diff --git a/annotated_bibliography/all_annot.txt b/annotated_bibliography/all_annot.txt deleted file mode 100644 index 55cf779..0000000 --- a/annotated_bibliography/all_annot.txt +++ /dev/null @@ -1,111 +0,0 @@ -1. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. *Science*, *303*(5664), 1634-1640. [`link`](https://doi.org/10.1126/science.1089506) *Original application of ISC analysis to naturalistic movie-watching fMRI data, demonstrating shared stimulus-evoked responses across subjects.* - -2. Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. *Social Cognitive and Affective Neuroscience*, *14*(6), 667-685. [`link`](https://doi.org/10.1093/scan/nsz037) *Recent tutorial article that reviews ISC analysis and shows different ways to compute statistics for ISC.* - -3. Hasson, U., Yang, E., Vallines, I., Heeger, D. J., & Rubin, N. (2008). A hierarchy of temporal receptive windows in human cortex. *Journal of Neuroscience*, *28*(10), 2539-2550. [`link`](https://doi.org/10.1523/JNEUROSCI.5487-07.2008) *ISC analysis is used to demonstrate that, under naturalistic conditions, brain areas integrate stimulus-related information at different timescales.* - -4. Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. *NeuroImage*, *142*, 248-259. [`link`](https://doi.org/10.1016/j.neuroimage.2016.05.023) *The first in a series of three papers that describe the statistical assessment of pairwise ISC.* -1. Chen, P. H. C., Chen, J., Yeshurun, Y., Hasson, U., Haxby, J., & Ramadge, P. J. (2015). A reduced-dimension fMRI shared response model. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), *Advances in Neural Information Processing Systems, vol. 28* (pp. 460-468). [`link`](https://papers.nips.cc/paper/5855-a-reduced-dimension-fmri-shared-response-model) *Introduces the SRM method of functional alignment with several performance benchmarks.* - -2. Haxby, J. V., Guntupalli, J. S., Nastase, S. A., & Feilong, M. (2020). Hyperalignment: modeling shared information encoded in idiosyncratic cortical topographies. *eLife*, *9*, e56601. [`link`](https://doi.org/10.7554/eLife.56601) *Recent review of hyperalignment and related functional alignment methods, including SRM.* - -3. Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. *Nature Neuroscience*, *20*(1), 115-125. [`link`](https://doi.org/10.1038/nn.4450) *SRM is used to discover the dimensionality of shared representations across subjects who are watching the same movie.* - -4. Nastase, S. A., Liu, Y. F., Hillman, H., Norman, K. A., & Hasson, U. (2020). Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space. *NeuroImage*, *217*, 116865. [`link`](https://doi.org/10.1016/j.neuroimage.2020.116865) *This paper demonstrates that applying SRM to functional connectivity data can yield a shared response space across disjoint datasets with different subjects and stimuli.* - -5. Anderson, M. J., Capota, M., Turek, J. S., Zhu, X., Willke, T. L., Wang, Y., Chen P.-H., Manning, J. R., Ramadge, P. J., & Norman, K. A. (2016). Enabling factor analysis on thousand-subject neuroimaging datasets. *2016 IEEE International Conference on Big Data, pages 1151–1160*. [`link`](http://ieeexplore.ieee.org/document/7840719/) *Describes BrainIAK’s implementation of SRM on high performance compute clusters. .* - -6. Turek, J. S., Willke, T. L., Chen, P.-H., & Ramadge, P. J. (2017). A semi-supervised method for multi-subject fMRI functional alignment. *2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1098–1102*. [`link`](https://ieeexplore.ieee.org/document/7952326) *Implements a semi-supervised version of SRM that exploits additional categorical measurements to improve on supervised analysis, such as image classification or scene recall.* - -7. Turek, J. S., Ellis, C. T., Skalaban, L. J., Willke, T. L., & Turk-Browne, N. B. (2018). Capturing shared and individual information in fMRI data. *2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), pages 826-830*. [`link`](https://ieeexplore.ieee.org/document/8462175) *Robust SRM helps in exploring subject populations with high variability, such as infants. It can account for idiosyncratic data and help to filter out outlying data.* - -8. Richard, H., Martin, L., Pinho, A. L., Pillow, J., & Thirion, B. (2019). Fast shared response model for fMRI data. [`link`](https://arxiv.org/abs/1909.12537). *A fast SRM algorithm that relies on an intermediate atlas-based representation. It provides a considerable speed-up in time and memory usage hence it allows easy and fast large-scale analysis of naturalistic stimulus fMRI data.* - -1. Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. *Cerebral Cortex*, 22(1), 158–165. [`link`](https://doi.org/10.1093/cercor/bhr099) *Describes successful decoding of cognitive tasks using the pattern of correlation between 90 functional regions of interest across the brain.