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44 results on '"Moia, Stefano"'

1. Frontostriatal salience network expansion in individuals in depression

Author

Lynch, Charles J., Elbau, Immanuel G., Ng, Tommy, Ayaz, Aliza, Zhu, Shasha, Wolk, Danielle, Manfredi, Nicola, Johnson, Megan, Chang, Megan, Chou, Jolin, Summerville, Indira, Ho, Claire, Lueckel, Maximilian, Bukhari, Hussain, Buchanan, Derrick, Victoria, Lindsay W., Solomonov, Nili, Goldwaser, Eric, Moia, Stefano, Caballero-Gaudes, Cesar, Downar, Jonathan, Vila-Rodriguez, Fidel, Daskalakis, Zafiris J., Blumberger, Daniel M., Kay, Kendrick, Aloysi, Amy, Gordon, Evan M., Bhati, Mahendra T., Williams, Nolan, Power, Jonathan D., Zebley, Benjamin, Grosenick, Logan, Gunning, Faith M., and Liston, Conor

Published
2024
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2. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author

Gau, Rémi, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine L, Bilgin, Isil P, Yang, Yu-Fang, Huntenburg, Julia M, Bayer, Johanna MM, Bethlehem, Richard AI, Rhoads, Shawn A, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael P, Huijser, Dorien C, Sandström, Malin S, Herholz, Peer, Nastase, Samuel A, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula P, Mancini, Matteo, Shine, James M, O’Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal S, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly G, Nolan, Christopher R, Demeter, Damion V, Barrios, Fernando A, Merchant, Junaid S, McDevitt, Elizabeth A, Oostenveld, Robert, Craddock, R Cameron, Rokem, Ariel, Doyle, Andrew, Ghosh, Satrajit S, Nikolaidis, Aki, Stanley, Olivia W, Uruñuela, Eneko, Community, The Brainhack, Anousheh, Nasim, Arnatkeviciute, Aurina, Auzias, Guillaume, Bachar, Dipankar, Bannier, Elise, Basanisi, Ruggero, Basavaraj, Arshitha, Bedini, Marco, Bellec, Pierre, Benn, R Austin, Berluti, Kathryn, Bollmann, Steffen, Bollmann, Saskia, Bradley, Claire, Brown, Jesse, Buchweitz, Augusto, Callahan, Patrick, Chan, Micaela Y, Chandio, Bramsh Q, Cheng, Theresa, Chopra, Sidhant, Chung, Ai Wern, Close, Thomas G, Combrisson, Etienne, Cona, Giorgia, Constable, R Todd, Cury, Claire, Dadi, Kamalaker, Damasceno, Pablo F, Das, Samir, De Vico Fallani, Fabrizio, DeStasio, Krista, Dickie, Erin W, Dorfschmidt, Lena, Duff, Eugene P, DuPre, Elizabeth, Dziura, Sarah, Esper, Nathalia B, Esteban, Oscar, Fadnavis, Shreyas, Flandin, Guillaume, Flannery, Jessica E, and Flournoy, John

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Communication, Congresses as Topic, Internet, Practice Guidelines as Topic, Brainhack Community, Brainhack, best practices, collaboration, community building, hackathon, inclusivity, neuroscience, open science, reproducibility, training, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Published
2021

8. Quantitative Deconvolution of fMRI Data with Multi-echo Sparse Paradigm Free Mapping

Author

Caballero-Gaudes, César, Moia, Stefano, Bandettini, Peter A., Gonzalez-Castillo, Javier, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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10. Expansion of a frontostriatal salience network in individuals with depression

Author

Lynch, Charles Joseph, primary, Elbau, Immanuel, additional, Ng, Tommy, additional, Ayaz, Aliza, additional, Zhu, Shasha, additional, Manfredi, Nicola, additional, Johnson, Megan, additional, Wolk, Danielle, additional, Power, Jonathan D., additional, Gordon, Evan M., additional, Kay, Kendrick, additional, Aloysi, Amy, additional, Moia, Stefano, additional, Gaudes, Cesar Caballero, additional, Victoria, Lindsa W., additional, Solomonov, Nili, additional, Goldwaser, Eric, additional, Zebley, Benjamin, additional, Grosenick, Logan, additional, Downar, Jonathan, additional, Vila-Rodriguez, Fidel, additional, Daskalakis, Zafiris J., additional, Blumberger, Daniel M., additional, Williams, Nolan, additional, Gunning, Faith, additional, and Liston, Conor, additional

