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1. Neurofilaments as biomarkers in neurological disorders — towards clinical application

Author

Khalil, Michael, Teunissen, Charlotte E., Lehmann, Sylvain, Otto, Markus, Piehl, Fredrik, Ziemssen, Tjalf, Bittner, Stefan, Sormani, Maria Pia, Gattringer, Thomas, Abu-Rumeileh, Samir, Thebault, Simon, Abdelhak, Ahmed, Green, Ari, Benkert, Pascal, Kappos, Ludwig, Comabella, Manuel, Tumani, Hayrettin, Freedman, Mark S., Petzold, Axel, Blennow, Kaj, Zetterberg, Henrik, Leppert, David, and Kuhle, Jens

Published
2024
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4. Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort

Author

Allen, Naomi, Aslam, Tariq, Atan, Denize, Balaskas, Konsantinos, Barman, Sarah A., Barrett, Jenny H., Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana O., Chakravarthy, Usha, Chan, Michelle, Chua, Sharon Y.L., Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew D., Doney, Alexander, Egan, Cathy, Ennis, Sarah, Foster, Paul, Fruttiger, Marcus, Gallacher, John E.J., Garway-Heath, David F., Gibson, Jane, Guggenheim, Jeremy A., Hammond, Chris J., Hardcastle, Alison, Harding, Simon P., Hogg, Ruth E., Hysi, Pirro, Keane, Pearse A., Khaw, Sir Peng T., Khawaja, Anthony P., Lascaratos, Gerassimos, Littlejohns, Thoams, Lotery, Andrew J., Luben, Robert, Luthert, Phil, Macgillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth J., McKibbin, Martin, Moore, Tony, Morgan, James E., O’Sullivan, Eoin, Oram, Richard, Owen, Chris G., Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Rahi, Jugnoo S., Rudnikca, Alicja R., Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Vitart, Veronique, Viswanathan, Ananth C., Weedon, Mike, Williams, Cathy, Williams, Katie, Woodside, Jayne V., Yates, Max M., Yip, Jennifer, Zheng, Yalin, Wagner, Siegfried K., Patel, Praveen J., Huemer, Josef, Khalid, Hagar, Stuart, Kelsey V., Chu, Colin J., Williamson, Dominic J., Struyven, Robbert R., Romero-Bascones, David, Foster, Paul J., Balaskas, Konstantinos, Cortina-Borja, Mario, Chapple, Iain, Dietrich, Thomas, and Denniston, Alastair K.

Published
2024
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5. Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department

Author

Fraser, Clare L., Micieli, Jonathan A., Costello, Fiona, Bénard-Séguin, Étienne, Yang, Hui, Chan, Carmen Kar Mun, Cheung, Carol Y, Chan, Noel CY, Hamann, Steffen, Gohier, Philippe, Vautier, Anaïs, Rougier, Marie-Bénédicte, Chiquet, Christophe, Vignal-Clermont, Catherine, Hage, Rabih, Khanna, Raoul Kanav, Tran, Thi Ha Chau, Lagrèze, Wolf Alexander, Jonas, Jost B, Ambika, Selvakumar, Fard, Masoud Aghsaei, La Morgia, Chiara, Carbonelli, Michele, Barboni, Piero, Carelli, Valerio, Romagnoli, Martina, Amore, Giulia, Nakamura, Makoto, Fumio, Takano, Petzold, Axel, Wenniger lj, Maillette de Buy, Kho, Richard, Fonseca, Pedro L., Bikbov, Mukharram M., Milea, Dan, Najjar, Raymond P, Ting, Daniel, Tang, Zhiqun, Loo, Jing Liang, Tow, Sharon, Singhal, Shweta, Vasseneix, Caroline, Wong, Tien Yin, Lamoureux, Ecosse, Yu Chen, Ching, Aung, Tin, Schmetterer, Leopold, Sanda, Nicolae, Thuman, Gabriele, Hwang, Jeong-Min, Vanikieti, Kavin, Suwan, Yanin, Padungkiatsagul, Tanyatuth, Yu-Wai-Man, Patrick, Jurkute, Neringa, Hong, Eun Hee, Biousse, Valerie, Newman, Nancy J., Peragallo, Jason H., Datillo, Michael, Kedar, Sachin, Lin, Mung Yan, Patil, Ajay, Aung, Andre, Boyko, Matthew, Alsakran, Wael Abdulraman, Zayani, Amani, Bouthour, Walid, Banc, Ana, Mosley, Rasha, Labella, Fernando, Miller, Neil R., Chen, John J., Mejico, Luis J., Kilangalanga, Janvier Ngoy, Biousse, Valérie, Najjar, Raymond P., Wright, David W., Keadey, Matthew T., Wong, Tien Y., and Bruce, Beau B.

Published
2024
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6. Associations between unilateral amblyopia in childhood and cardiometabolic disorders in adult life: a cross-sectional and longitudinal analysis of the UK Biobank

Author

Allen, Naomi, Aslam, Tariq, Atan, Denize, Balaskas, Konstantinos, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Chua, Sharon, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Foster, Paul, Fruttiger, Marcus, Gallacher, John, Garway-heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Tee Khaw, Sir Peng, Khawaja, Anthony, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luben, Robert, Luthert, Phil, Macgillivray, Tom, Mackie, Sarah, Madhusudhan, Savita, Mcguinness, Bernadette, Mckay, Gareth, Mckibbin, Martin, Moore, Tony, Morgan, James, O'sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Zheng, Yalin, Thomas, Mervyn, Wagner, Siegfried Karl, Bountziouka, Vasiliki, and Rahi, Jugnoo Sangeeta

Published
2024
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7. The BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) deep learning system can accurately identify pediatric papilledema on standard ocular fundus photographs