* - -2. Turk-Browne, N. B. (2013). Functional interactions as big data in the human brain. *Science*, 342(6158), 580–584. [`link`](https://doi.org/10.1126/science.1238409) *Describes how complex biological systems, including neural processes, can be understood through the interactions of their component variables, such as the full correlation matrix in fMRI data.* - -3. Wang, Y., Anderson, M. J., Cohen, J. D., Heinecke, A., Li, K., Satish, N., Sundaram, N., Turk-Browne, N. B., & Willke, T. L. (2015). Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. SC ’15: *Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis*, 1–12. [`link`](https://doi.org/10.1145/2807591.2807631) *Describes optimizations made for Intel Xeon Phi Coprocessors to greatly enhance FCMA performance in cluster-based computing environments.* - -4. Wang, Y., Cohen, J. D., Li, K., & Turk-Browne, N. B. (2015). Full correlation matrix analysis (FCMA): An unbiased method for task-related functional connectivity. *Journal of Neuroscience Methods*, 251, 108–119. [`link`](https://doi.org/10.1016/j.jneumeth.2015.05.012) *Describes how FCMA can be used to discover regions that are involved in cognitive tasks by way of their functional connectivity.* -1. Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2016). A Bayesian method for reducing bias in neural representational similarity analysis. *Advances in Neural Information Processing Systems* (pp. 4951-4959). [link](https://proceedings.neurips.cc/paper/2016/hash/b06f50d1f89bd8b2a0fb771c1a69c2b0-Abstract.html) *This is the original BRSA paper, explaining the spurious similarity structure existing in traditional RSA and introducing a Bayesian approach to reduce bias* -2. Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2019). Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. *PLoS Computational Biology*, 15(5), e1006299. [link](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006299&rev=2) *This is am improvement of the original method by additionally modeling the spatial noise correlation and marginalizing voxel-wise noise parameters. The paper also introduces group BRSA and cross-validation score to compare an estimated model against a null model, and further extends the BRSA to task-signal decoding, using the estimated similarity structure as an empirical prior for estimating neural patterns.* -1. Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017). Discovering event structure in continuous narrative perception and memory. *Neuron*, 95(3), 709–721.e5. [`link`](https://doi.org/10.1016/j.neuron.2017.06.041) *Describes and validates the event segmentation method, and applies it to perception and recall data from multiple experiments.* - -2. Baldassano, C., Hasson, U., & Norman, K. A. (2018). Representation of real-world event schemas during narrative perception. *Journal of Neuroscience*, 38(45), 9689–9699. [`link`](https://doi.org/10.1523/JNEUROSCI.0251-18.2018) *Uses the event segmentation model to find common event structure among narratives with a shared schematic script.* - -3. Ben-Yakov, A., & Henson, R. N. (2018). The hippocampal film editor: sensitivity and specificity to event boundaries in continuous experience. *Journal of Neuroscience*, 38(47), 10057–10068. [`link`](https://doi.org/10.1523/JNEUROSCI.0524-18.2018) *Further studies the relationship between the event boundaries produced by the event segmentation model, human-annotated boundaries, and hippocampal responses.* - -4. Silva, M., Baldassano, C., & Fuentemilla, L. (2019). Rapid memory reactivation at movie event boundaries promotes episodic encoding. *Journal of Neuroscience*, 39(43), 8538–8548. [`link`](https://doi.org/10.1523/JNEUROSCI.0360-19.2019) *Applies the event segmentation model to EEG signals collected while subjects were watching a movie.* - -5. Antony, J. W., Hartshorne, T. H., Pomeroy, K., Gureckis, T. M., Hasson, U., McDougle, S. D., & Norman, K. A. (2020). Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing. *Neuron*. [`link`](https://doi.org/10.1016/j.neuron.2020.10.029) *Uses the event segmentation model to relate the number and timing of event boundaries in neural signals to the degree of surprise elicited in basketball games.* -1. Manning JR, Ranganath R, Norman KA, Blei DM (2014). Topographic Factor Analysis: a Bayesian model for inferring brain networks from neural data. *PLoS One*, 9(5): e94914. [link](https://doi.org/10.1371/journal.pone.0094914) *Describes a single-subject model (TFA) for inferring brain network hubs and applies it to a semantic decoding dataset.