Published
2023
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13. Comparing end-tidal CO 2 , respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI

Author

Zvolanek, Kristina M., Moia, Stefano, Dean, Joshua N., Stickland, Rachael C., Caballero-Gaudes, César, Bright, Molly G., Zvolanek, Kristina M., Moia, Stefano, Dean, Joshua N., Stickland, Rachael C., Caballero-Gaudes, César, and Bright, Molly G.

Abstract

Available online 22 March 2023, Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imag- ing biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented method to map CVR involves using a breath-hold task during a BOLD fMRI scan. Recording end-tidal CO 2 (P ET CO 2 ) changes during the breath-hold task is recommended to be used as a refer- ence signal for modeling CVR amplitude in standard units (%BOLD/mmHg) and CVR delay in seconds. However, obtaining reliable P ET CO 2 recordings requires equipment and task compliance that may not be achievable in all settings. To address this challenge, we investigated two alternative reference signals to map CVR amplitude and delay in a lagged general linear model (lagged-GLM) framework: respiration volume per time (RVT) and average gray matter BOLD response (GM-BOLD). In 8 healthy adults with multiple scan sessions, we compare spatial agreement of CVR maps from RVT and GM-BOLD to those generated with P ET CO 2 . We define a threshold to determine whether a P ET CO 2 recording has “sufficient ”quality for CVR mapping and perform these comparisons in 16 datasets with sufficient P ET CO 2 and 6 datasets with insufficient P ET CO 2 . When P ET CO 2 quality is sufficient, both RVT and GM-BOLD produce CVR amplitude maps that are nearly identical to those from P ET CO 2 (after accounting for differences in scale), with the caveat they are not in standard units to facilitate between-group comparisons. CVR delays are comparable to P ET CO 2 with an RVT regressor but may be underestimated with the average GM-BOLD regressor. Importantly, when P ET CO 2 quality is insufficient, RVT and GM-BOLD CVR recover reasonable CVR amplitude and delay maps, provided the participant attempted the breath-hold task. Therefore, our framework offers a solution for achieving high quality CVR maps in both retrospective and prospective stud- ies where suf

Published
2023

15. Brainhack: developing a culture of open, inclusive, community-driven neuroscience

Author

Gau, Remi, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine, Bilgin, Isil, Yang, Yu-Fang, Huntenburg, Julia, Bayer, Johanna, Bethlehem, Richard, Rhoads, Shawn, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta Gorroño, Jon Haitz, Guay, Samuel, ATAY, Melvin, Varoquaux, Gael, Huijser, Dorien, Sandström, Malin, Herholz, Peer, Nastase, Samuel, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula, Mancini, Matteo, Shine, James, O'Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly, Nolan, Christopher, Demeter, Damion, Barrios, Fernando, Merchant, Junaid, McDevitt, Elizabeth, Oostenveld, Robert, Craddock, Cameron, Rokem, Ariel, Doyle, Andrew, Esper, Nathalia, Ghosh, Satrajit, Langs, Georg, Nikolaidis, Aki, Stanley, Olivia, Uruñuela, Eneko, Vohryzek, Jakub, and community, Brainhack

Subjects
neuroscience, inclusivity, training, hackathon, open science, best practices, Brainhack, reproducibility, collaboration, community building
Abstract

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Departing from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for individual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.

Published
2022
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18. BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

Author

Zwiers, Marcel Peter, Moia, Stefano, Oostenveld, Robert, Zwiers, Marcel Peter, Moia, Stefano, and Oostenveld, Robert

Abstract

Published: 13 January 2022, Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.

