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1. One hundred years of EEG for brain and behaviour research

Author

Mushtaq, Faisal, Welke, Dominik, Gallagher, Anne, Pavlov, Yuri G., Kouara, Layla, Bosch-Bayard, Jorge, van den Bosch, Jasper J. F., Arvaneh, Mahnaz, Bland, Amy R., Chaumon, Maximilien, Borck, Cornelius, He, Xun, Luck, Steven J., Machizawa, Maro G., Pernet, Cyril, Puce, Aina, Segalowitz, Sidney J., Rogers, Christine, Awais, Muhammad, Babiloni, Claudio, Bailey, Neil W., Baillet, Sylvain, Bendall, Robert C. A., Brady, Daniel, Bringas-Vega, Maria L., Busch, Niko A., Calzada-Reyes, Ana, Chatard, Armand, Clayson, Peter E., Cohen, Michael X., Cole, Jonathan, Constant, Martin, Corneyllie, Alexandra, Coyle, Damien, Cruse, Damian, Delis, Ioannis, Delorme, Arnaud, Fair, Damien, Falk, Tiago H., Gamer, Matthias, Ganis, Giorgio, Gloy, Kilian, Gregory, Samantha, Hassall, Cameron D., Hiley, Katherine E., Ivry, Richard B., Jerbi, Karim, Jenkins, Michael, Kaiser, Jakob, Keil, Andreas, Knight, Robert T., Kochen, Silvia, Kotchoubey, Boris, Krigolson, Olave E., Langer, Nicolas, Liesefeld, Heinrich R., Lippé, Sarah, London, Raquel E., MacNamara, Annmarie, Makeig, Scott, Marinovic, Welber, Martínez-Montes, Eduardo, Marzuki, Aleya A., Mathew, Ryan K., Michel, Christoph, Millán, José d. R., Mon-Williams, Mark, Morales-Chacón, Lilia, Naar, Richard, Nilsonne, Gustav, Niso, Guiomar, Nyhus, Erika, Oostenveld, Robert, Paul, Katharina, Paulus, Walter, Pfabigan, Daniela M., Pourtois, Gilles, Rampp, Stefan, Rausch, Manuel, Robbins, Kay, Rossini, Paolo M., Ruzzoli, Manuela, Schmidt, Barbara, Senderecka, Magdalena, Srinivasan, Narayanan, Stegmann, Yannik, Thompson, Paul M., Valdes-Sosa, Mitchell, van der Molen, Melle J. W., Veniero, Domenica, Verona, Edelyn, Voytek, Bradley, Yao, Dezhong, Evans, Alan C., and Valdes-Sosa, Pedro

Published
2024
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2. Spectral homogeneity cross frequencies can be a quality metric for the large-scale resting EEG preprocessing

Author

Hu, Shiang, Ruan, Jie, Langer, Nicolas, Bosch-Bayard, Jorge, Lv, Zhao, Yao, Dezhong, and Valdes-Sosa, Pedro Antonio

Subjects
Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Signal Processing, Quantitative Biology - Neurons and Cognition
Abstract

The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, although the waveforms-based metrics are partially useful, they may be unreliable by neglecting the spectral profiles. Here, we detected the phenomenon of parallel log spectra (PaLOS) that the scalp EEG power in the log scale were parallel to each other from 10% of 2549 HBN EEG. This phenomenon was reproduced in 8% of 412 PMDT EEG from 4 databases. We designed the PaLOS index (PaLOSi) to indicate this phenomenon by decomposing the cross-spectra at different frequencies into the common principal component spaces. We found that the PaLOS biophysically implied a prominently dominant dipole in the source space which was implausible for the resting EEG. And it may be practically resulted from excessive preprocessing. Compared with the 1966 normative EEG cross-spectra, the HBN and the PMDT EEG with PaLOS presented generally much higher electrode pairwise coherences and higher similarity of coherence-based network patterns, which went against the known frequency dependent characteristic of coherence networks. We suggest the PaLOSi should lay in the range of 0.4-0.7 for large resting EEG quality assurance.

Published
2023

3. Hierarchical Event Descriptor library schema for EEG data annotation

Author

Hermes, Dora, Attia, Tal Pal, Beniczky, Sándor, Bosch-Bayard, Jorge, Delorme, Arnaud, Lundstrom, Brian Nils, Rogers, Christine, Rampp, Stefan, Shirazi, Seyed Yahya, Truong, Dung, Valdes-Sosa, Pedro, Worrell, Greg, Makeig, Scott, and Robbins, Kay

Subjects
Quantitative Biology - Neurons and Cognition, E.1
Abstract

Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Sharing data enriched with standard terms can facilitate data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study developed a HED library schema for SCORE, making the terms machine-readable. We demonstrate that the HED-SCORE library schema can be used to annotate events in EEG data stored in the Brain Imaging Data Structure (BIDS). Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and compute on electrophysiological data obtained from the human brain., Comment: 23 pages, 5 figures, 1 table

