Merge pull request #14 from Inria-Empenn/hugoblox-import-publications

Hugo Blox Builder - Import latest publications

content/publication/deyoung-beyond-2024/cite.bib

@@ -0,0 +1,14 @@
+@misc{deyoung_beyond_2024,
+ abstract = {Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences.},
+ author = {DeYoung, Colin G. and Hilger, Kirsten and Hanson, Jamie L. and Abend, Rany and Allen, Timothy and Beaty, Roger and Blain, Scott D. and Chavez, Robert and Engel, Stephen A. and Ma, Feilong and Fornito, Alex and Genç, Erhan and Goghari, Vina and Grazioplene, Rachael G. and Homan, Philipp and Joyner, Keenan and Kaczkurkin, Antonia N. and Latzman, Robert D and Martin, Elizabeth A and Nikolaidis, Aki and Pickering, Alan and Safron, Adam and Sassenberg, Tyler and Servaas, Michelle and Smillie, Luke D. and Spreng, R. Nathan and Viding, Essi and Wacker, Jan},
+ copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
+ doi = {10.31219/osf.io/bjn62},
+ file = {DeYoung et al. - 2024 - Beyond Increasing Sample Sizes Optimizing Effect .pdf:/home/alpron/Zotero/storage/ZS9NMZRN/DeYoung et al. - 2024 - Beyond Increasing Sample Sizes Optimizing Effect .pdf:application/pdf},
+ language = {en},
+ month = {July},
+ shorttitle = {Beyond Increasing Sample Sizes},
+ title = {Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences},
+ url = {https://osf.io/bjn62},
+ urldate = {2024-07-30},
+ year = {2024}
+}

content/publication/deyoung-beyond-2024/index.md

@@ -0,0 +1,56 @@
+---
+title: 'Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research
+  on Individual Differences'
+authors:
+- Colin G. DeYoung
+- Kirsten Hilger
+- Jamie L. Hanson
+- Rany Abend
+- Timothy Allen
+- Roger Beaty
+- Scott D. Blain
+- Robert Chavez
+- Stephen A. Engel
+- Feilong Ma
+- Alex Fornito
+- Erhan Genç
+- Vina Goghari
+- Rachael G. Grazioplene
+- Philipp Homan
+- Keenan Joyner
+- Antonia N. Kaczkurkin
+- Robert D Latzman
+- Elizabeth A Martin
+- Aki Nikolaidis
+- Alan Pickering
+- Adam Safron
+- Tyler Sassenberg
+- Michelle Servaas
+- Luke D. Smillie
+- R. Nathan Spreng
+- Essi Viding
+- Jan Wacker
+date: '2024-07-01'
+publishDate: '2024-08-01T09:27:33.839571Z'
+publication_types:
+- manuscript
+doi: 10.31219/osf.io/bjn62
+abstract: 'Linking neurobiology to relatively stable individual differences in cognition,
+  emotion, motivation, and behavior can require large sample sizes to yield replicable
+  results. Given the nature of between-person research, sample sizes at least in the
+  hundreds are likely to be necessary in most neuroimaging studies of individual differences,
+  regardless of whether they are investigating the whole brain or more focal hypotheses.
+  However, the appropriate sample size depends on the expected effect size. Therefore,
+  we propose four strategies to increase effect sizes in neuroimaging research, which
+  may help to enable the detection of replicable between-person effects in samples
+  in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging
+  tasks and behavioral constructs of interest; (2) increasing the reliability of both
+  neural and psychological measurement; (3) individualization of measures for each
+  participant; and (4) using multivariate approaches with cross-validation instead
+  of univariate approaches. We discuss challenges associated with these methods and
+  highlight strategies for improvements that will help the field to move toward a
+  more robust and accessible neuroscience of individual differences.'
+links:
+- name: URL
+  url: https://osf.io/bjn62
+---

content/publication/feldman-value-2024/cite.bib

@@ -0,0 +1,14 @@
+@article{feldman_value_2024,
+ author = {Feldman, Gilad},
+ collaborator = {Center For Open Science},
+ copyright = {Creative Commons Attribution 4.0 International},
+ doi = {10.17605/OSF.IO/BTNUJ},
+ file = {Feldman - 2024 - The value of replications goes beyond replicabilit.pdf:/home/alpron/Zotero/storage/MAJXU58Z/Feldman - 2024 - The value of replications goes beyond replicabilit.pdf:application/pdf},
+ keywords = {to read},
+ note = {Publisher: OSF},
+ shorttitle = {The value of replications goes beyond replicability and is tied to the value of the research it replicates},
+ title = {The value of replications goes beyond replicability and is tied to the value of the research it replicates: Commentary on Isager et al. (2024)},
+ url = {https://osf.io/btnuj/},
+ urldate = {2024-08-01},
+ year = {2024}
+}