Author

Fraser, Clare L., Micieli, Jonathan A., Costello, Fiona, Étienne Bénard-Séguin, Yang, Hui, Mun Chan, Carmen Kar, Cheung, Carol Y., Chan, Noel CY., Hamann, Steffen, Gohier, Philippe, Vautier, Anaïs, Rougier, Marie-Bénédicte, Chiquet, Christophe, Vignal-Clermont, Catherine, Hage, Rabih, Khanna, Raoul Kanav, Chau Tran, Thi Ha, Lagrèze, Wolf Alexander, Jonas, Jost B., Ambika, Selvakumar, Fard, Masoud Aghsaei, La Morgia, Chiara, Carbonelli, Michele, Barboni, Piero, Carelli, Valerio, Romagnoli, Martina, Amore, Giulia, Nakamura, Makoto, Fumio, Takano, Petzold, Axel, de Buy Wenniger L.J., Maillette, Kho, Richard, Fonseca, Pedro L., Bikbov, Mukharram M., Milea, Dan, Najjar, Raymond P., Ting, Daniel, Tang, Zhiqun, Loo, Jing Liang, Tow, Sharon, Singhal, Shweta, Vasseneix, Caroline, Wong, Tien Yin, Lamoureux, Ecosse, Chen, Ching Yu, Aung, Tin, Schmetterer, Leopold, Sanda, Nicolae, Thuman, Gabriele, Hwang, Jeong-Min, Vanikieti, Kavin, Suwan, Yanin, Padungkiatsagul, Tanyatuth, Yu-Wai-Man, Patrick, Jurkute, Neringa, Hong, Eun Hee, Biousse, Valerie, Newman, Nancy J., Peragallo, Jason H., Datillo, Michael, Kedar, Sachin, Lin, Mung Yan, Patil, Ajay, Aung, Andre, Boyko, Matthew, Alsakran, Wael Abdulraman, Zayani, Amani, Bouthour, Walid, Banc, Ana, Mosley, Rasha, Labella, Fernando, Miller, Neil R., Chen, John J., Mejico, Luis J., Kilangalanga, Janvier Ngoy, Cioplean, Daniela, Dragomir, Mihaela, Chia, Audrey, and Biousse, Valérie

Published
2024
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8. The Association of Urinary Sodium Excretion with Glaucoma and Related Traits in a Large United Kingdom Population

Author

Aschard, Hugues, Chia, Mark, Chua, Sharon, Do, Ron, Foster, Paul, Kang, Jae, Kastner, Alan, Khawaja, Anthony, Kim, Jihye, Lentjes, Marleen, Luben, Robert, Madjedi, Kian, Montesano, Giovanni, Pasquale, Louis, Stuart, Kelsey, Warwick, Alasdair, Wiggs, Janey, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Fruttiger, Marcus, Garway-Heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Khaw, Peng Tee, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Moore, Tony, Morgan, James, O'Sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Aung, Tin, Burdon, Kathryn, Chen, Li, Cheng, Ching-Yu, Craig, Jamie, Cree, Angela, de Vries, Victor, Driessen, Sjoerd, Fingert, John, Gharahkhani, Puya, Hammond, Christopher, Hayward, Caroline, Hewitt, Alex, Jansonius, Nomdo, Jonansson, Fridbert, Jonas, Jost, Kass, Michael, Khor, Chiea, Klaver, Caroline, Koh, Jacyline, MacGregor, Stuart, Mackey, David, Mitchell, Paul, Pang, Calvin, Pasutto, Francesca, Pfeiffer, Norbert, Polašek, Ozren, Ramdas, Wishal, Schuster, Alexander, Segrè, Ayellet, Stefansson, Einer, Stefánsson, Kári, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, van Duijn, Cornelia, Vergroesen, Joëlle, Vithana, Eranga, Wilson, James, Wojciechowski, Robert, Wong, Tien, Young, Terri, Stuart, Kelsey V., Biradar, Mahantesh I., Luben, Robert N., Dhaun, Neeraj, Wagner, Siegfried K., Warwick, Alasdair N., Madjedi, Kian M., Pasquale, Louis R., Wiggs, Janey L., Kang, Jae H., Lentjes, Marleen A.H., Foster, Paul J., and Khawaja, Anthony P.

Published
2024
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11. The Association of Alcohol Consumption with Glaucoma and Related Traits: Findings from the UK Biobank

Author

Aschard, Hugues, Chia, Mark, Chua, Sharon, Do, Ron, Foster, Paul, Kang, Jae, Kastner, Alan, Khawaja, Anthony, Kim, Jihye, Lentjes, Marleen, Luben, Robert, Madjedi, Kian, Montesano, Giovanni, Pasquale, Louis, Stuart, Kelsey, Warwick, Alasdair, Wiggs, Janey, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Fruttiger, Marcus, Gallacher, John, Garway-Heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Khaw, Peng Tee, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Moore, Tony, Morgan, James, O'Sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Aung, Tin, Burdon, Kathryn, Chen, Li, Cheng, Ching-Yu, Craig, Jamie, Cree, Angela, de Vries, Victor, Driessen, Sjoerd, Fingert, John, Gharahkhani, Puya, Hammond, Christopher, Hayward, Caroline, Hewitt, Alex, Jansonius, Nomdo, Jonansson, Fridbert, Jonas, Jost, Kass, Michael, Khor, Chiea, Klaver, Caroline, Koh, Jacyline, MacGregor, Stuart, Mackey, David, Mitchell, Paul, Pang, Calvin, Pasutto, Francesca, Pfeiffer, Norbert, Polašek, Ozren, Ramdas, Wishal, Schuster, Alexander, Segrè, Ayellet, Stefansson, Einer, Stefánsson, Kári, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, van Duijn, Cornelia, Vergroesen, Joëlle, Vithana, Eranga, Wilson, James, Wojciechowski, Robert, Wong, Tien, Young, Terri, Stuart, Kelsey V., Luben, Robert N., Warwick, Alasdair N., Madjedi, Kian M., Patel, Praveen J., Biradar, Mahantesh I., Chia, Mark A., Pasquale, Louis R., Wiggs, Janey L., Kang, Jae H., Tran, Jessica H., Lentjes, Marleen A.H., Foster, Paul J., and Khawaja, Anthony P.