* - -2. Manning JR, Zhu X, Willke TL, Ranganath R, Stachenfeld K, Hasson U, Blei DM, Norman KA (2018). A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. *NeuroImage*, 180: 243-252. [link](https://www.sciencedirect.com/science/article/abs/pii/S1053811918300715) *Describes a multi-subject (hierarchical) model (HTFA) for inferring shared brain network hubs and applies it to a story listening and movie viewing dataset.* - -3. Owen LLW, Chang TH, Manning JR (2020). High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns. *bioRxiv.* [link](https://www.biorxiv.org/content/10.1101/763821v1.full.pdf) *Describes a model for inferring network dynamics from timeseries data and applies it to HTFA fits to a story listening dataset.* - -1. Brouwer, G.J., and Heeger, D.J. (2009). Decoding and reconstructing color from responses in human visual cortex. *Journal of Neuroscience* 29, 13992–14003. [`link`](https://doi.org/10.1523/JNEUROSCI.3577-09.2009) *Uses an inverted encoding model to reconstruct color in a continuous space, demonstrating how color is represented across a hierarchy of visual regions.* - -2. Naselaris, T., Kay, K. N., Nishimoto, S., & Gallant, J. L. (2011). Encoding and decoding in fMRI. *NeuroImage* 56(2), 400–410. [`link`](https://doi.org/10.1016/j.neuroimage.2010.07.073) *A review article distinguishing between the different uses of encoding and decoding approaches for fMRI.* - -3. Serences, J.T., and Saproo, S. (2012). Computational advances towards linking BOLD and behavior. *Neuropsychologia* 50, 435–446. [`link`](https://doi.org/10.1016/j.neuropsychologia.2011.07.013) *Describes the differences between encoding and decoding approaches and emphasizes how these approaches can test linking hypotheses between fMRI and behavior.* - -4. Sprague, T.C., Adam, K.C.S., Foster, J.J., Rahmati, M., Sutterer, D.W., and Vo, V.A. (2018). Inverted encoding models assay population-level stimulus representations, not single-unit neural tuning. *eNeuro* 5, 1–5. [`link`](https://doi.org/10.1523/ENEURO.0098-18.2018) *Argues that inverted encoding models are most useful when using population-level stimulus representations across experimental manipulations to pointedly test psychological theories.* - -5. Sprague, T.C., Boynton, G.M., and Serences, J.T. (2019). The importance of considering model choices when interpreting results in computational neuroimaging. *eNeuro* 6, 1–11. [`link`](https://doi.org/10.1523/ENEURO.0196-19.2019) *Describes the encoding model approach in the broader scope of computational models and acknowledges some important limitations.* - -1. Ellis, C. T., Baldassano, C., Schapiro, A. C., Cai, M. B., Cohen, J. D. (2020). Facilitating open-science with realistic fMRI simulation: validation and application. *PeerJ* 8:e8564 [`link`](https://peerj.com/articles/8564/) -*Describes and validates the fmrisim method. Applies it to a dataset to test alternative design parameters and evaluate how these parameters influence the effect size* - -2. Ellis, C. T., Lesnick, M., Henselman-Petrusek, G., Keller, B., & Cohen, J. D. (2019). Feasibility of topological data analysis for event-related fMRI, Network Neuroscience, 1-12 [`link`](https://www.mitpressjournals.org/doi/full/10.1162/netn_a_00095?mobileUi=0) -*Example of using fmrisim to evaluate the plausibility of an analysis procedure under different signal parameters and design constraints* - -3. Kumar, S., Ellis, C., O'Connell, T. P., Chun, M. M., & Turk-Browne, N. B. (in press). Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain. *PLoS Computational Biology* [`link`](https://www.biorxiv.org/content/10.1101/2020.04.20.052175v1) -*Example of using fmrisim to test different possible neural bases for an observed effect in real data* - -4. Welvaert, M., et al. (2011) neuRosim: An R package for generating fMRI data. *Journal of Statistical Software* 44, 1-18 [`link`](https://www.jstatsoft.org/article/view/v044i10) -*A package in R for simulating fMRI data that was an inspiration for fmrisim* -Shvartsman, M., Sundaram, N., Aoi, M., Charles, A., Willke, T. L., & Cohen, J. D. (2018). Matrix-normal models for fMRI analysis. *International Conference on Artificial Intelligence and Statistics, AISTATS 2018*, 1914–1923. Extended version available at [`link`](https://arxiv.org/abs/1711.03058) Describes how to formulate a number of common fMRI analysis methods available in `BrainIAK` as matrix-normal models, and shows some benefits of this formulation. - -Cai, M. B., Shvartsman, M., Wu, A., Zhang, H., & Zhu, X. (2020). Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. *Neuropsychologia*, 144, 1–23. [`link`](https://doi.org/10.1016/j.neuropsychologia.2020.107500) *Provides an alternate framing of the matrix normal model focusing on the modeling of structured residuals.