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1. Optimizing multivariate pattern classification in rapid event-related designs

   https://doi.org/10.1016/j.jneumeth.2023.109808  

   Stehr, Daniel A.; Garcia, Javier O.; Pyles, John A.; Grossman, Emily D. (March 2023, Journal of Neuroscience Methods)

   Background: Multivariate pattern analysis (MVPA or pattern decoding) has attracted considerable attention as a sensitive analytic tool for investigations using functional magnetic resonance imaging (fMRI) data. With the introduction of MVPA, however, has come a proliferation of methodological choices confronting the researcher, with few studies to date offering guidance from the vantage point of controlled datasets detached from specific experimental hypotheses. New method: We investigated the impact of four data processing steps on support vector machine (SVM) classification performance aimed at maximizing information capture in the presence of common noise sources. The four techniques included: trial averaging (classifying on separate trial estimates versus condition-based averages), within-run mean centering (centering the data or not), method of cost selection (using a fixed or tuned cost value), and motion-related denoising approach (comparing no denoising versus a variety of nuisance regressions capturing motion-related reference signals). The impact of these approaches was evaluated on real fMRI data from two control ROIs, as well as on simulated pattern data constructed with carefully controlled voxel- and trial-level noise components. Results: We find significant improvements in classification performance across both real and simulated datasets with run-wise trial averaging and mean centering. When averaging trials within conditions of each run, we note a simultaneous increase in the between-subject variability of SVM classification accuracies which we attribute to the reduced size of the test set used to assess the classifier's prediction error. Therefore, we propose a hybrid technique whereby randomly sampled subsets of trials are averaged per run and demonstrate that it helps mitigate the tradeoff between improving signal-to-noise ratio by averaging and losing exemplars in the test set. Comparison with existing methods: Though a handful of empirical studies have employed run-based trial averaging, mean centering, or their combination, such studies have done so without theoretical justification or rigorous testing using control ROIs. Conclusions: Therefore, we intend this study to serve as a practical guide for researchers wishing to optimize pattern decoding without risk of introducing spurious results. 

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2. Distributed Neural Systems Support Flexible Attention Updating during Category Learning

   https://doi.org/10.1162/jocn_a_01882  

   Weichart, Emily R.; Evans, Daniel G.; Galdo, Matthew; Bahg, Giwon; Turner, Brandon M. (January 2022, Journal of Cognitive Neuroscience)

   Abstract To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems. 

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3. Mind Reader: Reconstructing complex images from brain activities

   Lin, Sikun; Sprague, Thomas; Singh, Ambuj K. (November 2022, Advances in neural information processing systems)

   Understanding how the brain encodes external stimuli and how these stimuli can be decoded from the measured brain activities are long-standing and challenging questions in neuroscience. In this paper, we focus on reconstructing the complex image stimuli from fMRI (functional magnetic resonance imaging) signals. Unlike previous works that reconstruct images with single objects or simple shapes, our work aims to reconstruct image stimuli that are rich in semantics, closer to everyday scenes, and can reveal more perspectives. However, data scarcity of fMRI datasets is the main obstacle to applying state-of-the-art deep learning models to this problem. We find that incorporating an additional text modality is beneficial for the reconstruction problem compared to directly translating brain signals to images. Therefore, the modalities involved in our method are: (i) voxel-level fMRI signals, (ii) observed images that trigger the brain signals, and (iii) textual description of the images. To further address data scarcity, we leverage an aligned vision-language latent space pre-trained on massive datasets. Instead of training models from scratch to find a latent space shared by the three modalities, we encode fMRI signals into this pre-aligned latent space. Then, conditioned on embeddings in this space, we reconstruct images with a generative model. The reconstructed images from our pipeline balance both naturalness and fidelity: they are photo-realistic and capture the ground truth image contents well. 

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4. An fMRI dataset in response to “The Grand Budapest Hotel”, a socially-rich, naturalistic movie

   https://doi.org/10.1038/s41597-020-00735-4  

   Visconti di Oleggio Castello, Matteo; Chauhan, Vassiki; Jiahui, Guo; Gobbini, M. Ida (December 2020, Scientific Data)

   null (Ed.)

   Abstract Naturalistic stimuli evoke strong, consistent, and information-rich patterns of brain activity, and engage large extents of the human brain. They allow researchers to compare highly similar brain responses across subjects, and to study how complex representations are encoded in brain activity. Here, we describe and share a dataset where 25 subjects watched part of the feature film “The Grand Budapest Hotel” by Wes Anderson. The movie has a large cast with many famous actors. Throughout the story, the camera shots highlight faces and expressions, which are fundamental to understand the complex narrative of the movie. This movie was chosen to sample brain activity specifically related to social interactions and face processing. This dataset provides researchers with fMRI data that can be used to explore social cognitive processes and face processing, adding to the existing neuroimaging datasets that sample brain activity with naturalistic movies. 

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5. Naturalistic stimuli reveal a critical period in visual cortex development: Evidence from adult-onset blindness

   https://doi.org/10.1101/2021.09.14.460305  

   Musz, E.; Loitile, R.; Chen, J.; Cusack, R.; Bedny, M. (January 2021, bioRxiv)

   How do life experiences impact cortical function? In people who are born blind, the “visual” cortices are recruited for nonvisual tasks such as Braille reading and sound localization (e.g., Collignon et al., 2011; Sadato et al., 1996). The mechanisms of this recruitment are not known. Do visual cortices have a latent capacity to respond to nonvisual information that is equal throughout the lifespan? Alternatively, is there a sensitive period of heightened plasticity that makes visual cortex repurposing possible during childhood? To gain insight into these questions, we leveraged naturalistic auditory stimuli to quantify and compare cross-modal responses congenitally blind (CB, n=22), adult-onset blind (vision loss >18 years-of-age, AB, n=14) and sighted (n=22) individuals. Participants listened to auditory excerpts from movies; a spoken narrative; and matched meaningless auditory stimuli (i.e., shuffled sentences, backwards speech) during fMRI scanning. These rich naturalistic stimuli made it possible to simultaneous engage a broad range of cognitive domains. We correlated the voxel-wise timecourses of different participants within each group. For all groups, all stimulus conditions induced synchrony in auditory cortex and for all groups only the narrative stimuli synchronized responses in higher-cognitive fronto-parietal and temporal regions. Inter-subject synchrony in visual cortices was high in the CB group for the movie and narrative stimuli but not for meaningless auditory controls. In contrast, visual cortex synchrony was equally low among AB and sighted blindfolded participants. Even many years of blindness in adulthood fail to enable responses to naturalistic auditory information in visual cortices of people who had sight as children. These findings suggest that cross-modal responses in visual cortex of people born blind reflect the plasticity of developing visual cortex during a sensitive period. 

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