Published
2023
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12. A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Author

Bailey-Wilson, Joan E., Baird, Paul N., Barathi, Veluchamy A., Biino, Ginevra, Burdon, Kathryn P., Campbell, Harry, Chen, Li Jia, Cheng, Ching-Yu, Chew, Emily Y., Craig, Jamie E., Deangelis, Margaret M., Delcourt, Cécile, Ding, Xiaohu, Fan, Qiao, Fossarello, Maurizio, Foster, Paul J., Gharahkhani, Puya, Guggenheim, Jeremy A., Guo, Xiaobo, Haarman, Annechien E.G., Haller, Toomas, Hammond, Christopher J., Han, Xikun, Hayward, Caroline, He, Mingguang, Hewitt, Alex W., Hoang, Quan, Hysi, Pirro G., Iglesias, Adriana I., Igo, Robert P., Iyengar, Sudha K., Jonas, Jost B., Kähönen, Mika, Kaprio, Jaakko, Khawaja, Anthony P., Klein, Barbara E., Lass, Jonathan H., Lee, Kris, Lehtimäki, Terho, Lewis, Deyana, Li, Qing, Li, Shi-Ming, Lyytikäinen, Leo-Pekka, MacGregor, Stuart, Mackey, David A., Martin, Nicholas G., Meguro, Akira, Metspalu, Andres, Middlebrooks, Candace, Miyake, Masahiro, Mizuki, Nobuhisa, Musolf, Anthony, Nickels, Stefan, Oexle, Konrad, Pang, Chi Pui, Pärssinen, Olavi, Paterson, Andrew D., Pfeiffer, Norbert, Polasek, Ozren, Rahi, Jugnoo S., Raitakari, Olli, Rudan, Igor, Sahebjada, Srujana, Saw, Seang-Mei, Simpson, Claire L., Stambolian, Dwight, Tai, E-Shyong, Tedja, Milly S., Tideman, J. Willem L., Tsujikawa, Akitaka, van Duijn, Cornelia M., Verhoeven, Virginie J.M., Vitart, Veronique, Wang, Ningli, Wang, Ya Xing, Wedenoja, Juho, Wei, Wen Bin, Williams, Cathy, Williams, Katie M., Wilson, James F., Wojciechowski, Robert, Yam, Jason C.S., Yamashiro, Kenji, Yap, Maurice K.H., Yazar, Seyhan, Yip, Shea Ping, Young, Terri L., Zhou, Xiangtian, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Bunce, Catey, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Chua, Sharon, Cipriani, Valentina, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Foster, Paul, Fruttiger, Marcus, Gallacher, John, Garway-Heath, David, Gibson, Jane, Gore, Dan, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse A., Khaw, Peng Tee, Khawaja, Anthony, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Mitry, Danny, Moore, Tony, Morgan, James, Muthy, Zaynah, O'Sullivan, Eoin, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Tapp, Robyn, Thaung, Caroline, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Vernon, Stephen, Viswanathan, Ananth, Williams, Katie, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Clark, Rosie, Lee, Samantha Sze-Yee, Du, Ran, Wang, Yining, Kneepkens, Sander C.M., Charng, Jason, Huang, Yu, Hunter, Michael L., Jiang, Chen, Tideman, J.Willem L., Melles, Ronald B., Klaver, Caroline C.W., Choquet, Hélène, and Ohno-Matsui, Kyoko

Published
2023
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15. Diagnosis and classification of optic neuritis

Author

Petzold, Axel, Fraser, Clare L, Abegg, Mathias, Alroughani, Raed, Alshowaeir, Daniah, Alvarenga, Regina, Andris, Cécile, Asgari, Nasrin, Barnett, Yael, Battistella, Roberto, Behbehani, Raed, Berger, Thomas, Bikbov, Mukharram M, Biotti, Damien, Biousse, Valerie, Boschi, Antonella, Brazdil, Milan, Brezhnev, Andrei, Calabresi, Peter A, Cordonnier, Monique, Costello, Fiona, Cruz, Franz M, Cunha, Leonardo Provetti, Daoudi, Smail, Deschamps, Romain, de Seze, Jerome, Diem, Ricarda, Etemadifar, Masoud, Flores-Rivera, Jose, Fonseca, Pedro, Frederiksen, Jette, Frohman, Elliot, Frohman, Teresa, Tilikete, Caroline Froment, Fujihara, Kazuo, Gálvez, Alberto, Gouider, Riadh, Gracia, Fernando, Grigoriadis, Nikolaos, Guajardo, José M, Habek, Mario, Hawlina, Marko, Martínez-Lapiscina, Elena H, Hooker, Juzar, Hor, Jyh Yung, Howlett, William, Huang-Link, Yumin, Idrissova, Zhannat, Illes, Zsolt, Jancic, Jasna, Jindahra, Panitha, Karussis, Dimitrios, Kerty, Emilia, Kim, Ho Jin, Lagrèze, Wolf, Leocani, Letizia, Levin, Netta, Liskova, Petra, Liu, Yaou, Maiga, Youssoufa, Marignier, Romain, McGuigan, Chris, Meira, Dália, Merle, Harold, Monteiro, Mário L R, Moodley, Anand, Moura, Frederico, Muñoz, Silvia, Mustafa, Sharik, Nakashima, Ichiro, Noval, Susana, Oehninger, Carlos, Ogun, Olufunmilola, Omoti, Afekhide, Pandit, Lekha, Paul, Friedemann, Rebolleda, Gema, Reddel, Stephen, Rejdak, Konrad, Rejdak, Robert, Rodriguez-Morales, Alfonso J, Rougier, Marie-Bénédicte, Sa, Maria Jose, Sanchez-Dalmau, Bernardo, Saylor, Deanna, Shatriah, Ismail, Siva, Aksel, Stiebel-Kalish, Hadas, Szatmary, Gabriella, Ta, Linh, Tenembaum, Silvia, Tran, Huy, Trufanov, Yevgen, van Pesch, Vincent, Wang, An-Guor, Wattjes, Mike P, Willoughby, Ernest, Zakaria, Magd, Zvornicanin, Jasmin, Balcer, Laura, and Plant, Gordon T