* - - -Magnus, J. R., & Neudecker, H. (1988). Matrix differential calculus with applications in statistics and econometrics. This is the standard reference for matrix calculus. A summary of some important identities may also be found on Wikipedia at [`link`](https://en.wikipedia.org/wiki/Matrix_calculus#Identities_in_differential_form). - - -Katanoda, K., Matsuda, Y., & Sugishita, M. (2002). A spatio-temporal regression model for the analysis of functional MRI data. *NeuroImage*, 17(3), 1415–1428. [`link`](https://doi.org/10.1006/nimg.2002.1209) *Example of a regression model for fMRI with separable residuals.* - -Hartvig, N. V. (2002). A stochastic geometry model for functional magnetic resonance images. *Scandinavian Journal of Statistics*, 29(3), 333–353. [`link`](https://doi.org/10.1111/1467-9469.00294) *Example of a separable residual covariance to a spatial activation model for fMRI data.* - -Kia, S. M., Beckmann, C. F., & Marquand, A. F. (2018). Scalable multi-task gaussian process tensor regression for normative modeling of structured variation in neuroimaging data. Retrieved from [`link`](http://arxiv.org/abs/1808.00036) *Example of using tensor regression models for analyzing fMRI data.* -Mennen, A.C., Turk-Browne, N.B., Wallace, G., Seok, D., Jaganjac, A., Stock, J., deBettencourt, M.T., Cohen, J.D., Norman, K.A. & Sheline, Y.I. (2020). Cloud-based fMRI neurofeedback to reduce the negative attentional bias in depression: a proof-of-concept study. *Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.* [`link`](https://doi.org/10.1016/j.bpsc.2020.10.006) -*Describes the first implementation of the rt-cloud software framework in a closed-loop fMRI study that provided continuous neurofeedback to participants based on a multivariate pattern classifier.* - -Shvartsman, M., Sundaram, N., Aoi, M., Charles, A., Willke, T. L., & Cohen, J. D. (2018). Matrix-normal models for fMRI analysis. *International Conference on Artificial Intelligence and Statistics, AISTATS 2018*, 1914–1923. Extended version available at [`link`](https://arxiv.org/abs/1711.03058) *Describes how to formulate a number of common fMRI analysis methods available in `BrainIAK` as matrix-normal models, and shows some benefits of this formulation.* - -Cai, M. B., Shvartsman, M., Wu, A., Zhang, H., & Zhu, X. (2020). Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. *Neuropsychologia*, 144, 1–23. [`link`](https://doi.org/10.1016/j.neuropsychologia.2020.107500) *Provides an alternate framing of the matrix normal model focusing on the modeling of structured residuals.* - - -Magnus, J. R., & Neudecker, H. (1988). Matrix differential calculus with applications in statistics and econometrics. This is the standard reference for matrix calculus. *A summary of some important identities may also be found on Wikipedia at [`link`](https://en.wikipedia.org/wiki/Matrix_calculus#Identities_in_differential_form).* - - -Katanoda, K., Matsuda, Y., & Sugishita, M. (2002). A spatio-temporal regression model for the analysis of functional MRI data. *NeuroImage*, 17(3), 1415–1428. [`link`](https://doi.org/10.1006/nimg.2002.1209) *Example of a regression model for fMRI with separable residuals.* - -Hartvig, N. V. (2002). A stochastic geometry model for functional magnetic resonance images. *Scandinavian Journal of Statistics*, 29(3), 333–353. [`link`](https://doi.org/10.1111/1467-9469.00294) *Example of a separable residual covariance to a spatial activation model for fMRI data.* - -Kia, S. M., Beckmann, C. F., & Marquand, A. F. (2018). Scalable multi-task gaussian process tensor regression for normative modeling of structured variation in neuroimaging data. Retrieved from [`link`](http://arxiv.org/abs/1808.00036) *Example of using tensor regression models for analyzing fMRI data.* -Mennen, A.C., Turk-Browne, N.B., Wallace, G., Seok, D., Jaganjac, A., Stock, J., deBettencourt, M.T., Cohen, J.D., Norman, K.A. & Sheline, Y.I. (2020). Cloud-based fMRI neurofeedback to reduce the negative attentional bias in depression: a proof-of-concept study. *Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.* [`link`](https://doi.org/10.1016/j.bpsc.2020.10.006) -*Describes the first implementation of the rt-cloud software framework in a closed-loop fMRI study that provided continuous neurofeedback to participants based on a multivariate pattern classifier.* - -1. Shvartsman, M., Sundaram, N., Aoi, M., Charles, A., Willke, T. L., & Cohen, J. D. (2018). Matrix-normal models for fMRI analysis. *International Conference on Artificial Intelligence and Statistics, AISTATS 2018*, 1914–1923. Extended version available at [`link`](https://arxiv.org/abs/1711.03058) *Describes how to formulate a number of common fMRI analysis methods available in `BrainIAK` as matrix-normal models, and shows some benefits of this formulation.* - -2. Cai, M. B., Shvartsman, M., Wu, A., Zhang, H., & Zhu, X. (2020). Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. *Neuropsychologia*, 144, 1–23. [`link`](https://doi.org/10.1016/j.neuropsychologia.2020.107500) *Provides an alternate framing of the matrix normal model focusing on the modeling of structured residuals.* - -3. Magnus, J. R., & Neudecker, H. (1988). Matrix differential calculus with applications in statistics and econometrics. This is the standard reference for matrix calculus. *A summary of some important identities may also be found on Wikipedia at [`link`](https://en.wikipedia.org/wiki/Matrix_calculus#Identities_in_differential_form).* - -4. Katanoda, K., Matsuda, Y., & Sugishita, M. (2002). A spatio-temporal regression model for the analysis of functional MRI data. *NeuroImage*, 17(3), 1415–1428. [`link`](https://doi.org/10.1006/nimg.2002.1209) *Example of a regression model for fMRI with separable residuals.* - -5. Hartvig, N. V. (2002). A stochastic geometry model for functional magnetic resonance images. *Scandinavian Journal of Statistics*, 29(3), 333–353. [`link`](https://doi.org/10.1111/1467-9469.00294) *Example of a separable residual covariance to a spatial activation model for fMRI data.* - -6. Kia, S. M., Beckmann, C. F., & Marquand, A. F. (2018). Scalable multi-task gaussian process tensor regression for normative modeling of structured variation in neuroimaging data. Retrieved from [`link`](http://arxiv.org/abs/1808.00036) *Example of using tensor regression models for analyzing fMRI data.* diff --git a/annotated_bibliography/brsa.md b/annotated_bibliography/brsa.md deleted file mode 100644 index 58f90b8..0000000 --- a/annotated_bibliography/brsa.md +++ /dev/null @@ -1,3 +0,0 @@ -1. Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2016). A Bayesian method for reducing bias in neural representational similarity analysis. *Advances in Neural Information Processing Systems* (pp. 4951-4959). [link](https://proceedings.neurips.cc/paper/2016/hash/b06f50d1f89bd8b2a0fb771c1a69c2b0-Abstract.html) *Describes potential biases in computing RSA that could lead to spurious results and introduces a Bayesian approach to reduce bias.* - -2. Cai, M. B., Schuck, N. W., Pillow, J. W., & Niv, Y. (2019). Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. *PLoS Computational Biology*, 15(5), e1006299. [link](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006299&rev=2) *This paper improves the earlier version of BRSA by additionally modeling the spatial noise correlation and marginalizing voxel-wise noise parameters. The paper also introduces group BRSA and the use of cross-validation to compare an estimated model against a null model, and further extends BRSA to task-signal decoding, using the estimated similarity structure as an empirical prior for estimating neural patterns.* diff --git a/annotated_bibliography/eventseg.md b/annotated_bibliography/eventseg.md deleted file mode 100644 index d39eac6..0000000 --- a/annotated_bibliography/eventseg.md +++ /dev/null @@ -1,9 +0,0 @@ -1. Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017). Discovering event structure in continuous narrative perception and memory. *Neuron*, 95(3), 709–721.e5. [`link`](https://doi.org/10.1016/j.neuron.2017.06.041) *Describes and validates the event segmentation method, and applies it to perception and recall data from multiple experiments.* - -2. Baldassano, C., Hasson, U., & Norman, K. A. (2018). Representation of real-world event schemas during narrative perception. *Journal of Neuroscience*, 38(45), 9689–9699. [`link`](https://doi.org/10.1523/JNEUROSCI.0251-18.2018) *Uses the event segmentation model to find common event structure among narratives with a shared schematic script.* - -3. Ben-Yakov, A., & Henson, R. N. (2018). The hippocampal film editor: sensitivity and specificity to event boundaries in continuous experience. *Journal of Neuroscience*, 38(47), 10057–10068. [`link`](https://doi.org/10.1523/JNEUROSCI.0524-18.2018) *Further studies the relationship between the event boundaries produced by the event segmentation model, human-annotated boundaries, and hippocampal responses.* - -4. Silva, M., Baldassano, C., & Fuentemilla, L. (2019). Rapid memory reactivation at movie event boundaries promotes episodic encoding. *Journal of Neuroscience*, 39(43), 8538–8548. [`link`](https://doi.org/10.1523/JNEUROSCI.0360-19.2019) *Applies the event segmentation model to EEG signals collected while subjects were watching a movie.* - -5. Antony, J. W., Hartshorne, T. H., Pomeroy, K., Gureckis, T. M., Hasson, U., McDougle, S. D., & Norman, K. A. (2020). Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing. *Neuron*. [`link`](https://doi.org/10.1016/j.neuron.2020.10.029) *Uses the event segmentation model to relate the number and timing of event boundaries in neural signals to the degree of surprise elicited in basketball games.