Published
2022

20. Lag-Optimized Blood Oxygenation Level Dependent Cerebrovascular Reactivity Estimates Derived From Breathing Task Data Have a Stronger Relationship With Baseline Cerebral Blood Flow

Author

Stickland, Rachael C., Zvolanek, Kristina M., Moia, Stefano, Caballero-Gaudes, César, G. Bright, Molly, Stickland, Rachael C., Zvolanek, Kristina M., Moia, Stefano, Caballero-Gaudes, César, and G. Bright, Molly

Abstract

Published: 15 June 2022, Cerebrovascular reactivity (CVR), an important indicator of cerebrovascular health, is commonly studied with the Blood Oxygenation Level Dependent functional MRI (BOLD-fMRI) response to a vasoactive stimulus. Theoretical and empirical evidence suggests that baseline cerebral blood flow (CBF) modulates BOLD signal amplitude and may influence BOLD-CVR estimates. We address how acquisition and modeling choices affect the relationship between baseline cerebral blood flow (bCBF) and BOLD-CVR: whether BOLD-CVR is modeled with the inclusion of a breathing task, and whether BOLD-CVR amplitudes are optimized for hemodynamic lag effects. We assessed between-subject correlations of average GM values and within-subject spatial correlations across cortical regions. Our results suggest that a breathing task addition to a resting-state acquisition, alongside lag-optimization within BOLD-CVR modeling, can improve BOLD-CVR correlations with bCBF, both between- and within-subjects, likely because these CVR estimates are more physiologically accurate. We report positive correlations between bCBF and BOLD-CVR, both between- and within-subjects. The physiological explanation of this positive correlation is unclear; research with larger samples and tightly controlled vasoactive stimuli is needed. Insights into what drives variability in BOLD-CVR measurements and related measurements of cerebrovascular function are particularly relevant when interpreting results in populations with altered vascular and/or metabolic baselines or impaired cerebrovascular reserve.

Published
2022

23. TE-dependent analysis of multi-echo fMRI with tedana

Author

DuPre, Elizabeth, primary, Salo, Taylor, additional, Ahmed, Zaki, additional, Bandettini, Peter, additional, Bottenhorn, Katherine, additional, Caballero-Gaudes, César, additional, Dowdle, Logan, additional, Gonzalez-Castillo, Javier, additional, Heunis, Stephan, additional, Kundu, Prantik, additional, Laird, Angela, additional, Markello, Ross, additional, Markiewicz, Christopher, additional, Moia, Stefano, additional, Staden, Isla, additional, Teves, Joshua, additional, Uruñuela, Eneko, additional, Vaziri-Pashkam, Maryam, additional, Whitaker, Kirstie, additional, and Handwerker, Daniel, additional

Published
2021
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25. Unravelling the Neural Basis of Spatial Delusions After Stroke