Published
2022
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18. Normative Data and Conversion Equation for Spectral-Domain Optical Coherence Tomography in an International Healthy Control Cohort

Author

Kenney, Rachel, Liu, Mengling, Hasanaj, Lisena, Joseph, Binu, Al-Hassan, Abdullah A., Balk, Lisanne, Behbehani, Raed, Brandt, Alexander U., Calabresi, Peter A., Frohman, Elliot M., Frohman, Teresa, Havla, Joachim, Hemmer, Bernhard, Jiang, Hong, Knier, Benjamin, Korn, Thomas, Leocani, Letizia, Martínez-Lapiscina, Elena H., Papadopoulou, Athina, Paul, Friedemann, Petzold, Axel, Pisa, Marco, Villoslada, Pablo, Zimmermann, Hanna, Ishikawa, Hiroshi, Schuman, Joel S., Wollstein, Gadi, Chen, Yu, Saidha, Shiv, Thorpe, Lorna E., Galetta, Steven L., and Balcer, Laura J.

Published
2022
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19. Structural correlations between brain magnetic resonance image‐derived phenotypes and retinal neuroanatomy.

Author

Sun, Zihan, Zhang, Bing, Smith, Stephen, Atan, Denize, Khawaja, Anthony P., Stuart, Kelsey V., Luben, Robert N., Biradar, Mahantesh I., McGillivray, Thomas, Patel, Praveen J., Khaw, Peng T., Petzold, Axel, and Foster, Paul J.

Subjects
NEUROANATOMY, MAGNETIC resonance, PHENOTYPES, OPTICAL coherence tomography, MAGNETIC resonance imaging
Abstract

Background and purpose: The eye is a well‐established model of brain structure and function, yet region‐specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)‐derived phenotypes in UK Biobank. Methods: Participants with both quality‐controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image‐derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders. Results: Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10−5). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers. Conclusions: Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina–brain connections. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Differentiating glaucoma from chiasmal compression using optical coherence tomography: the macular naso-temporal ratio.

Author

Kleerekooper, Iris, Wagner, Siegfried K., Trip, S. Anand, Plant, Gordon T., Petzold, Axel, Keane, Pearse A., and Khawaja, Anthony P.

Abstract

Background/aims The analysis of visual field loss patterns is clinically useful to guide differential diagnosis of visual pathway pathology. This study investigates whether a novel index of macular atrophy patterns can discriminate between chiasmal compression and glaucoma. Methods A retrospective series of patients with preoperative chiasmal compression, primary open-angle glaucoma (POAG) and healthy controls. Macular optical coherence tomography (OCT) images were analysed for the macular ganglion cell and inner plexiform layer (mGCIPL) thickness. The nasal hemi-macula was compared with the temporal hemi-macula to derive the macular naso-temporal ratio (mNTR). Differences between groups and diagnostic accuracy were explored with multivariable linear regression and the area under the receiver operating characteristic curve (AUC). Results We included 111 individuals (31 with chiasmal compression, 30 with POAG and 50 healthy controls). Compared with healthy controls, the mNTR was significantly greater in POAG cases (ß=0.07, 95% CI 0.03 to 0.11, p=0.001) and lower in chiasmal compression cases (ß=-0.12, 95% CI -0.16 to -0.09, p<0.001), even though overall mGCIPL thickness did not discriminate between these pathologies (p=0.36). The mNTR distinguished POAG from chiasmal compression with an AUC of 95.3% (95% CI 90% to 100%). The AUCs when comparing healthy controls to POAG and chiasmal compression were 79.0% (95% CI 68% to 90%) and 89.0% (95% CI 80% to 98%), respectively. Conclusions The mNTR can distinguish between chiasmal compression and POAG with high discrimination. This ratio may provide utility over-and-above previously reported sectoral thinning metrics. Incorporation of mNTR into the output of OCT instruments may aid earlier diagnosis of chiasmal compression. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Myelin-oligodendrocyte glycoprotein antibody-associated disease

Author

Marignier, Romain, Hacohen, Yael, Cobo-Calvo, Alvaro, Pröbstel, Anne-Katrin, Aktas, Orhan, Alexopoulos, Harry, Amato, Maria-Pia, Asgari, Nasrin, Banwell, Brenda, Bennett, Jeffrey, Brilot, Fabienne, Capobianco, Marco, Chitnis, Tanuja, Ciccarelli, Olga, Deiva, Kumaran, De Sèze, Jérôme, Fujihara, Kazuo, Jacob, Anu, Kim, Ho Jin, Kleiter, Ingo, Lassmann, Hans, Leite, Maria-Isabel, Linington, Christopher, Meinl, Edgar, Palace, Jacqueline, Paul, Friedemann, Petzold, Axel, Pittock, Sean, Reindl, Markus, Sato, Douglas Kazutoshi, Selmaj, Krzysztof, Siva, Aksel, Stankoff, Bruno, Tintore, Mar, Traboulsee, Anthony, Waters, Patrick, Waubant, Emmanuelle, Weinshenker, Brian, Derfuss, Tobias, Vukusic, Sandra, and Hemmer, Bernhard

