelvin, Fisch, Lukas, Steenweg, Jakob, Kleine-Vennekate, Leon, Gebker, Julian, Emden, Daniel, Grotegerd, Dominik, Opel, Nils, Risse, Benjamin, Jiang, Xiaoyi, Dannlowski, Udo, Hahn, Tim

“…PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the…”
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Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood by Daskalakis, Nikolaos P, Iatrou, Artemis, Chatzinakos, Chris, Jajoo, Aarti, Snijders, Clara, Wylie, Dennis, DiPietro, Christopher P, Tsatsani, Ioulia, Chen, Chia-Yen, Pernia, Cameron D, Soliva-Estruch, Marina, Arasappan, Dhivya, Bharadwaj, Rahul A, Collado-Torres, Leonardo, Wuchty, Stefan, Alvarez, Victor E, Dammer, Eric B, Deep-Soboslay, Amy, Duong, Duc M, Eagles, Nick, Huber, Bertrand R, Huuki, Louise, Holstein, Vincent L, Logue, Mark W, Lugenbühl, Justina F, Maihofer, Adam X, Miller, Mark W, Nievergelt, Caroline M, Pertea, Geo, Ross, Deanna, Sendi, Mohammad S E, Sun, Benjamin B, Tao, Ran, Tooke, James, Wolf, Erika J, Zeier, Zane, Berretta, Sabina, Champagne, Frances A, Hyde, Thomas, Seyfried, Nicholas T, Shin, Joo Heon, Weinberger, Daniel R, Nemeroff, Charles B, Kleinman, Joel E, Ressler, Kerry J

“…The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major…”
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461. Digital Phenotyping Approaches for Tracking Clinical Symptoms in Serious Mental Illness by Chung, Yoonho, Gillis, Bryce, Eichi, Habiballah Rahimi, Holstein, Vincent, Rauch, Scott, Libenthal, Einat, Baker, Justin

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169. Natural Language Processing Analysis of Psychiatric Progress Notes: An Innovative Approach to Real-Time Unbiased Construct Quantification by Vogelgsang, Jonathan, Holstein, Vincent, Dan, Shu, Abston, Joshua, McCoy, Tom, Klengel, Torsten, Ressler, Kerry, Berretta, Sabina

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Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder by Hahn, Tim, Winter, Nils R., Ernsting, Jan, Gruber, Marius, Mauritz, Marco J., Fisch, Lukas, Leenings, Ramona, Sarink, Kelvin, Blanke, Julian, Holstein, Vincent, Emden, Daniel, Beisemann, Marie, Opel, Nils, Grotegerd, Dominik, Meinert, Susanne, Heindel, Walter, Witt, Stephanie, Rietschel, Marcella, Nöthen, Markus M., Forstner, Andreas J., Kircher, Tilo, Nenadic, Igor, Jansen, Andreas, Müller-Myhsok, Bertram, Andlauer, Till F. M., Walter, Martin, van den Heuvel, Martijn P., Jamalabadi, Hamidreza, Dannlowski, Udo, Repple, Jonathan

“…Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on…”
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An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling by Hahn, Tim, Ernsting, Jan, Winter, Nils R, Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Fisch, Lukas, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Hirsch, Jochen G, Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Kugel, Harald, Kircher, Tilo, Risse, Benjamin, Gaser, Christian, Cole, James H, Dannlowski, Udo, Berger, Klaus

“…The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain…”
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Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder by Repple, Jonathan, König, Amelie, de Lange, Siemon C., Opel, Nils, Redlich, Ronny, Meinert, Susanne, Grotegerd, Dominik, Mauritz, Marco, Hahn, Tim, Borgers, Tiana, Leehr, Elisabeth J., Winter, Nils, Goltermann, Janik, Enneking, Verena, Fingas, Stella M., Lemke, Hannah, Waltemate, Lena, Dohm, Katharina, Richter, Maike, Mehler, David M.A., Holstein, Vincent, Gruber, Marius, Nenadic, Igor, Krug, Axel, Brosch, Katharina, Schmitt, Simon, Stein, Frederike, Meller, Tina, Jansen, Andreas, Steinsträter, Olaf, Amare, Azmeraw T., Kircher, Tilo, Baune, Bernhard T., van den Heuvel, Martijn P., Dannlowski, Udo