Published
2023

4. Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder

Author

Adamson, Chris, Adler, Sophie, Alexander-Bloch, Aaron F., Anagnostou, Evdokia, Anderson, Kevin M., Areces-Gonzalez, Ariosky, Astle, Duncan E., Auyeung, Bonnie, Ayub, Muhammad, Bae, Jong Bin, Ball, Gareth, Baron-Cohen, Simon, Beare, Richard, Bedford, Saashi A., Benegal, Vivek, Bethlehem, Richard A.I., Beyer, Frauke, Blangero, John, Cábez, Manuel Blesa, Boardman, James P., Borzage, Matthew, Bosch-Bayard, Jorge F., Bourke, Niall, Bullmore, Edward T., Calhoun, Vince D., Chakravarty, Mallar M., Chen, Christina, Chertavian, Casey, Chetelat, Gaël, Chong, Yap S., Corvin, Aiden, Costantino, Manuela, Courchesne, Eric, Crivello, Fabrice, Cropley, Vanessa L., Crosbie, Jennifer, Crossley, Nicolas, Delarue, Marion, Delorme, Richard, Desrivieres, Sylvane, Devenyi, Gabriel, Di Biase, Maria A., Dolan, Ray, Donald, Kirsten A., Donohoe, Gary, Dorfschmidt, Lena, Dunlop, Katharine, Edwards, Anthony D., Elison, Jed T., Ellis, Cameron T., Elman, Jeremy A., Eyler, Lisa, Fair, Damien A., Fletcher, Paul C., Fonagy, Peter, Franz, Carol E., Galan-Garcia, Lidice, Gholipour, Ali, Giedd, Jay, Gilmore, John H., Glahn, David C., Goodyer, Ian M., Grant, P.E., Groenewold, Nynke A., Gudapati, Shreya, Gunning, Faith M., Gur, Raquel E., Gur, Ruben C., Hammill, Christopher F., Hansson, Oskar, Hedden, Trey, Heinz, Andreas, Henson, Richard N., Heuer, Katja, Hoare, Jacqueline, Holla, Bharath, Holmes, Avram J., Huang, Hao, Ipser, Jonathan, Jack, Clifford R., Jr., Jackowski, Andrea P., Jia, Tianye, Jones, David T., Jones, Peter B., Kahn, Rene S., Karlsson, Hasse, Karlsson, Linnea, Kawashima, Ryuta, Kelley, Elizabeth A., Kern, Silke, Kim, Ki-Woong, Kitzbichler, Manfred G., Kremen, William S., Lalonde, François, Landeau, Brigitte, Lerch, Jason, Lewis, John D., Li, Jiao, Liao, Wei, Liston, Conor, Lombardo, Michael V., Lv, Jinglei, Mallard, Travis T., Marcelis, Machteld, Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, Michael J., Mechelli, Andrea, Misic, Bratislav, Morgan, Sarah E., Mothersill, David, Ortinau, Cynthia, Ossenkoppele, Rik, Ouyang, Minhui, Palaniyappan, Lena, Paly, Leo, Pan, Pedro M., Pantelis, Christos, Park, Min Tae M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, Deirel, Binette, Alexa Pichet, Pierce, Karen, Qian, Xing, Qiu, Anqi, Raznahan, Armin, Rittman, Timothy, Rodrigue, Amanda, Rollins, Caitlin K., Romero-Garcia, Rafael, Ronan, Lisa, Rosenberg, Monica D., Rowitch, David H., Salum, Giovanni A., Satterthwaite, Theodore D., Schaare, H. Lina, Schabdach, Jenna, Schachar, Russell J., Schöll, Michael, Schultz, Aaron P., Seidlitz, Jakob, Sharp, David, Shinohara, Russell T., Skoog, Ingmar, Smyser, Christopher D., Sperling, Reisa A., Stein, Dan J., Stolicyn, Aleks, Suckling, John, Sullivan, Gemma, Thyreau, Benjamin, Toro, Roberto, Traut, Nicolas, Tsvetanov, Kamen A., Turk-Browne, Nicholas B., Tuulari, Jetro J., Tzourio, Christophe, Vachon-Presseau, Étienne, Valdes-Sosa, Mitchell J., Valdes-Sosa, Pedro A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, Simon N., Vasung, Lana, Vértes, Petra E., Victoria, Lindsay W., Villeneuve, Sylvia, Villringer, Arno, Vogel, Jacob W., Wagstyl, Konrad, Wang, Yin-Shan S., Warfield, Simon K., Warrier, Varun, Westman, Eric, Westwater, Margaret L., Whalley, Heather C., White, Simon R., Witte, A. Veronica, Yang, Ning, Yeo, B.T. Thomas, Yun, Hyuk Jin, Zalesky, Andrew, Zar, Heather J., Zettergren, Anna, Zhou, Juan H., Ziauddeen, Hisham, Zimmerman, Dabriel, Zugman, Andre, Zuo, Xi-Nian N., Ho, Natalie C.W., Nogovitsyn, Nikita, Metzak, Paul, Ballester, Pedro L., Hassel, Stefanie, Rotzinger, Susan, Poppenk, Jordan, Lam, Raymond W., Taylor, Valerie H., Milev, Roumen, Frey, Benicio N., Harkness, Kate L., Addington, Jean, and Kennedy, Sidney H.

Published
2024
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6. Identification of oscillatory brain networks with Hidden Gaussian Graphical Spectral models of EEG/MEG

Author

Paz-Linares, Deirel, Gonzalez-Moreira, Eduardo, Areces-Gonzalez, Ariosky, Wang, Ying, Li, Min, Martinez-Montes, Eduardo, Bosch-Bayard, Jorge, Bringas-Vega, Maria L., Valdes-Sosa, Mitchel J., and Valdes-Sosa, Pedro A.

Subjects
Statistics - Methodology, Quantitative Biology - Neurons and Cognition
Abstract

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflect functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our inverse-solution with 1/3 more congruence according to Riemannian distances than state-of-the-art methods.

Published
2018
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7. Caulking the Leakage Effect in MEEG Source Connectivity Analysis

Author

Gonzalez-Moreira, Eduardo, Paz-Linares, Deirel, Areces-Gonzalez, Ariosky, Wang, Rigel, Bosch-Bayard, Jorge, Bringas-Vega, Maria Luisa, and Valdes-Sosa, Pedro A.