content/publication/feldman-value-2024/index.md

@@ -0,0 +1,16 @@
+---
+title: 'The value of replications goes beyond replicability and is tied to the value
+  of the research it replicates: Commentary on Isager et al. (2024)'
+authors:
+- Gilad Feldman
+date: '2024-01-01'
+publishDate: '2024-08-01T09:27:33.854306Z'
+publication_types:
+- article-journal
+doi: 10.17605/OSF.IO/BTNUJ
+tags:
+- to read
+links:
+- name: URL
+  url: https://osf.io/btnuj/
+---

content/publication/grady-influence-2020/cite.bib

@@ -0,0 +1,18 @@
+@article{grady_influence_2020,
+ abstract = {Limited statistical power due to small sample sizes is a problem in fMRI research. Most of the work to date has examined the impact of sample size on task‐related activation, with less attention paid to the influence of sample size on brain‐behavior correlations, especially in actual experimental fMRI data. We addressed this issue using two large data sets (a working memory task, N = 171, and a relational processing task, N = 865) and both univariate and multivariate approaches to voxel‐wise correlations. We created subsamples of different sizes and calculated correlations between task‐related activity at each voxel and task performance. Across both data sets the magnitude of the brain‐behavior correlations decreased and similarity across spatial maps increased with larger sample sizes. The multivariate technique identified more extensive correlated areas and more similarity across spatial maps, suggesting that a multivariate approach would provide a consistent advantage over univariate approaches in the stability of brain‐behavior correlations. In addition, the multivariate analyses showed that a sample size of roughly 80 or more participants would be needed for stable estimates of correlation magnitude in these data sets. Importantly, a number of additional factors would likely influence the choice of sample size for assessing such correlations in any given experiment, including the cognitive task of interest and the amount of data collected per participant. Our results provide novel experimental evidence in two independent data sets that the sample size commonly used in fMRI studies of 20–30 participants is very unlikely to be sufficient for obtaining reproducible brain‐behavior correlations, regardless of analytic approach., Limited statistical power due to small sample sizes is a problem in fMRI research. Most of the work to date has examined the impact of sample size on task‐related activation, with less attention paid to the influence of sample size on brain‐behavior correlations, especially in actual experimental fMRI data. Our results provide novel experimental evidence in two independent data sets that the sample size commonly used in fMRI studies of 20–30 participants is very unlikely to be sufficient for obtaining reproducible brain‐behavior correlations, regardless of whether a univariate or multivariate approach is used.},
+ author = {Grady, Cheryl L. and Rieck, Jenny R. and Nichol, Daniel and Rodrigue, Karen M. and Kennedy, Kristen M.},
+ doi = {10.1002/hbm.25217},
+ file = {PubMed Central Full Text PDF:/home/alpron/Zotero/storage/W742CY9G/Grady et al. - 2020 - Influence of sample size and analytic approach on .pdf:application/pdf},
+ issn = {1065-9471},
+ journal = {Human Brain Mapping},
+ month = {September},
+ number = {1},
+ pages = {204--219},
+ pmcid = {PMC7721240},
+ pmid = {32996635},
+ title = {Influence of sample size and analytic approach on stability and interpretation of brain‐behavior correlations in task‐related fMRI data},
+ url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721240/},
+ urldate = {2024-07-31},
+ volume = {42},
+ year = {2020}
+}

