diff --git a/publications.bib b/publications.bib index ed1d1b1..73c99b8 100644 --- a/publications.bib +++ b/publications.bib @@ -771,7 +771,7 @@ @article{mandl_addressing_2024 author = {Mandl, Maximilian M. and Becker-Pennrich, Andrea S. and Hinske, Ludwig C. and Hoffmann, Sabine and Boulesteix, Anne-Laure}, month = jul, year = {2024}, - keywords = {multiverse, to read}, + keywords = {to read, multiverse}, pages = {152}, file = {Mandl et al. - 2024 - Addressing researcher degrees of freedom through m.pdf:/home/alpron/Zotero/storage/DZMEFP7J/Mandl et al. - 2024 - Addressing researcher degrees of freedom through m.pdf:application/pdf}, } @@ -788,3 +788,232 @@ @article{field_consequences_2024 year = {2024}, note = {Special Issue}, } + +@misc{sadil_maps_2024, + title = {From {Maps} to {Models}: {A} {Survey} on the {Reliability} of {Small} {Studies} of {Task}-{Based} {fMRI}}, + copyright = {http://creativecommons.org/licenses/by-nc/4.0/}, + shorttitle = {From {Maps} to {Models}}, + url = {http://biorxiv.org/lookup/doi/10.1101/2024.08.05.606611}, + doi = {10.1101/2024.08.05.606611}, + abstract = {Task-based functional magnetic resonance imaging is a powerful tool for studying brain function, but neuroimaging research produces ongoing concerns regarding small-sample studies and how to interpret them. Although it is well understood that larger samples are preferable, many situations require researchers to make judgments from small studies, including reviewing the existing literature, analyzing pilot data, or assessing subsamples. Quantitative guidance on how to make these judgments remains scarce. To address this, we leverage the Human Connectome Project's Young Adult dataset to survey various analyses--from regional activation maps to predictive models. We find that, for some classic analyses such as detecting regional activation or cluster peak location, studies with as few as 40 subjects are adequate, although this depends crucially on effect sizes. For predictive modeling, similar sizes can be adequate for detecting whether features are predictable, but at least an order of magnitude more (at least hundreds) may be required for developing consistent predictions. These results offer valuable insights for designing and interpreting fMRI studies, emphasizing the importance of considering effect size, sample size, and analysis approach when assessing the reliability of findings. We hope that this survey serves as a reference for identifying which kinds of research questions can be reliably answered with small-scale studies.}, + language = {en}, + urldate = {2024-08-09}, + author = {Sadil, Patrick and Lindquist, Martin A.}, + month = aug, + year = {2024}, + keywords = {to read}, +} + +@article{taylor_set_2024, + title = {A {Set} of {FMRI} {Quality} {Control} {Tools} in {AFNI}: {Systematic}, in-depth, and interactive {QC} with afni\_proc.py and more}, + volume = {2}, + issn = {2837-6056}, + shorttitle = {A {Set} of {FMRI} {Quality} {Control} {Tools} in {AFNI}}, + url = {https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00246/123633/A-Set-of-FMRI-Quality-Control-Tools-in-AFNI}, + doi = {10.1162/imag_a_00246}, + abstract = {Abstract + Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni\_proc.py. These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each “QC block,” as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.}, + language = {en}, + urldate = {2024-08-09}, + journal = {Imaging Neuroscience}, + author = {Taylor, Paul A. and Glen, Daniel R. and Chen, Gang and Cox, Robert W. and Hanayik, Taylor and Rorden, Chris and Nielson, Dylan M. and Rajendra, Justin K. and Reynolds, Richard C.