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19 results on '"Nestrašil, Igor"'

5. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author

Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Wheeler-Kingshott, Claudia A.M. Gandini, D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., and Descoteaux, Maxime

Published
2021
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6. Quantitative brain morphometry identifies cerebellar, cortical, and subcortical gray and white matter atrophy in late‐onset Tay‐Sachs disease

Author

Májovská, Jitka, primary, Nestrašil, Igor, additional, Ahmed, Alia, additional, Bondy, Monica T., additional, Klempíř, Jiří, additional, Jahnová, Helena, additional, Schneider, Susanne A., additional, Horáková, Dana, additional, Krásenský, Jan, additional, Ješina, Pavel, additional, Vaneckova, Manuela, additional, Nascene, David R., additional, Whitley, Chester B., additional, Jarnes, Jeanine R., additional, Magner, Martin, additional, and Dušek, Petr, additional

Published
2023
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7. Quantitative brain morphometry identifies cerebellar, cortical, and subcortical gray and white matter atrophy in late‐onset Tay‐Sachs disease.

Author

Májovská, Jitka, Nestrašil, Igor, Ahmed, Alia, Bondy, Monica T., Klempíř, Jiří, Jahnová, Helena, Schneider, Susanne A., Horáková, Dana, Krásenský, Jan, Ješina, Pavel, Vaneckova, Manuela, Nascene, David R., Whitley, Chester B., Jarnes, Jeanine R., Magner, Martin, and Dušek, Petr

Abstract

Cerebellar atrophy is a characteristic sign of late‐onset Tay‐Sachs disease (LOTS). Other structural neuroimaging abnormalities are inconsistently reported. Our study aimed to perform a detailed whole‐brain analysis and quantitatively characterize morphometric changes in LOTS patients. Fourteen patients (8 M/6F) with LOTS from three centers were included in this retrospective study. For morphometric brain analyses, we used deformation‐based morphometry, voxel‐based morphometry, surface‐based morphometry, and spatially unbiased cerebellar atlas template. The quantitative whole‐brain morphometric analysis confirmed the finding of profound pontocerebellar atrophy with most affected cerebellar lobules V and VI in LOTS patients. Additionally, the atrophy of structures mainly involved in motor control, including bilateral ventral and lateral thalamic nuclei, primary motor and sensory cortex, supplementary motor area, and white matter regions containing corticospinal tract, was present. The atrophy of the right amygdala, hippocampus, and regions of occipital, parietal and temporal white matter was also observed in LOTS patients in contrast with controls (p < 0.05, FWE corrected). Patients with dysarthria and those initially presenting with ataxia had more severe cerebellar atrophy. Our results show predominant impairment of cerebellar regions responsible for speech and hand motor function in LOTS patients. Widespread morphological changes of motor cortical and subcortical regions and tracts in white matter indicate abnormalities in central motor circuits likely coresponsible for impaired speech and motor function. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

Author

Labounek, René, Wu, Zhuolin, Bridwell, David, Brázdil, Milan, Jan, Jiří, Nestrašil, Igor, Labounek, René, Wu, Zhuolin, Bridwell, David, Brázdil, Milan, Jan, Jiří, and Nestrašil, Igor

Abstract

Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high 4 band and low 1 band) demonstrated significant negative linear relationship (pFWE < 0.05) to the frequent stimulus and three patterns (two low 2 and 3 bands, and narrow 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For 4, 1, and 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were

Published
2021

19. Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study.

Author

Labounek R, Bondy MT, Paulson AL, Bédard S, Abramovic M, Alonso-Ortiz E, Atcheson NT, Barlow LR, Barry RL, Barth M, Battiston M, Büchel C, Budde MD, Callot V, Combes A, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak AV, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers JM, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler H, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Laganà MM, Laule C, Law CSW, Leutritz T, Liu Y, Llufriu S, Mackey S, Martin AR, Martinez-Heras E, Mattera L, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley GW, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber KA 2nd, Weiskopf N, Wise RG, Wyss PO, Xu J, Cohen-Adad J, Lenglet C, and Nestrašil I

Abstract

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure., Competing Interests: Declaration of interests Since June 2022, Dr. A.K. Smith has been employed by GE HealthCare. This article was co-authored by Dr. Smith in his personal capacity. The opinions expressed in the article are his in and do not necessarily reflect the views of GE HealthCare. Since August 2022, Dr. M. M. Laganà has been employed by Canon Medical Systems srl, Rome, Italy. This article was co-authored by Dr. M. M. Laganà in her personal capacity. The opinions expressed in the article are her own and do not necessarily reflect the views of Canon Medical Systems. Since September 2023, Dr. Papp has been an employee of Siemens Healthcare AB, Sweden. This article was co-authored by Dr. Papp in his personal capacity. The views and opinions expressed in this article are his own and do not necessarily reflect the views of Siemens Healthcare AB, or Siemens Healthineers AG. Since January 2024, Dr. Barry has been employed by the National Institute of Biomedical Imaging and Bioengineering at the NIH. This article was co-authored by Robert Barry in his personal capacity. The opinions expressed in the article are his own and do not necessarily reflect the views of the NIH, the Department of Health and Human Services, or the United States government. Guillaume Gilbert is an employee of Philips Healthcare. S Llufriu received compensation for consulting services and speaker honoraria from Biogen Idec, Novartis, Bristol Myer Squibb Genzyme, Sanofi Jansen and Merck. The Max Planck Institute for Human Cognitive and Brain Sciences and Wellcome Centre for Human Neuroimaging have institutional research agreements with Siemens Healthcare. NW holds a patent on acquisition of MRI data during spoiler gradients (US 10,401,453 B2). NW was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses. The other authors declare no competing interests.

Published
2024
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