“…Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared…”
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From Group-Differences to Single-Subject Probability: Conformal Prediction-based Uncertainty Estimation for Brain-Age Modeling by Ernsting, Jan, Winter, Nils R, Leenings, Ramona, Sarink, Kelvin, Barkhau, Carlotta B. C, Fisch, Lukas, Emden, Daniel, Holstein, Vincent, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Investigators, NAKO, Berger, Klaus, Risse, Benjamin, Dannlowski, Udo, Hahn, Tim

“…The brain-age gap is one of the most investigated risk markers for brain changes across disorders. While the field is progressing towards large-scale models,…”
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Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder by Hahn, Tim, Winter, Nils R, Ernsting, Jan, Gruber, Marius, Mauritz, Marco J, Fisch, Lukas, Leenings, Ramona, Sarink, Kelvin, Blanke, Julian, Holstein, Vincent, Emden, Daniel, Beisemann, Marie, Opel, Nils, Grotegerd, Dominik, Meinert, Susanne, Heindel, Walter, Witt, Stephanie, Rietschel, Marcella, Nöthen, Markus M, Forstner, Andreas J, Kircher, Tilo, Nenadic, Igor, Jansen, Andreas, Müller-Myhsok, Bertram, Andlauer, Till F. M, Walter, Martin, Heuvel, Martijn P. van den, Jamalabadi, Hamidreza, Dannlowski, Udo, Repple, Jonathan

“…Background: A therapeutic intervention in psychiatry can be viewed as an attempt to influence the brain's large-scale, dynamic network state transitions…”
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A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder by Hahn, Tim, Jamalabadi, Hamidreza, Emden, Daniel, Goltermann, Janik, Ernsting, Jan, Winter, Nils R, Fisch, Lukas, Leenings, Ramona, Sarink, Kelvin, Holstein, Vincent, Gruber, Marius, Grotegerd, Dominik, Meinert, Susanne, Dohm, Katharina, Leehr, Elisabeth J, Richter, Maike, Sindermann, Lisa, Enneking, Verena, Lemke, Hannah, Witt, Stephanie, Rietschel, Marcella, Brosch, Katharina, Pfarr, Julia-Katharina, Meller, Tina, Ringwald, Kai Gustav, Schmitt, Simon, Stein, Frederike, Nenadic, Igor, Kircher, Tilo, Müller-Myhsok, Bertram, Andlauer, Till F. M, Repple, Jonathan, Dannlowski, Udo, Opel, Nils

“…Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework…”
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An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling by Hahn, Tim, Ernsting, Jan, Winter, Nils R, Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Fisch, Lukas, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Hirsch, Jochen G, Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Consortium, German National Cohort Study Center, Kugel, Harald, Kircher, Tilo, Risse, Benjamin, Gaser, Christian, Cole, James H, Dannlowski, Udo, Berger, Klaus

“…The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain…”
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PHOTONAI -- A Python API for Rapid Machine Learning Model Development by Leenings, Ramona, Winter, Nils Ralf, Plagwitz, Lucas, Holstein, Vincent, Ernsting, Jan, Steenweg, Jakob, Gebker, Julian, Sarink, Kelvin, Emden, Daniel, Grotegerd, Dominik, Opel, Nils, Risse, Benjamin, Jiang, Xiaoyi, Dannlowski, Udo, Hahn, Tim

“…PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the…”
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Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks by Fisch, Lukas, Ernsting, Jan, Winter, Nils R, Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Wulms, Niklas, Minnerup, Heike, Hirsch, Jochen G, Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Peters, Annette, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Consortium, German National Cohort Study Center, Kugel, Harald, Baune, Bernhard T, Kircher, Tilo, Risse, Benjamin, Dannlowski, Udo, Berger, Klaus, Hahn, Tim

“…Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current…”
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