Subjects
Quantitative Biology - Neurons and Cognition, Statistics - Methodology
Abstract

Simplistic estimation of neural connectivity in MEEG sensor space is impossible due to volume conduction. The only viable alternative is to carry out connectivity estimation in source space. Among the neuroscience community this is claimed to be impossible or misleading due to Leakage: linear mixing of the reconstructed sources. To address this problematic we propose a novel solution method that caulks the Leakage in MEEG source activity and connectivity estimates: BC-VARETA. It is based on a joint estimation of source activity and connectivity in the frequency domain representation of MEEG time series. To achieve this, we go beyond current methods that assume a fixed gaussian graphical model for source connectivity. In contrast we estimate this graphical model in a Bayesian framework by placing priors on it, which allows for highly optimized computations of the connectivity, via a new procedure based on the local quadratic approximation under quite general prior models. A further contribution of this paper is the rigorous definition of leakage via the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic distances over the cortical manifold. Both measures are extended for the first time to quantify Connectivity Leakage by defining them on the cartesian product of cortical manifolds. Using these measures, we show that BC-VARETA outperforms most state of the art inverse solvers by several orders of magnitude.

Published
2018

8. Harmonized-Multinational qEEG norms (HarMNqEEG)

Author

Li, Min, Wang, Ying, Lopez-Naranjo, Carlos, Hu, Shiang, Reyes, Ronaldo César García, Paz-Linares, Deirel, Areces-Gonzalez, Ariosky, Hamid, Aini Ismafairus Abd, Evans, Alan C., Savostyanov, Alexander N., Calzada-Reyes, Ana, Villringer, Arno, Tobon-Quintero, Carlos A., Garcia-Agustin, Daysi, Yao, Dezhong, Dong, Li, Aubert-Vazquez, Eduardo, Reza, Faruque, Razzaq, Fuleah Abdul, Omar, Hazim, Abdullah, Jafri Malin, Galler, Janina R., Ochoa-Gomez, John F., Prichep, Leslie S., Galan-Garcia, Lidice, Morales-Chacon, Lilia, Valdes-Sosa, Mitchell J., Tröndle, Marius, Zulkifly, Mohd Faizal Mohd, Abdul Rahman, Muhammad Riddha Bin, Milakhina, Natalya S., Langer, Nicolas, Rudych, Pavel, Koenig, Thomas, Virues-Alba, Trinidad A., Lei, Xu, Bringas-Vega, Maria L., Bosch-Bayard, Jorge F., and Valdes-Sosa, Pedro Antonio

Published
2022
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9. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity

Author

Bosch-Bayard, Jorge, Razzaq, Fuleah Abdul, Lopez-Naranjo, Carlos, Wang, Ying, Li, Min, Galan-Garcia, Lidice, Calzada-Reyes, Ana, Virues-Alba, Trinidad, Rabinowitz, Arielle G., Suarez-Murias, Carlos, Guo, Yanbo, Sanchez-Castillo, Manuel, Roger, Kassandra, Gallagher, Anne, Prichep, Leslie, Anderson, Simon G., Michel, Christoph M., Evans, Alan C., Bringas-Vega, Maria L., Galler, Janina R., and Valdes-Sosa, Pedro A.

Published
2022
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11. Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG 'leakage correction'

Author

Pascual-Marqui, Roberto D., Biscay, Rolando J., Bosch-Bayard, Jorge, Faber, Pascal, Kinoshita, Toshihiko, Kochi, Kieko, Milz, Patricia, Nishida, Keiichiro, and Yoshimura, Masafumi

Subjects
Statistics - Methodology, Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract

The problem of interest here is the study of brain functional and effective connectivity based on non-invasive EEG-MEG inverse solution time series. These signals generally have low spatial resolution, such that an estimated signal at any one site is an instantaneous linear mixture of the true, actual, unobserved signals across all cortical sites. False connectivity can result from analysis of these low-resolution signals. Recent efforts toward "unmixing" have been developed, under the name of "leakage correction". One recent noteworthy approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which forces the inverse solution signals to have zero cross-correlation at lag zero. One goal is to show that Colclough's method produces false human connectomes under very broad conditions. The second major goal is to develop a new solution, that appropriately "unmixes" the inverse solution signals, based on innovations orthogonalization. The new method first fits a multivariate autoregression to the inverse solution signals, giving the mixed innovations. Second, the mixed innovations are orthogonalized. Third, the mixed and orthogonalized innovations allow the estimation of the "unmixing" matrix, which is then finally used to "unmix" the inverse solution signals. It is shown that under very broad conditions, the new method produces proper human connectomes, even when the signals are not generated by an autoregressive model., Comment: preprint, technical report, under license "Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)", https://creativecommons.org/licenses/by-nc-nd/4.0/

Published
2017
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12. EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition

Author

Lopez Naranjo, Carlos, primary, Razzaq, Fuleah Abdul, additional, Li, Min, additional, Wang, Ying, additional, Bosch‐Bayard, Jorge F., additional, Lindquist, Martin A., additional, Gonzalez Mitjans, Anisleidy, additional, Garcia, Ronaldo, additional, Rabinowitz, Arielle G., additional, Anderson, Simon G., additional, Chiarenza, Giuseppe A., additional, Calzada‐Reyes, Ana, additional, Virues‐Alba, Trinidad, additional, Galler, Janina R., additional, Minati, Ludovico, additional, Bringas Vega, Maria L., additional, and Valdes‐Sosa, Pedro A., additional

Published
2024
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13. Atypical brain aging and its association with working memory performance in major depressive disorder