}, + month = aug, + year = {2024}, + keywords = {qc, to read}, + pages = {1--39}, + file = {Taylor et al. - 2024 - A Set of FMRI Quality Control Tools in AFNI Syste.pdf:/home/alpron/Zotero/storage/L84FIQ4Z/Taylor et al. - 2024 - A Set of FMRI Quality Control Tools in AFNI Syste.pdf:application/pdf}, +} + +@misc{cohen-adad_open_2024, + title = {Open {Source} in {Lab} {Management}}, + url = {http://arxiv.org/abs/2405.07774}, + abstract = {This document explores the advantages of integrating open source software and practices in managing a scientific lab, emphasizing reproducibility and the avoidance of pitfalls. It details practical applications from website management using GitHub Pages to organizing datasets in compliance with BIDS standards, highlights the importance of continuous testing for data integrity, IT management through Ansible for efficient system configuration, open source software development. The broader goal is to promote transparent, reproducible science by adopting open source tools. This approach not only saves time but exposes students to best practices, enhancing the transparency and reproducibility of scientific research.}, + urldate = {2024-08-09}, + publisher = {arXiv}, + author = {Cohen-Adad, Julien}, + month = may, + year = {2024}, + note = {arXiv:2405.07774 [cs]}, + keywords = {to read}, + file = {Cohen-Adad - 2024 - Open Source in Lab Management.html:/home/alpron/Zotero/storage/NSUKMR8S/Cohen-Adad - 2024 - Open Source in Lab Management.html:text/html;Cohen-Adad - 2024 - Open Source in Lab Management.pdf:/home/alpron/Zotero/storage/U8PYI68X/Cohen-Adad - 2024 - Open Source in Lab Management.pdf:application/pdf}, +} + +@article{desrosiers-gregoire_standardized_2024, + title = {A standardized image processing and data quality platform for rodent {fMRI}}, + volume = {15}, + issn = {2041-1723}, + url = {https://www.nature.com/articles/s41467-024-50826-8}, + doi = {10.1038/s41467-024-50826-8}, + abstract = {Abstract + Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.}, + language = {en}, + number = {1}, + urldate = {2024-08-12}, + journal = {Nature Communications}, + author = {Desrosiers-Grégoire, Gabriel and Devenyi, Gabriel A. and Grandjean, Joanes and Chakravarty, M. Mallar}, + month = aug, + year = {2024}, + keywords = {to read}, + pages = {6708}, + file = {Desrosiers-Grégoire et al. - 2024 - A standardized image processing and data quality p.pdf:/home/alpron/Zotero/storage/3J874RHT/Desrosiers-Grégoire et al. - 2024 - A standardized image processing and data quality p.pdf:application/pdf}, +} + +@article{karakuzu_qmri-bids_2022, + title = {{qMRI}-{BIDS}: {An} extension to the brain imaging data structure for quantitative magnetic resonance imaging data}, + volume = {9}, + issn = {2052-4463}, + shorttitle = {{qMRI}-{BIDS}}, + url = {https://www.nature.com/articles/s41597-022-01571-4}, + doi = {10.1038/s41597-022-01571-4}, + abstract = {Abstract + + The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store + multimodal + structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.}, + language = {en}, + number = {1}, + urldate = {2024-08-12}, + journal = {Scientific Data}, + author = {Karakuzu, Agah and Appelhoff, Stefan and Auer, Tibor and Boudreau, Mathieu and Feingold, Franklin and Khan, Ali R. and Lazari, Alberto and Markiewicz, Chris and Mulder, Martijn and Phillips, Christophe and Salo, Taylor and Stikov, Nikola and Whitaker, Kirstie and De Hollander, Gilles}, + month = aug, + year = {2022}, + keywords = {BIDS, to read}, + pages = {517}, + file = {Karakuzu et al. - 2022 - qMRI-BIDS An extension to the brain imaging data .pdf:/home/alpron/Zotero/storage/J6M7GHA3/Karakuzu et al. - 2022 - qMRI-BIDS An extension to the brain imaging data .pdf:application/pdf}, +} + +@misc{wang_reproducible_2023, + title = {A reproducible benchmark of resting-state {fMRIdenoising} strategies using {fMRIPrep} and {Nilearn}}, + copyright = {http://creativecommons.org/licenses/by/4.0/}, + url = {https://neurolibre.org/papers/10.55458/neurolibre.00012}, + doi = {10.55458/neurolibre.00012}, + abstract = {Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics for connectivity analyses, built primarily on the fMRIPrep (Esteban et al., 2018) and Nilearn (Abraham et al., 2014) software packages. We apply this reproducible benchmark to investigate the robustness of the conclusions across two different datasets and two versions of fMRIPrep. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. These results demonstrate that a reproducible denoising benchmark can effectively assess the robustness of conclusions across multiple datasets and software versions. In addition to reproducing core computations, interested readers can also reproduce or modify the figures of the article using the Jupyter Book project (Granger \& Pérez, 2021) and the Neurolibre (Karakuzu et al., 2022) reproducible preprint server. With the denoising benchmark, we hope to provide useful guidelines for the community, and that our software infrastructure will facilitate continued development as the state-of-the-art advances.}, + language = {en}, + urldate = {2024-08-12}, + author = {Wang, Hao-Ting and Meisler, Steven L and Sharmarke, Hanad and Clarke, Natasha and Paugam, François and Gensollen, Nicolas and Markiewicz, Christopher J and Thirion, Bertrand and Bellec, Pierre}, + month = jun, + year = {2023}, + file = {Wang et al. - 2023 - A reproducible benchmark of resting-state fMRIdeno.pdf:/home/alpron/Zotero/storage/AFHQLWEI/Wang et al. - 2023 - A reproducible benchmark of resting-state fMRIdeno.pdf:application/pdf}, +} + +@article{zabihi_nonlinear_2024, + title = {Nonlinear latent representations of high-dimensional task-{fMRI} data: {Unveiling} cognitive and behavioral insights in heterogeneous spatial maps}, + volume = {19}, + issn = {1932-6203}, + shorttitle = {Nonlinear latent representations of high-dimensional task-{fMRI} data}, + url = {https://dx.plos.org/10.1371/journal.pone.0308329}, + doi = {10.1371/journal.pone.0308329}, + abstract = {Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.}, + language = {en}, + number = {8}, + urldate = {2024-08-12}, + journal = {PLOS ONE}, + author = {Zabihi, Mariam and Kia, Seyed Mostafa and Wolfers, Thomas and De Boer, Stijn and Fraza, Charlotte and Dinga, Richard and Arenas, Alberto Llera and Bzdok, Danilo and Beckmann, Christian F. and Marquand, Andre}, + editor = {Tangherloni, Andrea}, + month = aug, + year = {2024}, + pages = {e0308329}, + file = {Zabihi et al. - 2024 - Nonlinear latent representations of high-dimension.pdf:/home/alpron/Zotero/storage/CZZUYM3Q/Zabihi et al. - 2024 - Nonlinear latent representations of high-dimension.pdf:application/pdf}, +} + +@article{white_data_2022, + title = {Data sharing and privacy issues in neuroimaging research: {Opportunities}, obstacles, challenges, and monsters under the bed}, + volume = {43}, + issn = {1065-9471, 1097-0193}, + shorttitle = {Data sharing and privacy issues in neuroimaging research}, + url = {https://onlinelibrary.wiley.com/doi/10.1002/hbm.25120}, + doi = {10.1002/hbm.25120}, + abstract = {Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.}, + language = {en}, + number = {1}, + urldate = {2024-08-12}, + journal = {Human Brain Mapping}, + author = {White, Tonya and Blok, Elisabet and Calhoun, Vince D.}, + month = jan, + year = {2022}, + keywords = {to read, data sharing}, + pages = {278--291}, + file = {White et al. - 2022 - Data sharing and privacy issues in neuroimaging re.pdf:/home/alpron/Zotero/storage/CAG53AC7/White et al. - 2022 - Data sharing and privacy issues in neuroimaging re.pdf:application/pdf}, +} + +@article{mennes_making_2013, + title = {Making data sharing work: {The} {FCP}/{INDI} experience}, + volume = {82}, + issn = {10538119}, + shorttitle = {Making data sharing work}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811912010671}, + doi = {10.1016/j.neuroimage.2012.10.064}, + abstract = {Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.}, + language = {en}, + urldate = {2024-08-12}, + journal = {NeuroImage}, + author = {Mennes, Maarten and Biswal, Bharat B. and Castellanos, F. Xavier and Milham, Michael P.}, + month = nov, + year = {2013}, + keywords = {to read, data sharing}, + pages = {683--691}, + file = {Mennes et al. - 2013 - Making data sharing work The FCPINDI experience.pdf:/home/alpron/Zotero/storage/E2SH89FC/Mennes et al. - 2013 - Making data sharing work The FCPINDI experience.pdf:application/pdf}, +} + +@article{li_moving_2024, + title = {Moving beyond processing- and analysis-related variation in resting-state functional brain imaging}, + issn = {2397-3374}, + url = {https://www.nature.com/articles/s41562-024-01942-4}, + doi = {10.1038/s41562-024-01942-4}, + language = {en}, + urldate = {2024-08-12}, + journal = {Nature Human Behaviour}, + author = {Li, Xinhui and Bianchini Esper, Nathalia and Ai, Lei and Giavasis, Steve and Jin, Hecheng and Feczko, Eric and Xu, Ting and Clucas, Jon and Franco, Alexandre and Sólon Heinsfeld, Anibal and Adebimpe, Azeez and Vogelstein, Joshua T. and Yan, Chao-Gan and Esteban, Oscar and Poldrack, Russell A. and Craddock, Cameron and Fair, Damien and Satterthwaite, Theodore and Kiar, Gregory and Milham, Michael P.