Author

Thiebaut De Schotten, Michel, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine, Bilgin, Isil, Yang, Yu-Fang, Huntenburg, Julia, Bayer, Johanna M.M., Bethlehem, Richard A.I., Rhoads, Shawn, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael, Huijser, Dorien, Sandström, Malin, Herholz, Peer, Nastase, Samuel, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula, Mancini, Matteo, Shine, James, O’Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly, Nolan, Christopher, Demeter, Damion, Barrios, Fernando, Merchant, Junaid, McDevitt, Elizabeth, Oostenveld, Robert, Craddock, R. Cameron, Rokem, Ariel, Doyle, Andrew, Ghosh, Satrajit, Nikolaidis, Aki, Stanley, Olivia, Uruñuela, Eneko, Anousheh, Nasim, Arnatkeviciute, Aurina, Auzias, Guillaume, Bachar, Dipankar, Bannier, Elise, Basanisi, Ruggero, Basavaraj, Arshitha, Bedini, Marco, Bellec, Pierre, Benn, R. Austin, Berluti, Kathryn, Bollmann, Steffen, Bollmann, Saskia, Bradley, Claire, Brown, Jesse, Buchweitz, Augusto, Callahan, Patrick, Chan, Micaela, Chandio, Bramsh, Cheng, Theresa, Chopra, Sidhant, Chung, Ai Wern, Close, Thomas, Combrisson, Etienne, Cona, Giorgia, Constable, R. Todd, Cury, Claire, Dadi, Kamalaker, Damasceno, Pablo, Das, Samir, De Vico Fallani, Fabrizio, DeStasio, Krista, Dickie, Erin, Dorfschmidt, Lena, Duff, Eugene, Dupre, Elizabeth, Dziura, Sarah, Esper, Nathalia, Esteban, Oscar, Fadnavis, Shreyas, Flandin, Guillaume, Flannery, Jessica, Flournoy, John, Franco, Alexandre, Ganesan, Saampras, Gao, Siyuan, García Alanis, José, Garyfallidis, Eleftherios, Glatard, Tristan, Glerean, Enrico, Gonzalez-Castillo, Javier, Gould van Praag, Cassandra, Greene, Abigail, Gupta, Geetika, Hahn, Catherine Alice, Halchenko, Yaroslav, Handwerker, Daniel, Hartmann, Thomas, Hayot-Sasson, Valérie, Heunis, Stephan, Hoffstaedter, Felix, Hohmann, Daniela, Horien, Corey, Ioanas, Horea-Ioan, Iordan, Alexandru, Jiang, Chao, Joseph, Michael, Kai, Jason, Karakuzu, Agah, KENNEDY, David, Keshavan, Anisha, Khan, Ali, Kiar, Gregory, Klink, P. Christiaan, Koppelmans, Vincent, Koudoro, Serge, Laird, Angela, Langs, Georg, Laws, Marissa, Licandro, Roxane, Liew, Sook-Lei, Lipic, Tomislav, Litinas, Krisanne, Lurie, Daniel, Lussier, Désirée, Madan, Christopher, Mais, Lea-Theresa, Mansour L, Sina, Manzano-Patron, J.P., Maoutsa, Dimitra, Marcon, Matheus, Margulies, Daniel, Marinato, Giorgio, Marinazzo, Daniele, Markiewicz, Christopher, Meneguzzi, Felipe, Meunier, David, Milham, Michael, Mills, Kathryn, Momi, Davide, Moreau, Clara, Motala, Aysha, Moxon-Emre, Iska, Nichols, Thomas, Nielson, Dylan, Nilsonne, Gustav, Novello, Lisa, O’Brien, Caroline, Olafson, Emily, Oliver, Lindsay, Onofrey, John, Orchard, Edwina, Oudyk, Kendra, Park, Patrick, Parsapoor, Mahboobeh, Pasquini, Lorenzo, Peltier, Scott, Pernet, Cyril, Pienaar, Rudolph, Pinheiro-Chagas, Pedro, Poline, Jean-Baptiste, Qiu, Anqi, Quendera, Tiago, Rice, Laura, Rocha-Hidalgo, Joscelin, Rutherford, Saige, Scharinger, Mathias, Scheinost, Dustin, Shariq, Deena, Shaw, Thomas, Siless, Viviana, Simmonite, Molly, Sirmpilatze, Nikoloz, Spence, Hayli, Sprenger, Julia, Stajduhar, Andrija, Szinte, Martin, Takerkart, Sylvain, Tam, Angela, Tejavibulya, Link, Thome, Ina, Tomaz da Silva, Laura, Traut, Nicolas, Uddin, Lucina, Vallesi, Antonino, VanMeter, John, Vijayakumar, Nandita, di Oleggio Castello, Matteo Visconti, Vohryzek, Jakub, Vukojević, Jakša, Whitaker, Kirstie Jane, Whitmore, Lucy, Wideman, Steve, Witt, Suzanne, Xie, Hua, Xu, Ting, Yan, Chao-Gan, Yeh, Fang-Cheng, Yeo, B.T. Thomas, Zuo, Xi-Nian, Alves, Pedro, Fonseca, Ana, Silva, Daniela, Andrade, Matilde, Pinho‐e‐Melo, Teresa, Martins, Isabel, Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), and Repositório da Universidade de Lisboa

Subjects
0301 basic medicine, Male, medicine.medical_specialty, Support Vector Machine, Reduplicative paramnesia, Audiology, Logistic regression, Delusions, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Neuroimaging, Image Interpretation, Computer-Assisted, medicine, Humans, Functional disconnection, Prospective Studies, Stroke, ComputingMilieux_MISCELLANEOUS, Aged, Aged, 80 and over, Brain Mapping, business.industry, Anosognosia, Confounding, 16. Peace & justice, medicine.disease, Magnetic Resonance Imaging, 030104 developmental biology, Neurology, Case-Control Studies, Female, Neurology (clinical), Disconnection, business, Tomography, X-Ray Computed, 030217 neurology & neurosurgery
Abstract