Author

Ho, Natalie C.W., primary, Bethlehem, Richard AI., additional, Seidlitz, Jakob, additional, Nogovitsyn, Nikita, additional, Metzak, Paul, additional, Ballester, Pedro L., additional, Hassel, Stefanie, additional, Rotzinger, Susan, additional, Poppenk, Jordan, additional, Lam, Raymond W., additional, Taylor, Valerie H., additional, Milev, Roumen, additional, Adamson, Chris, additional, Adler, Sophie, additional, Alexander-Bloch, Aaron F., additional, Anagnostou, Evdokia, additional, Anderson, Kevin M., additional, Areces-Gonzalez, Ariosky, additional, Astle, Duncan E., additional, Auyeung, Bonnie, additional, Ayub, Muhammad, additional, Bae, Jong Bin, additional, Ball, Gareth, additional, Baron-Cohen, Simon, additional, Beare, Richard, additional, Bedford, Saashi A., additional, Benegal, Vivek, additional, Bethlehem, Richard A.I., additional, Beyer, Frauke, additional, Blangero, John, additional, Blesa Cábez, Manuel, additional, Boardman, James P., additional, Borzage, Matthew, additional, Bosch-Bayard, Jorge F., additional, Bourke, Niall, additional, Bullmore, Edward T., additional, Calhoun, Vince D., additional, Chakravarty, Mallar M., additional, Chen, Christina, additional, Chertavian, Casey, additional, Chetelat, Gaël, additional, Chong, Yap S., additional, Corvin, Aiden, additional, Costantino, Manuela, additional, Courchesne, Eric, additional, Crivello, Fabrice, additional, Cropley, Vanessa L., additional, Crosbie, Jennifer, additional, Crossley, Nicolas, additional, Delarue, Marion, additional, Delorme, Richard, additional, Desrivieres, Sylvane, additional, Devenyi, Gabriel, additional, Di Biase, Maria A., additional, Dolan, Ray, additional, Donald, Kirsten A., additional, Donohoe, Gary, additional, Dorfschmidt, Lena, additional, Dunlop, Katharine, additional, Edwards, Anthony D., additional, Elison, Jed T., additional, Ellis, Cameron T., additional, Elman, Jeremy A., additional, Eyler, Lisa, additional, Fair, Damien A., additional, Fletcher, Paul C., additional, Fonagy, Peter, additional, Franz, Carol E., additional, Galan-Garcia, Lidice, additional, Gholipour, Ali, additional, Giedd, Jay, additional, Gilmore, John H., additional, Glahn, David C., additional, Goodyer, Ian M., additional, Grant, P.E., additional, Groenewold, Nynke A., additional, Gudapati, Shreya, additional, Gunning, Faith M., additional, Gur, Raquel E., additional, Gur, Ruben C., additional, Hammill, Christopher F., additional, Hansson, Oskar, additional, Hedden, Trey, additional, Heinz, Andreas, additional, Henson, Richard N., additional, Heuer, Katja, additional, Hoare, Jacqueline, additional, Holla, Bharath, additional, Holmes, Avram J., additional, Huang, Hao, additional, Ipser, Jonathan, additional, Jack, Clifford R., additional, Jackowski, Andrea P., additional, Jia, Tianye, additional, Jones, David T., additional, Jones, Peter B., additional, Kahn, Rene S., additional, Karlsson, Hasse, additional, Karlsson, Linnea, additional, Kawashima, Ryuta, additional, Kelley, Elizabeth A., additional, Kern, Silke, additional, Kim, Ki-Woong, additional, Kitzbichler, Manfred G., additional, Kremen, William S., additional, Lalonde, François, additional, Landeau, Brigitte, additional, Lerch, Jason, additional, Lewis, John D., additional, Li, Jiao, additional, Liao, Wei, additional, Liston, Conor, additional, Lombardo, Michael V., additional, Lv, Jinglei, additional, Mallard, Travis T., additional, Marcelis, Machteld, additional, Mathias, Samuel R., additional, Mazoyer, Bernard, additional, McGuire, Philip, additional, Meaney, Michael J., additional, Mechelli, Andrea, additional, Misic, Bratislav, additional, Morgan, Sarah E., additional, Mothersill, David, additional, Ortinau, Cynthia, additional, Ossenkoppele, Rik, additional, Ouyang, Minhui, additional, Palaniyappan, Lena, additional, Paly, Leo, additional, Pan, Pedro M., additional, Pantelis, Christos, additional, Park, Min Tae M., additional, Paus, Tomas, additional, Pausova, Zdenka, additional, Paz-Linares, Deirel, additional, Pichet Binette, Alexa, additional, Pierce, Karen, additional, Qian, Xing, additional, Qiu, Anqi, additional, Raznahan, Armin, additional, Rittman, Timothy, additional, Rodrigue, Amanda, additional, Rollins, Caitlin K., additional, Romero-Garcia, Rafael, additional, Ronan, Lisa, additional, Rosenberg, Monica D., additional, Rowitch, David H., additional, Salum, Giovanni A., additional, Satterthwaite, Theodore D., additional, Schaare, H. Lina, additional, Schabdach, Jenna, additional, Schachar, Russell J., additional, Schöll, Michael, additional, Schultz, Aaron P., additional, Sharp, David, additional, Shinohara, Russell T., additional, Skoog, Ingmar, additional, Smyser, Christopher D., additional, Sperling, Reisa A., additional, Stein, Dan J., additional, Stolicyn, Aleks, additional, Suckling, John, additional, Sullivan, Gemma, additional, Thyreau, Benjamin, additional, Toro, Roberto, additional, Traut, Nicolas, additional, Tsvetanov, Kamen A., additional, Turk-Browne, Nicholas B., additional, Tuulari, Jetro J., additional, Tzourio, Christophe, additional, Vachon-Presseau, Étienne, additional, Valdes-Sosa, Mitchell J., additional, Valdes-Sosa, Pedro A., additional, Valk, Sofie L., additional, van Amelsvoort, Therese, additional, Vandekar, Simon N., additional, Vasung, Lana, additional, Vértes, Petra E., additional, Victoria, Lindsay W., additional, Villeneuve, Sylvia, additional, Villringer, Arno, additional, Vogel, Jacob W., additional, Wagstyl, Konrad, additional, Wang, Yin-Shan S., additional, Warfield, Simon K., additional, Warrier, Varun, additional, Westman, Eric, additional, Westwater, Margaret L., additional, Whalley, Heather C., additional, White, Simon R., additional, Witte, A. Veronica, additional, Yang, Ning, additional, Yeo, B.T. Thomas, additional, Yun, Hyuk Jin, additional, Zalesky, Andrew, additional, Zar, Heather J., additional, Zettergren, Anna, additional, Zhou, Juan H., additional, Ziauddeen, Hisham, additional, Zimmerman, Dabriel, additional, Zugman, Andre, additional, Zuo, Xi-Nian N., additional, Frey, Benicio N., additional, Harkness, Kate L., additional, Addington, Jean, additional, and Kennedy, Sidney H., additional

Published
2024
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16. Cortical current source connectivity by means of partial coherence fields

Author

Pascual-Marqui, Roberto D., Biscay, Rolando J., Valdes-Sosa, Pedro A., Bosch-Bayard, Jorge, and Riera-Diaz, Jorge J.