content/publication/grady-influence-2020/index.md

@@ -0,0 +1,48 @@
+---
+title: Influence of sample size and analytic approach on stability and interpretation
+  of brain‐behavior correlations in task‐related fMRI data
+authors:
+- Cheryl L. Grady
+- Jenny R. Rieck
+- Daniel Nichol
+- Karen M. Rodrigue
+- Kristen M. Kennedy
+date: '2020-09-01'
+publishDate: '2024-08-01T09:27:33.831633Z'
+publication_types:
+- article-journal
+publication: '*Human Brain Mapping*'
+doi: 10.1002/hbm.25217
+abstract: Limited statistical power due to small sample sizes is a problem in fMRI
+  research. Most of the work to date has examined the impact of sample size on task‐related
+  activation, with less attention paid to the influence of sample size on brain‐behavior
+  correlations, especially in actual experimental fMRI data. We addressed this issue
+  using two large data sets (a working memory task, N = 171, and a relational processing
+  task, N = 865) and both univariate and multivariate approaches to voxel‐wise correlations.
+  We created subsamples of different sizes and calculated correlations between task‐related
+  activity at each voxel and task performance. Across both data sets the magnitude
+  of the brain‐behavior correlations decreased and similarity across spatial maps
+  increased with larger sample sizes. The multivariate technique identified more extensive
+  correlated areas and more similarity across spatial maps, suggesting that a multivariate
+  approach would provide a consistent advantage over univariate approaches in the
+  stability of brain‐behavior correlations. In addition, the multivariate analyses
+  showed that a sample size of roughly 80 or more participants would be needed for
+  stable estimates of correlation magnitude in these data sets. Importantly, a number
+  of additional factors would likely influence the choice of sample size for assessing
+  such correlations in any given experiment, including the cognitive task of interest
+  and the amount of data collected per participant. Our results provide novel experimental
+  evidence in two independent data sets that the sample size commonly used in fMRI
+  studies of 20–30 participants is very unlikely to be sufficient for obtaining reproducible
+  brain‐behavior correlations, regardless of analytic approach., Limited statistical
+  power due to small sample sizes is a problem in fMRI research. Most of the work
+  to date has examined the impact of sample size on task‐related activation, with
+  less attention paid to the influence of sample size on brain‐behavior correlations,
+  especially in actual experimental fMRI data. Our results provide novel experimental
+  evidence in two independent data sets that the sample size commonly used in fMRI
+  studies of 20–30 participants is very unlikely to be sufficient for obtaining reproducible
+  brain‐behavior correlations, regardless of whether a univariate or multivariate
+  approach is used.
+links:
+- name: URL
+  url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721240/
+---

content/publication/isager-exploring-2024/cite.bib

@@ -0,0 +1,16 @@
+@article{isager_exploring_2024,
+ author = {Isager, Peder M. and Lakens, Daniël and Van Leeuwen, Thed and Van 'T Veer, Anna E.},
+ doi = {10.1016/j.cortex.2023.10.012},
+ file = {Isager et al. - 2024 - Exploring a formal approach to selecting studies f.pdf:/home/alpron/Zotero/storage/Z2VP3RRD/Isager et al. - 2024 - Exploring a formal approach to selecting studies f.pdf:application/pdf},
+ issn = {00109452},
+ journal = {Cortex},
+ language = {en},
+ month = {February},
+ pages = {330--346},
+ shorttitle = {Exploring a formal approach to selecting studies for replication},
+ title = {Exploring a formal approach to selecting studies for replication: A feasibility study in social neuroscience},
+ url = {https://linkinghub.elsevier.com/retrieve/pii/S0010945223002691},
+ urldate = {2024-08-01},
+ volume = {171},
+ year = {2024}
+}

content/publication/isager-exploring-2024/index.md

@@ -0,0 +1,18 @@
+---
+title: 'Exploring a formal approach to selecting studies for replication: A feasibility
+  study in social neuroscience'
+authors:
+- Peder M. Isager
+- Daniël Lakens
+- Thed Van Leeuwen
+- Anna E. Van 'T Veer
+date: '2024-02-01'
+publishDate: '2024-08-01T09:27:33.860414Z'
+publication_types:
+- article-journal
+publication: '*Cortex*'
+doi: 10.1016/j.cortex.2023.10.012
+links:
+- name: URL
+  url: https://linkinghub.elsevier.com/retrieve/pii/S0010945223002691
+---