Published
2021
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24. Leveraging Visual Outcome Measures to Advance Therapy Development in Neuroimmunologic Disorders

Author

Graves, Jennifer S., Oertel, Frederike Cosima, Van der Walt, Anneke, Collorone, Sara, Sotirchos, Elias S., Pihl-Jensen, Gorm, Albrecht, Philipp, Yeh, E. Ann, Saidha, Shiv, Frederiksen, Jette, Newsome, Scott Douglas, Paul, Friedemann, Petzold, Axel, Villoslada, Pablo, Brandt, Alex, Bermel, Robert, Kenney, Rachel, Calabresi, Peter, and Balcer, Laura

Published
2022
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26. Retinal Optical Coherence Tomography in Neuromyelitis Optica

Author

Oertel, Frederike Cosima, Specovius, Svenja, Zimmermann, Hanna G., Chien, Claudia, Motamedi, Seyedamirhosein, Bereuter, Charlotte, Cook, Lawrence, Lana Peixoto, Marco Aurélio, Fontanelle, Mariana Andrade, Kim, Ho Jin, Hyun, Jae-Won, Palace, Jacqueline, Roca-Fernandez, Adriana, Leite, Maria Isabel, Sharma, Srilakshmi, Ashtari, Fereshteh, Kafieh, Rahele, Dehghani, Alireza, Pourazizi, Mohsen, Pandit, Lekha, DʼCunha, Anitha, Aktas, Orhan, Ringelstein, Marius, Albrecht, Philipp, May, Eugene, Tongco, Caryl, Leocani, Letizia, Pisa, Marco, Radaelli, Marta, Martinez-Lapiscina, Elena H., Stiebel-Kalish, Hadas, Siritho, Sasitorn, de Seze, Jérome, Senger, Thomas, Havla, Joachim, Marignier, Romain, Calvo, Alvaro Cobo, Bichuetti, Denis, Tavares, Ivan Maynart, Asgari, Nasrin, Soelberg, Kerstin, Altintas, Ayse, Yildirim, Rengin, Tanriverdi, Uygur, Jacob, Anu, Huda, Saif, Rimler, Zoe, Reid, Allyson, Mao-Draayer, Yang, Soto de Castillo, Ibis, Petzold, Axel, Green, Ari J., Yeaman, Michael R., Smith, Terry, Brandt, Alexander U., and Paul, Friedemann

Published
2021
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28. Partial Parallelism Plots.

Author

Petzold, Axel

Subjects
CLINICAL trials, RESEARCH personnel, REGRESSION analysis, BIOMARKERS, CONFOUNDING variables, CLINICAL medicine
Abstract

Featured Application: This article proposes a novel graphical approach for the the assessment of parallelism of biomarker tests that takes into consideration situations where parallelism is partially lacking. The new approach expands on earlier observations and criticism of the limitations of statistical methods included in the guidelines of regulatory authorities. Researchers in the field concur on emphasising the importance of ensuring the accuracy and reliability; a pertinent point which still remains to be addressed. To this purpose, two primary computational approaches are discussed: (a) statistical assessment and (b) visual assessment. Statistical methods, such as regression analysis and parallelism/non-parallelism indexes, offer precision and objectivity, making them suitable for large datasets and high accuracy requirements. They can detect subtle differences in parallelism that may be missed by visual assessment. However, they assume a linear relationship between analyte concentration and assay response, which may not always hold true. Visual assessment relies on interpreting graphs or charts depicting the biomarker–concentration–response relationship. It is intuitive and can quickly identify gross deviations from partial parallelism, making it useful for screening biomarker assays. Visual assessment may detect non-parallelism due to confounding factors that statistical methods might miss. The graphical method proposed here suggests using partial parallelism plots, which visually depict the relationship between biomarker concentration and assay response for each sample. These plots enable the identification of non-parallelism caused by analytical issues or confounding factors. They assist in determining the optimal range of dilutions for each sample and provide a language that is easily understood by researchers, regulatory authorities, and technicians. For regulatory authorities, this document provides valuable insights into the assessment of partial parallelism for biomarker tests. It highlights the need for both statistical and visual assessment methods to evaluate parallelism accurately. The proposed use of partial parallelism plots can aid in visualising and understanding the relationship between biomarker concentration and assay response. By considering these plots during the evaluation of biomarker assays, regulatory authorities can ensure the accuracy, reliability, and suitability of these tests as trial outcome measures and for clinical use. Demonstrating parallelism in quantitative laboratory tests is crucial to ensure accurate reporting of data and minimise risks to patients. Regulatory authorities make the demonstration of parallelism before clinical use approval mandate. However, achieving statistical parallelism can be arduous, especially when parallelism is limited to a subrange of the data. To address potential biases and confounds, I propose a simple graphical method, the Partial Parallelism Plot, to demonstrate partial parallelism. The proposed method offers ease of understanding, intuitiveness, and graphical simplicity. It enables the graphical assessment of quantitative data risk when parallelism is lacking within a defined range. As parallelism may not be consistent across the entire analytical range, the plots focus on partial parallelism. The method can readily be programmed into graphical applications for enhanced interactivity. By providing a clear graphical representation, the method allows researchers to ascertain the presence of parallelism in laboratory tests, thus aiding in the validation process for trials and clinical applications. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Exploring Vitreous Haze as a Potential Biomarker for Accelerated Glymphatic Outflow and Neurodegeneration in Multiple Sclerosis: A Cross-Sectional Study.