Subjects
Statistics - Applications, Physics - Biological Physics, Quantitative Biology - Neurons and Cognition, Statistics - Methodology
Abstract

An important field of research in functional neuroimaging is the discovery of integrated, distributed brain systems and networks, whose different regions need to work in unison for normal functioning. The EEG is a non-invasive technique that can provide information for massive connectivity analyses. Cortical signals of time varying electric neuronal activity can be estimated from the EEG. Although such techniques have very high time resolution, two cortical signals even at distant locations will appear to be highly similar due to the low spatial resolution nature of the EEG. In this study a method for eliminating the effect of common sources due to low spatial resolution is presented. It is based on an efficient estimation of the whole-cortex partial coherence matrix. Using as a starting point any linear EEG tomography that satisfies the EEG forward equation, it is shown that the generalized partial coherences for the cortical grey matter current density time series are invariant to the selected tomography. It is empirically shown with simulation experiments that the generalized partial coherences have higher spatial resolution than the classical coherences. The results demonstrate that with as little as 19 electrodes, lag-connected brain regions can often be missed and misplaced even with lagged coherence measures, while the new method detects and localizes correctly the connected regions using the lagged partial coherences.

Published
2011

17. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

Author

Areces-Gonzalez, Ariosky, Paz-Linares, Deirel, Riaz, Usama, Ying Wang, Min Li, Razzaq, Fuleah A., Bosch-Bayard, Jorge F., Gonzalez-Moreira, Eduardo, Ontivero-Ortega, Marlis, Galan-Garcia, Lidice, Martínez-Montes, Eduardo, Minati, Ludovico, Valdes-Sosa, Mitchell J., Bringas-Vega, Maria L., and Valdes-Sosa, Pedro A.

Subjects
ELECTROENCEPHALOGRAPHY, MAGNETIC resonance imaging, BRAIN mapping, SPATIAL resolution, QUALITY control
Abstract

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline

Author

Razzaq, Fuleah A., primary, Calzada-Reyes, Ana, additional, Tang, Qin, additional, Guo, Yanbo, additional, Rabinowitz, Arielle G., additional, Bosch-Bayard, Jorge, additional, Galan-Garcia, Lidice, additional, Virues-Alba, Trinidad, additional, Suarez-Murias, Carlos, additional, Miranda, Ileana, additional, Riaz, Usama, additional, Bernardo Lagomasino, Vivian, additional, Bryce, Cyralene, additional, Anderson, Simon G., additional, Galler, Janina R., additional, Bringas-Vega, Maria L., additional, and Valdes-Sosa, Pedro A., additional

Published
2023
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21. Predicting aging-related decline in physical performance with sparse electrophysiological source imaging

Author

Vega-Hernandez, Mayrim, Galan-Garcia, Lidice, Perez-Hidalgo-Gato, Jhoanna, Ontivero-Ortega, Marlis, Garcia-Reyes, Ronaldo, Bosch-Bayard, Jorge, Martinez-Montes, Eduardo, Marinazzo, Daniele, and Valdes-Sosa, Pedro A.

Subjects
FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Neurons and Cognition (q-bio.NC), Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)
Abstract

Objective: We introduce a methodology for selecting biomarkers from activation and connectivity derived from Electrophysiological Source Imaging (ESI). Specifically, we pursue the selection of stable biomarkers associated with cognitive decline based on source activation and connectivity patterns of resting-state EEG theta rhythm, used as predictors of physical performance decline in aging individuals measured by a Gait Speed (GS) slowing. Methods: Our two-step methodology involves estimating ESI using flexible sparse-smooth-nonnegative models, from which activation ESI (aESI) and connectivity ESI (cESI) features are derived. The Stable Sparse Classifier method then selects potential biomarkers related to GS changes. Results and Conclusions: Our predictive models using aESI outperform traditional methods such as the LORETA family. The models combining aESI and cESI features provide the best prediction of GS changes. Potential biomarkers from activation/connectivity patterns involve orbitofrontal and temporal cortical regions. Significance: The proposed methodology contributes to the understanding of activation and connectivity of GS-related ESI and provides features that are potential biomarkers of GS slowing. Given the known relationship between GS decline and cognitive impairment, this preliminary work opens novel paths to predict the progression of healthy and pathological aging and might allow an ESI-based evaluation of rehabilitation programs.