content/publication/marek-reproducible-2022/cite.bib

@@ -0,0 +1,20 @@
+@article{marek_reproducible_2022,
+ abstract = {Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.},
+ author = {Marek, Scott and Tervo-Clemmens, Brenden and Calabro, Finnegan J. and Montez, David F. and Kay, Benjamin P. and Hatoum, Alexander S. and Donohue, Meghan Rose and Foran, William and Miller, Ryland L. and Hendrickson, Timothy J. and Malone, Stephen M. and Kandala, Sridhar and Feczko, Eric and Miranda-Dominguez, Oscar and Graham, Alice M. and Earl, Eric A. and Perrone, Anders J. and Cordova, Michaela and Doyle, Olivia and Moore, Lucille A. and Conan, Gregory M. and Uriarte, Johnny and Snider, Kathy and Lynch, Benjamin J. and Wilgenbusch, James C. and Pengo, Thomas and Tam, Angela and Chen, Jianzhong and Newbold, Dillan J. and Zheng, Annie and Seider, Nicole A. and Van, Andrew N. and Metoki, Athanasia and Chauvin, Roselyne J. and Laumann, Timothy O. and Greene, Deanna J. and Petersen, Steven E. and Garavan, Hugh and Thompson, Wesley K. and Nichols, Thomas E. and Yeo, B. T. Thomas and Barch, Deanna M. and Luna, Beatriz and Fair, Damien A. and Dosenbach, Nico U. F.},
+ copyright = {2022 The Author(s), under exclusive licence to Springer Nature Limited},
+ doi = {10.1038/s41586-022-04492-9},
+ file = {Full Text PDF:/home/alpron/Zotero/storage/RVNDP5RZ/Marek et al. - 2022 - Reproducible brain-wide association studies requir.pdf:application/pdf},
+ issn = {1476-4687},
+ journal = {Nature},
+ keywords = {Cognitive neuroscience, Psychology},
+ language = {en},
+ month = {March},
+ note = {Publisher: Nature Publishing Group},
+ number = {7902},
+ pages = {654--660},
+ title = {Reproducible brain-wide association studies require thousands of individuals},
+ url = {https://www.nature.com/articles/s41586-022-04492-9},
+ urldate = {2024-07-31},
+ volume = {603},
+ year = {2022}
+}

content/publication/marek-reproducible-2022/index.md

@@ -0,0 +1,83 @@
+---
+title: Reproducible brain-wide association studies require thousands of individuals
+authors:
+- Scott Marek
+- Brenden Tervo-Clemmens
+- Finnegan J. Calabro
+- David F. Montez
+- Benjamin P. Kay
+- Alexander S. Hatoum
+- Meghan Rose Donohue
+- William Foran
+- Ryland L. Miller
+- Timothy J. Hendrickson
+- Stephen M. Malone
+- Sridhar Kandala
+- Eric Feczko
+- Oscar Miranda-Dominguez
+- Alice M. Graham
+- Eric A. Earl
+- Anders J. Perrone
+- Michaela Cordova
+- Olivia Doyle
+- Lucille A. Moore
+- Gregory M. Conan
+- Johnny Uriarte
+- Kathy Snider
+- Benjamin J. Lynch
+- James C. Wilgenbusch
+- Thomas Pengo
+- Angela Tam
+- Jianzhong Chen
+- Dillan J. Newbold
+- Annie Zheng
+- Nicole A. Seider
+- Andrew N. Van
+- Athanasia Metoki
+- Roselyne J. Chauvin
+- Timothy O. Laumann
+- Deanna J. Greene
+- Steven E. Petersen
+- Hugh Garavan
+- Wesley K. Thompson
+- Thomas E. Nichols
+- B. T. Thomas Yeo
+- Deanna M. Barch
+- Beatriz Luna
+- Damien A. Fair
+- Nico U. F. Dosenbach
+date: '2022-03-01'
+publishDate: '2024-08-01T09:27:33.822524Z'
+publication_types:
+- article-journal
+publication: '*Nature*'
+doi: 10.1038/s41586-022-04492-9
+abstract: Magnetic resonance imaging (MRI) has transformed our understanding of the
+  human brain through well-replicated mapping of abilities to specific structures
+  (for example, lesion studies) and functions1–3 (for example, task functional MRI
+  (fMRI)). Mental health research and care have yet to realize similar advances from
+  MRI. A primary challenge has been replicating associations between inter-individual
+  differences in brain structure or function and complex cognitive or mental health
+  phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied
+  on sample sizes appropriate for classical brain mapping4 (the median neuroimaging
+  study sample size is about 25), but potentially too small for capturing reproducible
+  brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging
+  datasets currently available—with a total sample size of around 50,000 individuals—to
+  quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS
+  associations were smaller than previously thought, resulting in statistically underpowered
+  studies, inflated effect sizes and replication failures at typical sample sizes.
+  As sample sizes grew into the thousands, replication rates began to improve and
+  effect size inflation decreased. More robust BWAS effects were detected for functional
+  MRI (versus structural), cognitive tests (versus mental health questionnaires) and
+  multivariate methods (versus univariate). Smaller than expected brain–phenotype
+  associations and variability across population subsamples can explain widespread
+  BWAS replication failures. In contrast to non-BWAS approaches with larger effects
+  (for example, lesions, interventions and within-person), BWAS reproducibility requires
+  samples with thousands of individuals.
+tags:
+- Cognitive neuroscience
+- Psychology
+links:
+- name: URL
+  url: https://www.nature.com/articles/s41586-022-04492-9
+---