* diff --git a/annotated_bibliography/fcma.md b/annotated_bibliography/fcma.md deleted file mode 100644 index c108f97..0000000 --- a/annotated_bibliography/fcma.md +++ /dev/null @@ -1,7 +0,0 @@ -1. Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. *Cerebral Cortex*, 22(1), 158–165. [`link`](https://doi.org/10.1093/cercor/bhr099) *Describes successful decoding of cognitive tasks using the pattern of correlation between 90 functional regions of interest across the brain.* - -2. Turk-Browne, N. B. (2013). Functional interactions as big data in the human brain. *Science*, 342(6158), 580–584. [`link`](https://doi.org/10.1126/science.1238409) *Describes how complex biological systems, including neural processes, can be understood through the interactions of their component variables, such as the full correlation matrix in fMRI data.* - -3. Wang, Y., Anderson, M. J., Cohen, J. D., Heinecke, A., Li, K., Satish, N., Sundaram, N., Turk-Browne, N. B., & Willke, T. L. (2015). Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. SC ’15: *Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis*, 1–12. [`link`](https://doi.org/10.1145/2807591.2807631) *Describes optimizations made for Intel Xeon Phi Coprocessors to greatly enhance FCMA performance in cluster-based computing environments.* - -4. Wang, Y., Cohen, J. D., Li, K., & Turk-Browne, N. B. (2015). Full correlation matrix analysis (FCMA): An unbiased method for task-related functional connectivity. *Journal of Neuroscience Methods*, 251, 108–119. [`link`](https://doi.org/10.1016/j.jneumeth.2015.05.012) *Describes how FCMA can be used to discover regions that are involved in cognitive tasks by way of their functional connectivity.* diff --git a/annotated_bibliography/fmrisim.md b/annotated_bibliography/fmrisim.md deleted file mode 100644 index f3e6987..0000000 --- a/annotated_bibliography/fmrisim.md +++ /dev/null @@ -1,11 +0,0 @@ -1. Ellis, C. T., Baldassano, C., Schapiro, A. C., Cai, M. B., Cohen, J. D. (2020). Facilitating open-science with realistic fMRI simulation: validation and application. *PeerJ* 8:e8564 [`link`](https://peerj.com/articles/8564/) -*Describes and validates the fmrisim method. Applies it to a dataset to test alternative design parameters and evaluate how these parameters influence the effect size* - -2. Ellis, C. T., Lesnick, M., Henselman-Petrusek, G., Keller, B., & Cohen, J. D. (2019). Feasibility of topological data analysis for event-related fMRI, Network Neuroscience, 1-12 [`link`](https://www.mitpressjournals.org/doi/full/10.1162/netn_a_00095?mobileUi=0) -*Example of using fmrisim to evaluate the plausibility of an analysis procedure under different signal parameters and design constraints* - -3. Kumar, S., Ellis, C., O'Connell, T. P., Chun, M. M., & Turk-Browne, N. B. (in press). Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain. *PLoS Computational Biology* [`link`](https://www.biorxiv.org/content/10.1101/2020.04.20.052175v1) -*Example of using fmrisim to test different possible neural bases for an observed effect in real data* - -4. Welvaert, M., et al. (2011) neuRosim: An R package for generating fMRI data. *Journal of Statistical Software* 44, 1-18 [`link`](https://www.jstatsoft.org/article/view/v044i10) -*A package in R for simulating fMRI data that was an inspiration for fmrisim* diff --git a/annotated_bibliography/htfa.md b/annotated_bibliography/htfa.md deleted file mode 100644 index eaf4ffd..0000000 --- a/annotated_bibliography/htfa.md +++ /dev/null @@ -1,6 +0,0 @@ -1. Manning JR, Ranganath R, Norman KA, Blei DM (2014). Topographic Factor Analysis: a Bayesian model for inferring brain networks from neural data. *PLoS One*, 9(5): e94914. [link](https://doi.org/10.1371/journal.pone.0094914) *Describes a single-subject model (TFA) for inferring brain network hubs and applies it to a semantic decoding dataset.* - -2. Manning JR, Zhu X, Willke TL, Ranganath R, Stachenfeld K, Hasson U, Blei DM, Norman KA (2018). A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. *NeuroImage*, 180: 243-252. [link](https://www.sciencedirect.com/science/article/abs/pii/S1053811918300715) *Describes a multi-subject (hierarchical) model (HTFA) for inferring shared brain network hubs and applies it to a story listening and movie viewing dataset.* - -3. Owen LLW, Chang TH, Manning JR (2020). High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns. *bioRxiv.* [link](https://www.biorxiv.org/content/10.1101/763821v1.full.pdf) *Describes a model for inferring network dynamics from timeseries data and applies it to HTFA fits to a story listening dataset.