Author

Kaçar, Sezgi, Coric, Danko, Ometto, Giovanni, Montesano, Giovanni, Denniston, Alastair K., Keane, Pearse A., Uitdehaag, Bernard M. J., Crabb, David P., Schoonheim, Menno M., Petzold, Axel, and Strijbis, Eva M. M.

Subjects
MULTIPLE sclerosis, OPTICAL coherence tomography, NEURODEGENERATION, GENERALIZED estimating equations, HAZE, OPTIC neuritis
Abstract

Background: The glymphatic system removes neurodegenerative debris. The ocular glymphatic outflow is from the eye to the proximal optic nerve. In multiple sclerosis (MS), atrophy of the optic nerve increases the glymphatic outflow space. Here, we tested whether vitreous haze (VH) can provide novel insights into the relationship between neurodegeneration and the ocular glymphatic system in MS. Methods: This cross-sectional study comprised 315 persons with MS and 87 healthy controls (HCs). VH was quantified from optical coherence tomography (OCT) volume scans. Neurodegeneration was determined on three-dimensional T1 (3DT1) MRI, lesion detection on fluid-attenuated inversion (FLAIR), and layer thickness on OCT. Generalized estimating equations, corrected for age, were used to analyze associations between VH and metrics for neurodegeneration, demographics, and clinical scales. Group differences were determined between mild, moderate, and severe disability. Results: On the group level, VH scores were comparable between MS and control (p = 0.629). In MS, VH scores declined with disease duration (β = −0.009, p = 0.004) and age (β = −0.007, p = 0.001). There was no relation between VH scores and higher age in HCs. In MS patients, VH was related to normalized gray (NGMV, β = 0.001, p = 0.011) and white matter volume (NWMV, β = 0.001, p = 0.003), macular ganglion cell–inner plexiform layer thickness (mGCIPL, β = 0.006, p < 0.001), and peripapillary retinal nerve fiber layer thickness (pRNFL, β = 0.004, p = 0.008). VH was significantly lower in severe compared to mild disability (mean difference −28.86%, p = 0.058). Conclusions: There is a correlation between VH on OCT and disease duration, more severe disability and lower brain volumes in MS. Biologically, these relationships suggest accelerated glymphatic clearance with disease-related atrophy. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Peripheral Neuropathy in Virologically Suppressed People Living with HIV: Evidence from the PIVOT Trial.

Author

Schuldt, Anna L., Bern, Henry, Hart, Melanie, Gompels, Mark, Winston, Alan, Clarke, Amanda, Chen, Fabian, Stöhr, Wolfgang, Heslegrave, Amanda, Paton, Nicholas I., Petzold, Axel, and Arenas-Pinto, Alejandro

Subjects
PERIPHERAL neuropathy, HIV-positive persons, CD4 lymphocyte count, HIV infections, BLOOD sugar
Abstract

The aim of this study is to identify the factors associated with peripheral neuropathy and to explore neurofilament light chain (NfL) as a biomarker for peripheral neuropathy (PN) in effectively virologically suppressed adults living with HIV. All protease inhibitor monotherapy versus ongoing triple therapy in the long-term management of HIV infection (PIVOT) trial participants with data on PN at baseline were included in the study. NfL plasma levels (pNfL) were measured in a sub-set of participants. Multivariable logistic regression was used to examine the associations of PN with potential risk factors (including age, sex, nadir CD4 cell count, history of dideoxynucleoside (d-drugs) exposure, and blood glucose levels) and NfL levels. Of the 585 participants included, 131 (22.4%) reported PN during the study period (median of 44 months). The participants were predominantly male (76.6%), White (68.2%), and virologically suppressed for a median period of 37 months (range of 20–63) before recruitment. The age at baseline was 44.3 years (standard deviation (SD) of 9.2). PN was independently associated with age (adjusted odds ratio (aOR) = 1.35, 95% CI of 1.20–1.52; additional 5 years), history of d-drugs (aOR 1.88, 95% CI of 1.12–3.16), height (aOR 1.19, 95% CI of 1.05–1.35; additional 5 cm), nadir CD4 cell count (aOR 1.10 CI of 1.00–1.20; 50 cells fewer), and metabolic syndrome (aOR 2.31, 95% CI of 1.27 4.20), but not pNfL. The excess risk for PN associated with d-drug use remains after the exposure has stopped for years, suggesting non-reversible toxicity. In people with HIV, metabolic syndrome is independently associated with PN. There was no additional value for pNfL as a screening test for peripheral neuropathy in effectively virologically suppressed adults living with HIV. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Peripherin is a biomarker of axonal damage in peripheral nervous system disease.

Author

Keddie, Stephen, Smyth, Duncan, Keh, Ryan Y S, Chou, Michael K L, Grant, Donna, Surana, Sunaina, Heslegrave, Amanda, Zetterberg, Henrik, Wieske, Luuk, Michael, Milou, Eftimov, Filip, Bellanti, Roberto, Rinaldi, Simon, Hart, Melanie S, Petzold, Axel, and Lunn, Michael P

Subjects
PERIPHERAL neuropathy, MOTOR neurons, CHRONIC inflammatory demyelinating polyradiculoneuropathy, PERIPHERAL nervous system, INTERMEDIATE filament proteins, POLYNEUROPATHIES
Abstract