}, + month = aug, + year = {2024}, + keywords = {fMRI, to read}, +} + +@article{mehta_xcp-d_2024, + title = {{XCP}-{D}: {A} {Robust} {Pipeline} for the {Post}-{Processing} of {fMRI} data}, + issn = {2837-6056}, + shorttitle = {{XCP}-{D}}, + url = {https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00257/123715/XCP-D-A-Robust-Pipeline-for-the-Post-Processing-of}, + doi = {10.1162/imag_a_00257}, + abstract = {Abstract + Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they may not support output from different pre-processing pipelines, may have limited documentation, and may not follow generally accepted data organization standards (e.g. BIDS). In response, we present XCP-D: a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University of Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Apptainer image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NIfTI or CIFTI files following pre-processing with fMRIPrep, HCP, or ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded \>5,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.}, + language = {en}, + urldate = {2024-08-12}, + journal = {Imaging Neuroscience}, + author = {Mehta, Kahini and Salo, Taylor and Madison, Thomas J. and Adebimpe, Azeez and Bassett, Danielle S. and Bertolero, Max and Cieslak, Matthew and Covitz, Sydney and Houghton, Audrey and Keller, Arielle S. and Lundquist, Jacob T. and Luo, Audrey and Miranda-Dominguez, Oscar and Nelson, Steve M. and Shafiei, Golia and Shanmugan, Sheila and Shinohara, Russell T. and Smyser, Christopher D. and Sydnor, Valerie J. and Weldon, Kimberly B. and Feczko, Eric and Fair, Damien A. and Satterthwaite, Theodore D.}, + month = jul, + year = {2024}, + keywords = {fMRI, to read}, +} + +@article{caeyenberghs_enigmas_2024, + title = {{ENIGMA}’s simple seven: {Recommendations} to enhance the reproducibility of resting-state {fMRI} in traumatic brain injury}, + volume = {42}, + issn = {22131582}, + shorttitle = {{ENIGMA}’s simple seven}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S221315822400024X}, + doi = {10.1016/j.nicl.2024.103585}, + language = {en}, + urldate = {2024-08-12}, + journal = {NeuroImage: Clinical}, + author = {Caeyenberghs, Karen and Imms, Phoebe and Irimia, Andrei and Monti, Martin M. and Esopenko, Carrie and De Souza, Nicola L. and Dominguez D, Juan F. and Newsome, Mary R. and Dobryakova, Ekaterina and Cwiek, Andrew and Mullin, Hollie A.C. and Kim, Nicholas J. and Mayer, Andrew R. and Adamson, Maheen M. and Bickart, Kevin and Breedlove, Katherine M. and Dennis, Emily L. and Disner, Seth G. and Haswell, Courtney and Hodges, Cooper B. and Hoskinson, Kristen R. and Johnson, Paula K. and Königs, Marsh and Li, Lucia M. and Liebel, Spencer W. and Livny, Abigail and Morey, Rajendra A. and Muir, Alexandra M. and Olsen, Alexander and Razi, Adeel and Su, Matthew and Tate, David F. and Velez, Carmen and Wilde, Elisabeth A. and Zielinski, Brandon A. and Thompson, Paul M. and Hillary, Frank G.}, + year = {2024}, + keywords = {Review, to read}, + pages = {103585}, + file = {Caeyenberghs et al. - 2024 - ENIGMA’s simple seven Recommendations to enhance .pdf:/home/alpron/Zotero/storage/YQPWVWSN/Caeyenberghs et al. - 2024 - ENIGMA’s simple seven Recommendations to enhance .pdf:application/pdf}, +} + +@article{makowski_quality_2024, + title = {Quality over quantity: powering neuroimaging samples in psychiatry}, + issn = {0893-133X, 1740-634X}, + shorttitle = {Quality over quantity}, + url = {https://www.nature.com/articles/s41386-024-01893-4}, + doi = {10.1038/s41386-024-01893-4}, + language = {en}, + urldate = {2024-08-12}, + journal = {Neuropsychopharmacology}, + author = {Makowski, Carolina and Nichols, Thomas E. and Dale, Anders M.}, + month = jun, + year = {2024}, + keywords = {to read}, +}