© 2021 American Neurological Association, Objective: Knowing explicitly where we are is an interpretation of our spatial representations. Reduplicative paramnesia is a disrupting syndrome in which patients present a firm belief of spatial mislocation. Here, we studied the largest sample of patients with delusional misidentifications of space (i.e. reduplicative paramnesia) after stroke to shed light on their neurobiology. Methods: In a prospective, cumulative, case-control study, we screened 400 patients with acute right-hemispheric stroke. We included 64 cases and 233 controls. First, lesions were delimited and normalized. Then, we computed structural and functional disconnection maps using methods of lesion-track and network-mapping. The maps were compared, controlling for confounders. Second, we built a multivariate logistic model including clinical, behavioural and neuroimaging data. Finally, we performed a nested cross-validation of the model with a support-vector machine analysis. Results: The most frequent misidentification subtype was confabulatory mislocation (56%), followed by place reduplication (19%) and chimeric assimilation (13%). Our results indicate that structural disconnection is the strongest predictor of the syndrome and included two distinct streams, connecting right fronto-thalamic and right occipito-temporal structures. In the multivariate model, the independent predictors of reduplicative paramnesia were the structural disconnection map, lesion sparing of right dorsal fronto-parietal regions, age and anosognosia. Good discrimination accuracy was demonstrated (area under the curve = 0.80[0.75-0.85]). Interpretation: Our results localize the anatomical circuits that may have a role in the abnormal spatial-emotional binding and in the defective updating of spatial representations underlying reduplicative paramnesia. This novel data may contribute to better understand the pathophysiology of delusional syndromes after stroke., The authors acknowledge our colleagues from the Department of Neurology and from the Language Research Laboratory for their contribution to patient screening. This work received funding from “PRÉMIO JOÃO LOBO ANTUNES” – SCML (grant to P.N.A.), from “Bolsa de Investigação em Doenças Vasculares Cerebrais 2017”–SPAVC (grant to P.N.A.) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 818521 to M.T.S.).“Data were provided in part bythe Human Connectome Project, WU-Minn Consortium(Principal Investigators: David Van Essen and KamilUgurbil; 1U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Published
2021

27. A LOW RANK AND SPARSE PARADIGM FREE MAPPING ALGORITHM FOR DECONVOLUTION OF FMRI DATA

Author

Uruñuela, Eneko, Moia, Stefano, Caballero-Gaudes, César, Uruñuela, Eneko, Moia, Stefano, and Caballero-Gaudes, César

Abstract

Date Added to IEEE Xplore: 25 May 2021, Current deconvolution algorithms for functional magnetic resonance imaging (fMRI) data are hindered by widespread signal changes arising from motion or physiological processes (e.g. deep breaths) that can be interpreted incorrectly as neuronal-related hemodynamic events. This work proposes a novel deconvolution approach that simultaneously estimates global signal fluctuations and neuronalrelated activity with no prior information about the timings of the blood oxygenation level-dependent (BOLD) events by means of a low rank plus sparse decomposition algorithm. The performance of the proposed method is evaluated on simulated and experimental fMRI data, and compared with state-of-the-art sparsity-based deconvolution approaches and with a conventional analysis that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel low-rank and sparse paradigm free mapping algorithm can estimate global signal fluctuations related to motion in our task, while estimating the neuronal-related activity with high fidelity.

Published
2021

29. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Author

UCL - SSH/IPSY - Psychological Sciences Research Institute, Gau, Remi, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine L, Bilgin, Isil P, Yang, Yu-Fang, Huntenburg, Julia M, Bayer, Johanna M M, Bethlehem, Richard A I, Rhoads, Shawn A, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael P, Huijser, Dorien C, Sandström, Malin S, Herholz, Peer, Nastase, Samuel A, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula P, Mancini, Matteo, Shine, James M, O'Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal S, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly G, Nolan, Christopher R, Demeter, Damion V, Barrios, Fernando A, Merchant, Junaid S, McDevitt, Elizabeth A, Oostenveld, Robert, Craddock, R Cameron, Rokem, Ariel, Doyle, Andrew, Ghosh, Satrajit S, Nikolaidis, Aki, Stanley, Olivia W, Uruñuela, Eneko, Brainhack Community, UCL - SSH/IPSY - Psychological Sciences Research Institute, Gau, Remi, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine L, Bilgin, Isil P, Yang, Yu-Fang, Huntenburg, Julia M, Bayer, Johanna M M, Bethlehem, Richard A I, Rhoads, Shawn A, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael P, Huijser, Dorien C, Sandström, Malin S, Herholz, Peer, Nastase, Samuel A, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula P, Mancini, Matteo, Shine, James M, O'Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal S, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly G, Nolan, Christopher R, Demeter, Damion V, Barrios, Fernando A, Merchant, Junaid S, McDevitt, Elizabeth A, Oostenveld, Robert, Craddock, R Cameron, Rokem, Ariel, Doyle, Andrew, Ghosh, Satrajit S, Nikolaidis, Aki, Stanley, Olivia W, Uruñuela, Eneko, and Brainhack Community