* - diff --git a/annotated_bibliography/iem.md b/annotated_bibliography/iem.md deleted file mode 100644 index f756e29..0000000 --- a/annotated_bibliography/iem.md +++ /dev/null @@ -1,10 +0,0 @@ -1. Brouwer, G.J., and Heeger, D.J. (2009). Decoding and reconstructing color from responses in human visual cortex. *Journal of Neuroscience* 29, 13992–14003. [`link`](https://doi.org/10.1523/JNEUROSCI.3577-09.2009) *Uses an inverted encoding model to reconstruct color in a continuous space, demonstrating how color is represented across a hierarchy of visual regions.* - -2. Naselaris, T., Kay, K. N., Nishimoto, S., & Gallant, J. L. (2011). Encoding and decoding in fMRI. *NeuroImage* 56(2), 400–410. [`link`](https://doi.org/10.1016/j.neuroimage.2010.07.073) *A review article distinguishing between the different uses of encoding and decoding approaches for fMRI.* - -3. Serences, J.T., and Saproo, S. (2012). Computational advances towards linking BOLD and behavior. *Neuropsychologia* 50, 435–446. [`link`](https://doi.org/10.1016/j.neuropsychologia.2011.07.013) *Describes the differences between encoding and decoding approaches and emphasizes how these approaches can test linking hypotheses between fMRI and behavior.* - -4. Sprague, T.C., Adam, K.C.S., Foster, J.J., Rahmati, M., Sutterer, D.W., and Vo, V.A. (2018). Inverted encoding models assay population-level stimulus representations, not single-unit neural tuning. *eNeuro* 5, 1–5. [`link`](https://doi.org/10.1523/ENEURO.0098-18.2018) *Argues that inverted encoding models are most useful when using population-level stimulus representations across experimental manipulations to pointedly test psychological theories.* - -5. Sprague, T.C., Boynton, G.M., and Serences, J.T. (2019). The importance of considering model choices when interpreting results in computational neuroimaging. *eNeuro* 6, 1–11. [`link`](https://doi.org/10.1523/ENEURO.0196-19.2019) *Describes the encoding model approach in the broader scope of computational models and acknowledges some important limitations.* - diff --git a/annotated_bibliography/isc.md b/annotated_bibliography/isc.md deleted file mode 100644 index 7a3b791..0000000 --- a/annotated_bibliography/isc.md +++ /dev/null @@ -1,7 +0,0 @@ -1. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. *Science*, *303*(5664), 1634-1640. [`link`](https://doi.org/10.1126/science.1089506) *Original application of ISC analysis to naturalistic movie-watching fMRI data, demonstrating shared stimulus-evoked responses across subjects.* - -2. Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. *Social Cognitive and Affective Neuroscience*, *14*(6), 667-685. [`link`](https://doi.org/10.1093/scan/nsz037) *Recent tutorial article that reviews ISC analysis and shows different ways to compute statistics for ISC.* - -3. Hasson, U., Yang, E., Vallines, I., Heeger, D. J., & Rubin, N. (2008). A hierarchy of temporal receptive windows in human cortex. *Journal of Neuroscience*, *28*(10), 2539-2550. [`link`](https://doi.org/10.1523/JNEUROSCI.5487-07.2008) *ISC analysis is used to demonstrate that, under naturalistic conditions, brain areas integrate stimulus-related information at different timescales.* - -4. Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. *NeuroImage*, *142*, 248-259. [`link`](https://doi.org/10.1016/j.neuroimage.2016.05.023) *The first in a series of three papers that describe the statistical assessment of pairwise ISC.* diff --git a/annotated_bibliography/matnormal.md b/annotated_bibliography/matnormal.md deleted file mode 100644 index 27ea014..0000000 --- a/annotated_bibliography/matnormal.md +++ /dev/null @@ -1,11 +0,0 @@ -1. Shvartsman, M., Sundaram, N., Aoi, M., Charles, A., Willke, T. L., & Cohen, J. D. (2018). Matrix-normal models for fMRI analysis. *International Conference on Artificial Intelligence and Statistics, AISTATS 2018*, 1914–1923. Extended version available at [`link`](https://arxiv.org/abs/1711.03058) *Describes how to formulate a number of common fMRI analysis methods available in `BrainIAK` as matrix-normal models, and shows some benefits of this formulation.* - -2. Cai, M. B., Shvartsman, M., Wu, A., Zhang, H., & Zhu, X. (2020). Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. *Neuropsychologia*, 144, 1–23. [`link`](https://doi.org/10.1016/j.neuropsychologia.2020.107500) *Provides an alternate framing of the matrix normal model focusing on the modeling of structured residuals.* - -3. Magnus, J. R., & Neudecker, H. (1988). Matrix differential calculus with applications in statistics and econometrics. This is the standard reference for matrix calculus. *A summary of some important identities may also be found on Wikipedia at [`link`](https://en.wikipedia.org/wiki/Matrix_calculus#Identities_in_differential_form).* - -4. Katanoda, K., Matsuda, Y., & Sugishita, M. (2002). A spatio-temporal regression model for the analysis of functional MRI data. *NeuroImage*, 17(3), 1415–1428. [`link`](https://doi.org/10.1006/nimg.2002.1209) *Example of a regression model for fMRI with separable residuals.