Published
2023

23. The Global Brain Consortium’s GLAD White Paper: Challenges and Prospects for a Multi-National Children and Adolescents’ Brain Health Study

Author

Bonney, Emmanuel, primary, Byington, Nora, additional, Miranda-Domínguez, Oscar, additional, Mushtaq, Faisal, additional, Yao, Dezhong Yao, additional, Panda, Erin, additional, Rahayel, Shady, additional, Das, Samir, additional, Harmony, Thalia, additional, Tekok-Kilic, Ayda, additional, Gallagher, Anna, additional, Lippé, Sarah, additional, Nyante, Gifty G., additional, Abbo, Catherine, additional, Guerrini, Sofia, additional, Chiarenza, Giuseppe, additional, Bosch-Bayard, Jorge, additional, Ådén, Ulrika, additional, Padilla, Nelly, additional, Brew, Yvonne, additional, Smit, Dirk, additional, Thompson, Paul, additional, Poline, Jean-Baptiste, additional, Rogers, Christine, additional, Amunts, Katrin, additional, Rossini, Paolo Maria, additional, Roskams, Jane A., additional, Smits-Engelsman, Bouwien, additional, Segalowitz, Sid, additional, Prasad, Shailey, additional, Nardos, Rahel, additional, Elison, Jed, additional, Evans, Alan Charles, additional, Fair, Damien, additional, and Valdes-Sosa, Pedro A., additional

Published
2023
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25. Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning

Author

Paz-Linares, Deirel, primary, Gonzalez-Moreira, Eduardo, additional, Areces-Gonzalez, Ariosky, additional, Wang, Ying, additional, Li, Min, additional, Vega-Hernandez, Mayrim, additional, Wang, Qing, additional, Bosch-Bayard, Jorge, additional, Bringas-Vega, Maria L., additional, Martinez-Montes, Eduardo, additional, Valdes-Sosa, Mitchel J., additional, and Valdes-Sosa, Pedro A., additional

Published
2023
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26. Country-level gender inequality is associated with structural differences in the brains of women and men

Author

Zugman, André, Alliende, Luz María, Medel, Vicente, Bethlehem, Richard A.I., Seidlitz, Jakob, Ringlein, Grace, Arango, Celso, Arnatkevičiūtė, Aurina, Asmal, Laila, Bellgrove, Mark, Benegal, Vivek, Bernardo, Miquel, Billeke, Pablo, Bosch-Bayard, Jorge, Bressan, Rodrigo, Busatto, Geraldo F., Castro, Mariana N., Chaim-Avancini, Tiffany, Compte, Albert, Costanzi, Monise, Czepielewski, Leticia, Dazzan, Paola, de la Fuente-Sandoval, Camilo, Di Forti, Marta, Díaz-Caneja, Covadonga M., María Díaz-Zuluaga, Ana, Du Plessis, Stefan, Duran, Fabio L. S., Fittipaldi, Sol, Fornito, Alex, Freimer, Nelson B., Gadelha, Ary, Gama, Clarissa S., Garani, Ranjini, Garcia-Rizo, Clemente, Gonzalez Campo, Cecilia, Gonzalez-Valderrama, Alfonso, Guinjoan, Salvador, Holla, Bharath, Ibañez, Agustín, Ivanovic, Daniza, Jackowski, Andrea, Leon-Ortiz, Pablo, Lochner, Christine, López-Jaramillo, Carlos, Luckhoff, Hilmar, Massuda, Raffael, McGuire, Philip, Miyata, Jun, Mizrahi, Romina, Murray, Robin, Ozerdem, Aysegul, Pan, Pedro M., Parellada, Mara, Phahladira, Lebogan, Ramirez-Mahaluf, Juan P., Reckziegel, Ramiro, Reis Marques, Tiago, Reyes-Madrigal, Francisco, Roos, Annerine, Rosa, Pedro, Salum, Giovanni, Scheffler, Freda, Schumann, Gunter, Serpa, Mauricio, Stein, Dan J., Tepper, Angeles, Tiego, Jeggan, Ueno, Tsukasa, Undurraga, Juan, Undurraga, Eduardo A., Valdes-Sosa, Pedro, Valli, Isabel, Villarreal, Mirta, Winton-Brown, Toby T., Yalin, Nefize, Zamorano, Francisco, Zanetti, Marcus V., cVEDA, Winkler, Anderson M., Pine, Daniel S., Evans-Lacko, Sara, Crossley, Nicolas A., Zugman, André, Alliende, Luz María, Medel, Vicente, Bethlehem, Richard A.I., Seidlitz, Jakob, Ringlein, Grace, Arango, Celso, Arnatkevičiūtė, Aurina, Asmal, Laila, Bellgrove, Mark, Benegal, Vivek, Bernardo, Miquel, Billeke, Pablo, Bosch-Bayard, Jorge, Bressan, Rodrigo, Busatto, Geraldo F., Castro, Mariana N., Chaim-Avancini, Tiffany, Compte, Albert, Costanzi, Monise, Czepielewski, Leticia, Dazzan, Paola, de la Fuente-Sandoval, Camilo, Di Forti, Marta, Díaz-Caneja, Covadonga M., María Díaz-Zuluaga, Ana, Du Plessis, Stefan, Duran, Fabio L. S., Fittipaldi, Sol, Fornito, Alex, Freimer, Nelson B., Gadelha, Ary, Gama, Clarissa S., Garani, Ranjini, Garcia-Rizo, Clemente, Gonzalez Campo, Cecilia, Gonzalez-Valderrama, Alfonso, Guinjoan, Salvador, Holla, Bharath, Ibañez, Agustín, Ivanovic, Daniza, Jackowski, Andrea, Leon-Ortiz, Pablo, Lochner, Christine, López-Jaramillo, Carlos, Luckhoff, Hilmar, Massuda, Raffael, McGuire, Philip, Miyata, Jun, Mizrahi, Romina, Murray, Robin, Ozerdem, Aysegul, Pan, Pedro M., Parellada, Mara, Phahladira, Lebogan, Ramirez-Mahaluf, Juan P., Reckziegel, Ramiro, Reis Marques, Tiago, Reyes-Madrigal, Francisco, Roos, Annerine, Rosa, Pedro, Salum, Giovanni, Scheffler, Freda, Schumann, Gunter, Serpa, Mauricio, Stein, Dan J., Tepper, Angeles, Tiego, Jeggan, Ueno, Tsukasa, Undurraga, Juan, Undurraga, Eduardo A., Valdes-Sosa, Pedro, Valli, Isabel, Villarreal, Mirta, Winton-Brown, Toby T., Yalin, Nefize, Zamorano, Francisco, Zanetti, Marcus V., cVEDA, Winkler, Anderson M., Pine, Daniel S., Evans-Lacko, Sara, and Crossley, Nicolas A.

