Modulating human brain responses via optimal natural image selection and synthetic image generation

Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy. Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images. In our second experiment, we found that synthetic images derived using a personalized encoding model elicited higher responses compared to synthetic images from group-level or other subjects' encoding models. The finding of aTLfaces favoring synthetic images than natural images was also replicated. Our results indicate the possibility of using data-driven and generative approaches to modulate macro-scale brain region responses and probe inter-individual differences in and functional specialization of the human visual system

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing.

Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. Effect size boosting occurs via reduction of non-BOLD noise at the subject-level and consequent reductions in between-subject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICA-related effect size enhancements enable much higher-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could mitigate some issues regarding statistical power in fMRI studies and enable novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.This work was supported by a Wellcome Trust project grant to SB-C and ETB. MVL was supported by the Wellcome Trust and fellowships from Jesus College, Cambridge and the British Academy. PK was supported by the National Institutes of Health–Cambridge Scholars Program. ETB is employed half-time by the University of Cambridge and halftime by GlaxoSmithKline (GSK).This is the author accepted manuscript. It first appeared from Elseiver at http://dx.doi.org/10.1016/j.neuroimage.2016.07.022

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

<div><p>The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum <i>a posteriori</i> estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).</p></div

Homology and Specificity of Natural Sound-Encoding in Human and Monkey Auditory Cortex

Understanding homologies and differences in auditory cortical processing in human and nonhuman primates is an essential step in elucidating the neurobiology of speech and language. Using fMRI responses to natural sounds, we investigated the representation of multiple acoustic features in auditory cortex of awake macaques and humans. Comparative analyses revealed homologous large-scale topographies not only for frequency but also for temporal and spectral modulations. In both species, posterior regions preferably encoded relatively fast temporal and coarse spectral information, whereas anterior regions encoded slow temporal and fine spectral modulations. Conversely, we observed a striking interspecies difference in cortical sensitivity to temporal modulations: While decoding from macaque auditory cortex was most accurate at fast rates (> 30 Hz), humans had highest sensitivity to ~3 Hz, a relevant rate for speech analysis. These findings suggest that characteristic tuning of human auditory cortex to slow temporal modulations is unique and may have emerged as a critical step in the evolution of speech and language

An object's smell in the multisensory brain : how our senses interact during olfactory object processing

Object perception is a remarkable and fundamental cognitive ability that allows us to interpret and interact with the world we are living in. In our everyday life, we constantly perceive objects–mostly without being aware of it and through several senses at the same time. Although it might seem that object perception is accomplished without any effort, the underlying neural mechanisms are anything but simple. How we perceive objects in the world surrounding us is the result of a complex interplay of our senses. The aim of the present thesis was to explore, by means of functional magnetic resonance imaging, how our senses interact when we perceive an object’s smell in a multisensory setting where the amount of sensory stimulation increases, as well as in a unisensory setting where we perceive an object’s smell in isolation. In Study I, we sought to determine whether and how multisensory object information influences the processing of olfactory object information in the posterior piriform cortex (PPC), a region linked to olfactory object encoding. In Study II, we then expanded our search for integration effects during multisensory object perception to the whole brain because previous research has demonstrated that multisensory integration is accomplished by a network of early sensory cortices and higher-order multisensory integration sites. We specifically aimed at determining whether there exist cortical regions that process multisensory object information independent of from which senses and from how many senses the information arises. In Study III, we then sought to unveil how our senses interact during olfactory object perception in a unisensory setting. Other previous studies have shown that even in such unisensory settings, olfactory object processing is not exclusively accomplished by regions within the olfactory system but instead engages a more widespread network of brain regions, such as regions belonging to the visual system. We aimed at determining what this visual engagement represents. That is, whether areas of the brain that are principally concerned with processing visual object information also hold neural representations of olfactory object information, and if so, whether these representations are similar for smells and pictures of the same objects. In Study I we demonstrated that assisting inputs from our senses of vision and hearing increase the processing of olfactory object information in the PPC, and that the more assisting input we receive the more the processing is enhanced. As this enhancement occurred only for matching inputs, it likely reflects integration of multisensory object information. Study II provided evidence for convergence of multisensory object information in form of a non-linear response enhancement in the inferior parietal cortex: activation increased for bimodal compared to unimodal stimulation, and increased even further for trimodal compared to bimodal stimulation. As this multisensory response enhancement occurred independent of the congruency of the incoming signals, it likely reflects a process of relating the incoming sensory information streams to each other. Finally, Study III revealed that regions of the ventral visual object stream are engaged in recognition of an object’s smell and represent olfactory object information in form of distinct neural activation patterns. While the visual system encodes information about both visual and olfactory objects, it appears to keep information from the two sensory modalities separate by representing smells and pictures of objects differently. Taken together, the studies included in this thesis reveal that olfactory object perception is a multisensory process that engages a widespread network of early sensory as well higher-order cortical regions, even if we do not encounter ourselves in a multisensory setting but exclusively perceive an object’s smell

Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior

Representational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational (dis-)similarity matrices (RDM or RSM), which characterize the pairwise (dis-)similarities of all conditions across all features (e.g. fMRI voxels or units of a model). However, classical RSA treats each feature as equally important. This 'equal weights' assumption contrasts with the flexibility of multivariate decoding, which reweights individual features for predicting a target variable. As a consequence, classical RSA may lead researchers to underestimate the correspondence between a model and a brain region and, in case of model comparison, may lead them to select an inferior model. The aim of this work is twofold: First, we sought to broadly test feature-reweighted RSA (FR-RSA) applied to computational models and reveal the extent to which reweighting model features improves RSM correspondence and affects model selection. Previous work suggested that reweighting can improve model selection in RSA but it has remained unclear to what extent these results generalize across datasets and data modalities. To draw more general conclusions, we utilized a range of publicly available datasets and three popular deep neural networks (DNNs). Second, we propose voxel-reweighted RSA, a novel use case of FR-RSA that reweights fMRI voxels, mirroring the rationale of multivariate decoding of optimally combining voxel activity patterns. We found that reweighting individual model units markedly improved the fit between model RSMs and target RSMs derived from several fMRI and behavioral datasets and affected model selection, highlighting the importance of considering FR-RSA. For voxel-reweighted RSA, improvements in RSM correspondence were even more pronounced, demonstrating the utility of this novel approach. We additionally show that classical noise ceilings can be exceeded when FR-RSA is applied and propose an updated approach for their computation. Taken together, our results broadly validate the use of FR-RSA for improving the fit between computational models, brain, and behavioral data, possibly allowing us to better adjudicate between competing computational models. Further, our results suggest that FR-RSA applied to brain measurement channels could become an important new method to assess the correspondence between representational spaces