* - -5. Hartvig, N. V. (2002). A stochastic geometry model for functional magnetic resonance images. *Scandinavian Journal of Statistics*, 29(3), 333–353. [`link`](https://doi.org/10.1111/1467-9469.00294) *Example of a separable residual covariance to a spatial activation model for fMRI data.* - -6. Kia, S. M., Beckmann, C. F., & Marquand, A. F. (2018). Scalable multi-task gaussian process tensor regression for normative modeling of structured variation in neuroimaging data. Retrieved from [`link`](http://arxiv.org/abs/1808.00036) *Example of using tensor regression models for analyzing fMRI data.* diff --git a/annotated_bibliography/real-time.md b/annotated_bibliography/real-time.md deleted file mode 100644 index f338c4b..0000000 --- a/annotated_bibliography/real-time.md +++ /dev/null @@ -1,3 +0,0 @@ -Mennen, A.C., Turk-Browne, N.B., Wallace, G., Seok, D., Jaganjac, A., Stock, J., deBettencourt, M.T., Cohen, J.D., Norman, K.A. & Sheline, Y.I. (2020). Cloud-based fMRI neurofeedback to reduce the negative attentional bias in depression: a proof-of-concept study. *Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.* [`link`](https://doi.org/10.1016/j.bpsc.2020.10.006) -*Describes the first implementation of the rt-cloud software framework in a closed-loop fMRI study that provided continuous neurofeedback to participants based on a multivariate pattern classifier.* - diff --git a/annotated_bibliography/srm.md b/annotated_bibliography/srm.md deleted file mode 100644 index 1f6a731..0000000 --- a/annotated_bibliography/srm.md +++ /dev/null @@ -1,16 +0,0 @@ -1. Chen, P. H. C., Chen, J., Yeshurun, Y., Hasson, U., Haxby, J., & Ramadge, P. J. (2015). A reduced-dimension fMRI shared response model. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), *Advances in Neural Information Processing Systems, vol. 28* (pp. 460-468). [`link`](https://papers.nips.cc/paper/5855-a-reduced-dimension-fmri-shared-response-model) *Introduces the SRM method of functional alignment with several performance benchmarks.* - -2. Haxby, J. V., Guntupalli, J. S., Nastase, S. A., & Feilong, M. (2020). Hyperalignment: modeling shared information encoded in idiosyncratic cortical topographies. *eLife*, *9*, e56601. [`link`](https://doi.org/10.7554/eLife.56601) *Recent review of hyperalignment and related functional alignment methods, including SRM.* - -3. Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. *Nature Neuroscience*, *20*(1), 115-125. [`link`](https://doi.org/10.1038/nn.4450) *SRM is used to discover the dimensionality of shared representations across subjects who are watching the same movie.* - -4. Nastase, S. A., Liu, Y. F., Hillman, H., Norman, K. A., & Hasson, U. (2020). Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space. *NeuroImage*, *217*, 116865. [`link`](https://doi.org/10.1016/j.neuroimage.2020.116865) *This paper demonstrates that applying SRM to functional connectivity data can yield a shared response space across disjoint datasets with different subjects and stimuli.* - -5. Anderson, M. J., Capota, M., Turek, J. S., Zhu, X., Willke, T. L., Wang, Y., Chen P.-H., Manning, J. R., Ramadge, P. J., & Norman, K. A. (2016). Enabling factor analysis on thousand-subject neuroimaging datasets. *2016 IEEE International Conference on Big Data, pages 1151–1160*. [`link`](http://ieeexplore.ieee.org/document/7840719/) *Describes BrainIAK’s implementation of SRM on high performance compute clusters. .* - -6. Turek, J. S., Willke, T. L., Chen, P.-H., & Ramadge, P. J. (2017). A semi-supervised method for multi-subject fMRI functional alignment. *2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1098–1102*. [`link`](https://ieeexplore.ieee.org/document/7952326) *Implements a semi-supervised version of SRM that exploits additional categorical measurements to improve on supervised analysis, such as image classification or scene recall.* - -7. Turek, J. S., Ellis, C. T., Skalaban, L. J., Willke, T. L., & Turk-Browne, N. B. (2018). Capturing shared and individual information in fMRI data. *2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), pages 826-830*. [`link`](https://ieeexplore.ieee.org/document/8462175) *Robust SRM helps in exploring subject populations with high variability, such as infants. It can account for idiosyncratic data and help to filter out outlying data.* - -8. Richard, H., Martin, L., Pinho, A. L., Pillow, J., & Thirion, B. (2019). Fast shared response model for fMRI data. [`link`](https://arxiv.org/abs/1909.12537). *A fast SRM algorithm that relies on an intermediate atlas-based representation. It provides a considerable speed-up in time and memory usage hence it allows easy and fast large-scale analysis of naturalistic stimulus fMRI data.* -