Abstract

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women’s worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7, 876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women’s brains and provide initial evidence for neuroscience-informed policies for gender equality.

Published
2023

29. TH-179. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity

Author

Bosch-Bayard, Jorge F., primary, Valdes-Sosa, Pedro, additional, Galler, Janina, additional, Bringas-Vega, Maria L., additional, Razzaq, Fuleah A, additional, Evans, Alan C., additional, Wang, Yin, additional, Lopez-Naranjo, Carlos, additional, Galan-Garcia, Lidice, additional, Calzada-Reyes, Ana, additional, Virues-Alba, Trinidad, additional, Rabinowitz, Arielle, additional, Suarez-Murias, Carlos, additional, Sanchez-Castillo, Manuel, additional, Michel, Christoph M., additional, Rogers, Kassandra, additional, Gallagher, Anne, additional, Anderson, Simon, additional, and Prichep, Leslie, additional

Published
2022
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32. The Effect of Neuroepo on Cognition in Parkinson’s Disease Patients Is Mediated by Electroencephalogram Source Activity

Author

Bringas Vega, Maria L., primary, Pedroso Ibáñez, Ivonne, additional, Razzaq, Fuleah A., additional, Zhang, Min, additional, Morales Chacón, Lilia, additional, Ren, Peng, additional, Galan Garcia, Lidice, additional, Gan, Peng, additional, Virues Alba, Trinidad, additional, Lopez Naranjo, Carlos, additional, Jahanshahi, Marjan, additional, Bosch-Bayard, Jorge, additional, and Valdes-Sosa, Pedro A., additional

Published
2022
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33. Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy

Author

Chacon, Lilia Morales, primary, Garcia, Lidice Galan, additional, Bosch-Bayard, Jorge, additional, García-Ramo, Karla Batista, additional, Martin, Margarita Minou Báez, additional, Alfonso, Maydelin Alfonso, additional, Batista, Sheyla Berrillo, additional, de la Paz Bermudez, Tania, additional, González, Judith González, additional, and Coroneux, Abel Sánchez, additional

Published
2022
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36. Common Ictal and Interictal Perfusion Patterns: A Window into the Epileptogenic Network and SUDEP Mechanism in Drug-Resistant Focal Epilepsy

Author

Chacón, Lilia M. Morales, primary, García, Lidice Galan, additional, García-Ramón, Karla Batista, additional, Báez Martin, Margarita Minou, additional, Bosch-Bayard, Jorge, additional, Alfonso, Maydelis Alfonso, additional, Batista, Sheyla Berrillo, additional, Bermudez, Tania de la Paz, additional, González, Judith González, additional, Coroneaux, Abel Sánchez, additional, Ruiz, Ángel Águila, additional, Roque, Marlene Perera, additional, and Matamoro, Leysi Murada, additional

Published
2022
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37. Harmonized-Multinational qEEG Norms (HarMNqEEG)

Author

Li, Min, primary, Wang, Ying, additional, Lopez-Naranjo, Carlos, additional, Abd Hamid, Aini Ismafairus, additional, Evans, Alan C., additional, Savostyanov, Alexander N., additional, Calzada-Reyes, Ana, additional, Areces-Gonzalez, Ariosky, additional, Villringer, Arno, additional, Tobon-Quintero, Carlos A., additional, Garcia-Agustin, Daysi, additional, Paz-Linares, Deirel, additional, Yao, Dezhong, additional, Dong, Li, additional, Aubert-Vazquez, Eduardo, additional, Reza, Faruque, additional, Omar, Hazim, additional, Abdullah, Jafri Malin, additional, Galler, Janina R., additional, Ochoa-Gomez, John F., additional, Prichep, Leslie S., additional, Galan-Garcia, Lidice, additional, Morales-Chacon, Lilia, additional, Valdes-Sosa, Mitchell J., additional, Tröndle, Marius, additional, Mohd Zulkifly, Mohd Faizal Bin, additional, Abdul Rahman, Muhammad Riddha Bin, additional, Milakhina, Natalya S., additional, Langer, Nicolas, additional, Rudych, Pavel, additional, Hu, Shiang, additional, Koenig, Thomas, additional, Virues-Alba, Trinidad A., additional, Lei, Xu, additional, Bringas-Vega, Maria L., additional, Bosch-Bayard, Jorge F., additional, and Valdes-Sosa, Pedro Antonio, additional

Published
2022
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48. Braincharts for the human lifespan