Abstract

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Published
2021

34. A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping

Author

Caballero Gaudes, César, Moia, Stefano, Panwar, Puja, Bandettini, Peter A., Gonzalez-Castillo, Javier, Caballero Gaudes, César, Moia, Stefano, Panwar, Puja, Bandettini, Peter A., and Gonzalez-Castillo, Javier

Abstract

Available online 13 August 2019., This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of ⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.

Published
2019

40. Proper Italian.

Author

Moia, Stefano

Subjects
*LETTERS to the editor, ITALIAN politics & government, 1994-
Abstract

A letter to the editor is presented in response to the article on Italian politics that appeared in the May 6, 2006 issue.

Published
2006

41. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Author

Reddy NA, Zvolanek KM, Moia S, Caballero-Gaudes C, and Bright MG

Abstract

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage., Competing Interests: DECLARATION OF COMPETING INTEREST The authors declare no competing financial interests.

Published
2024
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42. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Author

Reddy NA, Zvolanek KM, Moia S, Caballero-Gaudes C, and Bright MG

Abstract

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage., Competing Interests: Declaration of competing interest The authors declare no competing financial interests.

Published
2023
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43. Expansion of a frontostriatal salience network in individuals with depression.

Author

Lynch CJ, Elbau I, Ng T, Ayaz A, Zhu S, Manfredi N, Johnson M, Wolk D, Power JD, Gordon EM, Kay K, Aloysi A, Moia S, Caballero-Gaudes C, Victoria LW, Solomonov N, Goldwaser E, Zebley B, Grosenick L, Downar J, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Williams N, Gunning FM, and Liston C

Abstract

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Published
2023
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44. Voxelwise optimization of hemodynamic lags to improve regional CVR estimates in breath-hold fMRI.

Author

Moia S, Stickland RC, Ayyagari A, Termenon M, Caballero-Gaudes C, and Bright MG

Subjects
Brain diagnostic imaging, Hemodynamics, Oxygen, Cerebrovascular Circulation, Magnetic Resonance Imaging
Abstract

Cerebrovascular Reactivity (CVR), the responsiveness of blood vessels to a vasodilatory stimulus, is an important indicator of cerebrovascular health. Assessing CVR with fMRI, we can measure the change in the Blood Oxygen Level Dependent (BOLD) response induced by a change in CO2 pressure (%BOLD/mmHg). However, there exists a temporal offset between the recorded CO2 pressure and the local BOLD response, due to both measurement and physiological delays. If this offset is not corrected for, voxel-wise CVR values will not be accurate. In this paper, we propose a framework for mapping hemodynamic lag in breath-hold fMRI data. As breath-hold tasks drive task-correlated head motion artifacts in BOLD fMRI data, our framework for lag estimation fits a model that includes polynomial terms and head motion parameters, as well as a shifted variant of the CO2 regressor (±9 s in 0.3 s increments), and the hemodynamic lag at each voxel is the shift producing the maximum total model R2 within physiological constraints. This approach is evaluated in 8 subjects with multi-echo fMRI data, resulting in robust maps of hemodynamic delay that show consistent regional variation across subjects, and improved contrast-to-noise compared to methods where motion regression is ignored or performed earlier in preprocessing.Clinical Relevance- We map hemodynamic lag using breathhold fMRI, providing insight into vascular transit times and improving the regional accuracy of cerebrovascular reactivity measurements.

Published
2020
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