Valid, responsive blood biomarkers specific to peripheral nerve damage would improve management of peripheral nervous system (PNS) diseases. Neurofilament light chain (NfL) is sensitive for detecting axonal pathology but is not specific to PNS damage, as it is expressed throughout the PNS and CNS. Peripherin, another intermediate filament protein, is almost exclusively expressed in peripheral nerve axons. We postulated that peripherin would be a promising blood biomarker of PNS axonal damage. We demonstrated that peripherin is distributed in sciatic nerve, and to a lesser extent spinal cord tissue lysates, but not in brain or extra-neural tissues. In the spinal cord, anti-peripherin antibody bound only to the primary cells of the periphery (anterior horn cells, motor axons and primary afferent sensory axons). In vitro models of antibody-mediated axonal and demyelinating nerve injury showed marked elevation of peripherin levels only in axonal damage and only a minimal rise in demyelination. We developed an immunoassay using single molecule array technology for the detection of serum peripherin as a biomarker for PNS axonal damage. We examined longitudinal serum peripherin and NfL concentrations in individuals with Guillain-Barré syndrome (GBS, n = 45, 179 time points), chronic inflammatory demyelinating polyradiculoneuropathy (CIDP, n = 35, 70 time points), multiple sclerosis (n = 30), dementia (as non-inflammatory CNS controls, n = 30) and healthy individuals (n = 24). Peak peripherin levels were higher in GBS than all other groups (median 18.75 pg/ml versus < 6.98 pg/ml, P < 0.0001). Peak NfL was highest in GBS (median 220.8 pg/ml) and lowest in healthy controls (median 5.6 pg/ml), but NfL did not distinguish between CIDP (17.3 pg/ml), multiple sclerosis (21.5 pg/ml) and dementia (29.9 pg/ml). While peak NfL levels were higher with older age (rho = +0.39, P < 0.0001), peak peripherin levels did not vary with age. In GBS, local regression analysis of serial peripherin in the majority of individuals with three or more time points of data (16/25) displayed a rise-and-fall pattern with the highest value within the first week of initial assessment. Similar analysis of serial NfL concentrations showed a later peak at 16 days. Group analysis of serum peripherin and NfL levels in GBS and CIDP patients were not significantly associated with clinical data, but in some individuals with GBS, peripherin levels appeared to better reflect clinical outcome measure improvement. Serum peripherin is a promising new, dynamic and specific biomarker of acute PNS axonal damage. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Identification and treatment of the visual processing asymmetry in MS patients with optic neuritis: The Pulfrich phenomenon

Author

Sobhanian, Millad J., Agarwal, Rohit, Meltzer, Ethan, Kildebeck, Eric, Frohman, Benjamin S., Frohman, Ashley N., Galetta, Steven L., Saidha, Shiv, White, Owen, Villoslada, Pablo, Paul, Friedemann, Petzold, Axel, Rennaker, Robert L., Martinez-Lapiscina, Elena H., Balcer, Laura J., Kardon, Randy, Frohman, Elliot M., and Frohman, Teresa C.

Published
2018
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35. Cohort profile:rationale and methods of UK Biobank repeat imaging study eye measures to study dementia

Author

Foster, Paul J., Atan, Denize, Khawaja, Anthony, Lotery, Andrew, MacGillivray, Tom, Owen, Christopher G., Patel, Praveen J., Petzold, Axel, Rudnicka, Alicja, Sun, Zihan, Sheard, Simon, and Allen, Naomi

Abstract

PURPOSE: The retina provides biomarkers of neuronal and vascular health that offer promising insights into cognitive ageing, mild cognitive impairment and dementia. This article described the rationale and methodology of eye and vision assessments with the aim of supporting the study of dementia in the UK Biobank Repeat Imaging study. PARTICIPANTS: UK Biobank is a large-scale, multicentre, prospective cohort containing in-depth genetic, lifestyle, environmental and health information from half a million participants aged 40-69 enrolled in 2006-2010 across the UK. A subset (up to 60 000 participants) of the cohort will be invited to the UK Biobank Repeat Imaging Study to collect repeated brain, cardiac and abdominal MRI scans, whole-body dual-energy X-ray absorptiometry, carotid ultrasound, as well as retinal optical coherence tomography (OCT) and colour fundus photographs. FINDINGS TO DATE: UK Biobank has helped make significant advances in understanding risk factors for many common diseases, including for dementia and cognitive decline. Ophthalmic genetic and epidemiology studies have also benefited from the unparalleled combination of very large numbers of participants, deep phenotyping and longitudinal follow-up of the cohort, with comprehensive health data linkage to disease outcomes. In addition, we have used UK Biobank data to describe the relationship between retinal structures, cognitive function and brain MRI-derived phenotypes. FUTURE PLANS: The collection of eye-related data (eg, OCT), as part of the UK Biobank Repeat Imaging study, will take place in 2022-2028. The depth and breadth and longitudinal nature of this dataset, coupled with its open-access policy, will create a major new resource for dementia diagnostic discovery and to better understand its association with comorbid diseases. In addition, the broad and diverse data available in this study will support research into ophthalmic diseases and various other health outcomes beyond dementia.

Published
2023

37. Neurofilaments as biomarkers in neurological disorders

Author

Khalil, Michael, Teunissen, Charlotte E., Otto, Markus, Piehl, Fredrik, Sormani, Maria Pia, Gattringer, Thomas, Barro, Christian, Kappos, Ludwig, Comabella, Manuel, Fazekas, Franz, Petzold, Axel, Blennow, Kaj, Zetterberg, Henrik, and Kuhle, Jens

Published
2018
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42. Remodeling of the neuromuscular junction in myasthenia gravis increases serum neurofilament heavy chain levels.