Author

Bethlehem, Richard AI, Seidlitz, Jakob, Astle, Duncan E, Li, Jiao, Liao, Wei, Linares, Deirel P, Liston, Conor, Lombardo, Michael V, Lv, Jinglei, Lynch, Charles, Mallard, Travis T, Marcelis, Machteld, Markello, Ross D, Auyeung, Bonnie, Mazoyer, Bernard, McGuire, Philip, Meaney, Michael J, Mechelli, Andrea, Medic, Nenad, Misic, Bratislav, Morgan, Sarah E, Mothersill, David, Nigg, Joel, Ong, Marcus QW, Ayub, Muhammad, Ortinau, Cynthia, Ossenkoppele, Rik, Ouyang, Minhui, Palaniyappan, Lena, Paly, Leo, Pan, Pedro M, Pantelis, Christos, Park, Min Tae M, Paus, Tomas, Pausova, Zdenka, Ball, Gareth, Binette, Alexa Pichet, Pierce, Karen, Qian, Xing, Qiu, Anqi, Qiu, Jiang, Raznahan, Armin, Rittman, Timothy, Rollins, Caitlin K, Romero-Garcia, Rafael, Ronan, Lisa, Baron-Cohen, Simon, Rosenberg, Monica D, Rowitch, David H, Salum, Giovanni A, Satterthwaite, Theodore D, Schaare, Lina, Schachar, Russell J, Scholl, Michael, Schultz, Aaron P, Schumann, Gunter, Sharp, David, Beare, Richard, Bedford, Saashi A, Benegal, Vivek, Beyer, Frauke, Bae, Jong Bin, White, Simon R, Blangero, John, Cabez, Manuel Blesa, Boardman, James P, Borzage, Matthew, Bosch-Bayard, Jorge F, Bourke, Niall, Calhoun, Vince D, Chakravarty, Mallar M, Chen, Christina, Chertavian, Casey, Vogel, Jacob W, Chetelat, Gael, Chong, Yap S, Cole, James H, Corvin, Aiden, Courchesne, Eric, Crivello, Fabrice, Cropley, Vanessa L, Crosbie, Jennifer, Crossley, Nicolas, Delarue, Marion, Anderson, Kevin M, Desrivieres, Sylvane, Devenyi, Gabriel, Biase, Maria A Di, Dolan, Ray, Donald, Kirsten A, Donohoe, Gary, Dunlop, Katharine, Edwards, Anthony D, Elison, Jed T, Ellis, Cameron T, Adamson, Chris, Elman, Jeremy A, Eyler, Lisa, Fair, Damien A, Fletcher, Paul C, Fonagy, Peter, Franz, Carol E, Galan-Garcia, Lidice, Gholipour, Ali, Giedd, Jay, Gilmore, John H, Adler, Sophie, Glahn, David C, Goodyer, Ian, Grant, PE, Groenewold, Nynke A, Gunning, Faith M, Gur, Raquel E, Gur, Ruben C, Hammill, Christopher F, Hansson, Oskar, Hedden, Trey, Alexopoulos, George S, Heinz, Andreas, Henson, Richard, Heuer, Katja, Hoare, Jacqueline, Holla, Bharath, Holmes, Avram J, Holt, Rosie, Huang, Hao, Im, Kiho, Ipser, Jonathan, Anagnostou, Evdokia, Jack, Clifford R, Jackowski, Andrea P, Jia, Tianye, Johnson, Keith A, Jones, David T, Jones, Peter B, Kahn, Rene, Karlsson, Hasse, Karlsson, Linnea, Kawashima, Ryuta, Areces-Gonzalez, Ariosky, Kelley, Elizabeth A, Kern, Silke, Kim, KW, Kitzbichler, Manfred G, Kremen, William S, Lalonde, Francois, Landeau, Brigitte, Lee, Subin, Lerch, Jason, and Lewis, John D

Abstract

Over the past 25 years, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, there are no reference standards against which to anchor measures of individual differences in brain morphology, in contrast to growth charts for traits such as height and weight. Here, we built an interactive online resource (www.brainchart.io) to quantify individual differences in brain structure from any current or future magnetic resonance imaging (MRI) study, against models of expected age-related trends. With the goal of basing these on the largest and most inclusive dataset, we aggregated MRI data spanning 115 days post-conception through 100 postnatal years, totaling 122,123 scans from 100,071 individuals in over 100 studies across 6 continents. When quantified as centile scores relative to the reference models, individual differences show high validity with non-MRI brain growth estimates and high stability across longitudinal assessment. Centile scores helped identify previously unreported brain developmental milestones and demonstrated increased genetic heritability compared to non-centiled MRI phenotypes. Crucially for the study of brain disorders, centile scores provide a standardised and interpretable measure of deviation that reveals new patterns of neuroanatomical differences across neurological and psychiatric disorders emerging during development and ageing. In sum, brain charts for the human lifespan are an essential first step towards robust, standardised quantification of individual variation and for characterizing deviation from age-related trends. Our global collaborative study …

Published
2021

49. Linear causal filtering: definition and theory

Author

Pascual-Marqui, Roberto D; https://orcid.org/0000-0002-5029-4065, Biscay, Rolando J, Bosch-Bayard, Jorge, Achermann, Peter, Faber, Pascal, Kinoshita, Toshihiko, Kochi, Kieko, Pascual-Marqui, Roberto D; https://orcid.org/0000-0002-5029-4065, Biscay, Rolando J, Bosch-Bayard, Jorge, Achermann, Peter, Faber, Pascal, Kinoshita, Toshihiko, and Kochi, Kieko

Abstract

This work provides a framework based on multivariate autoregressive modeling for linear causal filtering in the sense of Granger. In its bivariate form, the linear causal filter defined here takes as input signals A and B, and it filters out the causal effect of B on A, thus yielding two new signals only containing the Granger-causal effect of A on B. In its general multivariate form for more than two signals, the effect of all indirect causal connections between A and B, mediated by all other signals, are accounted for, partialled out, and filtered out also. The importance of this filter is that it enables the estimation of directional measures of causal information flow from any non-causal, non-directional measure of association. For instance, based on the classic coherence, a directional measure of strength of information flow from A to B is obtained when applied to the linear causal filtered pair containing only A to B connectivity information. This particular case is equivalent to the isolated effective coherence (doi.org/10.3389/fnhum.2014.00448). Of more recent interest are the large family of phase-phase, phase-amplitude, and amplitude-amplitude cross-frequency coupling measures which are non-directional. The linear causal filter makes it now possible to estimate the directional causal versions these measures of association. One important field of application is in brain connectivity analysis based on cortical signals of electric neuronal activity (e.g. estimated sources of EEG and MEG, and invasive intracranial ECoG recordings). The linear causal filter introduced here provides a novel solution to the problem of estimating the direction of information flow from any non-directional measure of association. This work provides definitions, non-ambiguous equations, and clear prescriptions for implementing the linear causal filter in diverse settings.

Published
2021

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