Here are a few recent and notable fMRI papers from our lab. More of our papers can be found by searching PubMed or Google Scholar. Public archives of some papers can be found at the open National Institutes of Health or the University of California repositories.

Decoding the semantic content of natural movies from human brain activity (Huth et al., Frontiers in Systems Neuroscience, 2016)

Decoding performanceSeveral recent neuroimaging studies have decoded the structure or semantic content of static visual images from human brain activity. This paper presents a decoding algorithm, hierarchical logistic regression (HLR), that makes it possible to decode detailed information about the object and action categories present in natural movies from human brain activity signals measured by functional MRI. The model decodes the present of many object and action categories from fMRI responses with a high degree of accuracy. This framework can also be used to test whether semantic relationships defined in the WordNet taxonomy are represented the same way in the human brain. Hierarchical relationships between general categories and atypical examples, such as organism and plant, did not seem to be reflected in brain representations measured by fMRI. Get the paper here.


NatureCoverNatural speech reveals the semantic maps that tile human cerebral cortex (Huth et al., Nature, 2016)

Comprehension of the meaning of narrative natural speech is a challenging computational problem but one that the human brain solves easily. This study shows how 985 distinct semantic concepts are mapped systematically across the surface of the cerebral cortex. Find more information about this study here.

Lescroart.2015Fourier power, subjective distance and semantic categories all provide plausible models of BOLD responses in scene-selective visual areas (Lescroart et al., Frontiers in Computational Neuroscience, 2015)

Perception of natural visual scenes activates several functional areas in the human brain, including the Parahippocampal Place Area (PPA), Retrosplenial Complex (RSC), and the Occipital Place Area (OPA). However, it is currently unclear what specific scene-related features are represented in these areas. We evaluated three qualitatively different classes of features: 2D Fourier power; 3D spatial features; and abstract categorical features. The response variance explained by these three models is largely shared, and the individual models explain little unique variance in responses. Unfortunately, this suggests that there is currently currently no good reason to favor any one of these models over any other.

naselaris.methodA voxel-wise encoding model for early visual areas decodes mental
images of remembered scenes
(Naselaris et al., NeuroImage, 2015)

Are low-level visual features encoded in activity generated during mental imagery of complex scenes? Voxel-wise encoding models were fit to fMRI signals elicited by works of art. Separately, activity was measured as subjects imagined previously memorized works of art. We show that mental images can be accurately identified using decoding models constructed from voxel-wise encoding models, and that accuracy of mental image identification depends upon the voxel tuning to low-level visual features. These results show that low-level visual features are encoded during mental imagery of complex scenes. We also provide a proof-of-concept demonstration of an internet image search guided by mental imagery.

Cukur.2013Attention during natural vision warps semantic representation across the human brain (Cukur et al., Journal of Neuroscience, 2013)

The human brain consists of hundreds of distinct functional areas, and each area represents different information about the external and internal world. This study shows that these representations are task-dependent. Find more information about this study here.

Stansbury.2013Natural scene statistics explain the representation of scene categories in human visual cortex (Stansbury et al., Neuron, 2013)

Much of human visual cortex appears to be selective for specific categories of natural scenes. However, it is unknown whether this scene selectivity is essentially arbitrary, or rather whether it reflects the statistical strucure of natural scenes. This paper uses a machine learning algorithm to discover the intrinsic categorical structure of natural scenes, and then uses fMRI to show that the human brain represents these natural categories. For further information watch a video summary provided by the first author, Dustin Stansbury.

Huth.2012A continuous semantic space describes the representation of thousands of object and action categories across the human brain (Huth et al., Neuron, 2012)

This study shows how 1,705 distinct object and action categories are mapped systematically across the surface of the cerebral cortex. Find more information about this study here.


Nishimoto.etal.2011.Reconstruction.SimpleReconstructing visual experiences from brain activity evoked by natural movies (Nishimoto et al., Current Biology 2011)

This study uses a novel motion-energy model of human visual cortex to reconstruct movies from brain activity. Find much more information about this study here.

System identification, encoding models and decoding models, a powerful new approach to fMRI (Gallant et al., Book Chapter, 2012)

Most fMRI experiments employ a deductive approach in which a specific hypothesis governs selection of a narrow range of stimulus and task parameters. This chapter explains an alternative approach called system identification (SI). The goal of SI is to construct an explicit encoding model for each voxel that predicts responses to any arbitrary input. This approach is much more efficient and much more powerful than the conventional approach.

Encoding and decoding in fMRI (Naselaris et al., NeuroImage, 2011)

This paper provides a general framework to understand the relationship between encoding models of the brain and brain decoding. It also explains in detail one one particularly powerful approach to brain decoding based on Bayesian decoding of voxel-wise models.

naselaris.structuralBayesian reconstruction of natural images from human brain activity (Naselaris et al., Neuron, 2009)

This paper presents the first successful approach for reconstructing natural images from human brain activity. It provides clear demonstrations of the application of Bayesian decoding of voxel-wise models, and it illustrates the importance of the prior.

Identifying natural images from human brain activitykay.nature (Kay et al., Nature, 2008)

Functional MRI measures hemodynamic signals that are indirectly coupled to neural activity. Thus, fMRI is an inherently limited method that cannot recover all of the information available in the brain. However, this landmark paper shows that far more information can be recovered from fMRI signals than had been believed previously.

Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fMRI (Kay et al., Human Brain Mapping, 2008)

Data pre-processing is a critical stage of any fMRI study, and the pre-processing pipeline can have a dramatic effect on the quality of the data. This technical paper focuses on one approach to pre-processing and modeling fMRI data that was used in our laboratory circa 2008. This paper is still valuable for its desciption of fMRI pre-processing problems, but many aspects of the pipeline itself have been superseded by later developments.

Topographic organization in and near human visual areas V4 (Hansen et al., Journal of Neuroscience, 2007)hansen

The human visual system consists of over three dozen distinct visual areas. Several early visual areas (V1, V2, V3, MT) appear to be functionally homologous between humans and non-human primates such as the macaque. However, there has been debate about one specific visual area, V4. V4 in primates appears to be organized into separate ventral and dorsal components. In contrast, fMRI studies in humans have argued that human V4 is organized differently, and that the ventral and dorsal halves of V4 are contiguous. This paper reports a systematic and detailed mapping study of area V4, including a crucial experiment on selective attention. We conclude that human V4 is organized analogously to V4 in non-human primates, with separate ventral and dorsal halves.