Author

Wong, Sui H. and Petzold, Axel

Abstract

Introduction/Aims: In myasthenia gravis, prolonged muscle denervation causes muscle atrophy. We re‐visited this observation using a biomarker hypothesis. We tested if serum neurofilament heavy chain levels, a biomarker for axonal degeneration, were elevated in myasthenia gravis. Methods: We enrolled 70 patients with isolated ocular myasthenia gravis and 74 controls recruited from patients in the emergency department. Demographic data were collected alongside serum samples. Serum samples were analyzed by enzyme‐linked immunosorbent assay (ELISA) for the neurofilament heavy chain (NfH‐SMI35). The statistical analyses included group comparisons, receiver operator characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values. Results: Serum neurofilament heavy chain levels were significantly (p < 0.0001) higher in individuals with myasthenia gravis (0.19 ng/mL) than in healthy control subjects (0.07 ng/mL). A ROC AUC optimized cutoff level of 0.06 ng/mL gave a diagnostic sensitivity of 82%, specificity of 76%, positive predictive value of 0.77 and a negative predictive value of 0.81. Discussion: The increase of serum neurofilament heavy chain levels in myasthenia gravis is consistent with observations of muscle denervation. We suggest that there is ongoing remodeling of the neuromuscular junction in myasthenia gravis. Longitudinal quantification of neurofilament isoform levels will be needed to investigate the prognostic value and potentially guide treatment decisions. [ABSTRACT FROM AUTHOR]

Published
2023
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43. novel eye-movement impairment in multiple sclerosis indicating widespread cortical damage.

Author

Bijvank, Jenny A Nij, Hof, Sam N, Prouskas, Stefanos E, Schoonheim, Menno M, Uitdehaag, Bernard M J, Rijn, Laurentius J van, and Petzold, Axel

Subjects
MULTIPLE sclerosis, SACCADIC eye movements, VISUAL evoked potentials, VISION, OPTIC neuritis, NEUROPROSTHESES
Abstract

In multiple sclerosis, remyelination trials have yet to deliver success like that achieved for relapse rates with disease course modifying treatment trials. The challenge is to have a clinical, functional outcome measure. Currently, there are none that have been validated, other than visual evoked potentials in optic neuritis. Like vision, quick eye movements (saccades) are heavily dependent on myelination. We proposed that it is possible to extrapolate from demyelination of the medial longitudinal fasciculus in the brainstem to quantitative assessment of cortical networks governing saccadic eye movements in multiple sclerosis. We have developed and validated a double-step saccadic test, which consists of a pair of eye movements towards two stimuli presented in quick succession (the demonstrate eye movement networks with saccades protocol). In this single-centre, cross-sectional cohort study we interrogated the structural and functional relationships of double-step saccades in multiple sclerosis. Data were collected for double-step saccades, cognitive function (extended Rao's Brief Repeatable Battery), disability (Expanded Disability Status Scale) and visual functioning in daily life (National Eye Institute Visual Function Questionnaire). MRI was used to quantify grey matter atrophy and multiple sclerosis lesion load. Multivariable linear regression models were used for analysis of the relationships between double-step saccades and clinical and MRI metrics. We included 209 individuals with multiple sclerosis (mean age 54.3 ± 10.5 years, 58% female, 63% relapsing-remitting multiple sclerosis) and 60 healthy control subjects (mean age 52.1 ± 9.2 years, 53% female). The proportion of correct double-step saccades was significantly reduced in multiple sclerosis (mean 0.29 ± 0.22) compared to controls (0.45 ± 0.22, P < 0.001). Consistent with this, there was a significantly larger double-step dysmetric saccadic error in multiple sclerosis (mean vertical error −1.18 ± 1.20°) compared to controls (−0.54 ± 0.86°, P < 0.001). Impaired double-step saccadic metrics were consistently associated with more severe global and local grey matter atrophy (correct responses—cortical grey matter: β = 0.42, P < 0.001), lesion load (vertical error: β = −0.28, P < 0.001), progressive phenotypes, more severe physical and cognitive impairment (correct responses—information processing: β = 0.46, P < 0.001) and visual functioning. In conclusion, double-step saccades represent a robust metric that revealed a novel eye-movement impairment in individuals with multiple sclerosis. Double-step saccades outperformed other saccadic tasks in their statistical relationship with clinical, cognitive and visual functioning, as well as global and local grey matter atrophy. Double-step saccades should be evaluated longitudinally and tested as a potential novel outcome measure for remyelination trials in multiple sclerosis. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Automated detection of hyperreflective foci in the outer nuclear layer of the retina.

Author

Schmidt, Mathias Falck, Christensen, Jakob Lønborg, Dahl, Vedrana Andersen, Toosy, Ahmed, Petzold, Axel, Hanson, James V. M., Schippling, Sven, Frederiksen, Jette Lautrup, and Larsen, Michael

Subjects
RETINA, CONVOLUTIONAL neural networks, OPTICAL coherence tomography, IMAGE analysis, DIABETIC retinopathy
Abstract

Purpose: Hyperreflective foci are poorly understood transient elements seen on optical coherence tomography (OCT) of the retina in both healthy and diseased eyes. Systematic studies may benefit from the development of automated tools that can map and track such foci. The outer nuclear layer (ONL) of the retina is an attractive layer in which to study hyperreflective foci as it has no fixed hyperreflective elements in healthy eyes. In this study, we intended to evaluate whether automated image analysis can identify, quantify and visualize hyperreflective foci in the ONL of the retina. Methods: This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non‐proliferative diabetic retinopathy. In total, 2596 OCT B‐scan were obtained. An image analysis blob detector algorithm was used to detect candidate foci, and a convolutional neural network (CNN) trained on a manually labelled subset of data was then used to select those candidate foci in the ONL that fitted the characteristics of the reference foci best. Results: In the manually labelled data set, the blob detector found 2548 candidate foci, correctly detecting 350 (89%) out of 391 manually labelled reference foci. The accuracy of CNN classifier was assessed by manually splitting the 2548 candidate foci into a training and validation set. On the validation set, the classifier obtained an accuracy of 96.3%, a sensitivity of 88.4% and a specificity of 97.5% (AUC 0.989). Conclusion: This study demonstrated that automated image analysis and machine learning methods can be used to successfully identify, quantify and visualize hyperreflective foci in the ONL of the retina on OCT scans. [ABSTRACT FROM AUTHOR]

Published
2023
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