Understanding whether cognitive and perceptual states can be decoded from brain activity alone is a fundamental question in cognitive neuroscience. It is not only relevant for scientific theories of how information is encoded in the brain, but also has important practical and ethical implications.
Non-invasive techniques such as functional MRI (fMRI) can be used to record signals related to brain activity in humans from many locations in the brain simultaneously. However, many conventional approaches to analysing these data rely on considering signal changes at each location independently of all the other locations in the brain
These conventional approaches have proven successful in elucidating many aspects of the relationship between cognitive and mental states and brain activity. However, recent advances in data analysis procedures raise the possibility of deciphering additional and complementary information from neuroimaging data.
Recently, a powerful approach has emerged that applies pattern-recognition techniques to neuroimaging data. The new strategy is to decode a person's current mental state by learning to recognize characteristic spatial patterns of brain activity associated with different mental states. This takes into account not just activity at single locations but the full spatial pattern of activity. Such pattern-based decoding reveals that substantially more information is encoded in fMRI signals than was previously recognised.
These new approaches have a particular use in addressing the question of how information regarding perceptual and cognitive states is encoded in the human brain. Pattern-based decoding has now been successfully used to reveal the principles underlying the representation of objects in the ventral visual pathway. It can also reveal conscious and unconscious sensory representations of individual features, and can be used to track dynamic changes in the contents of consciousness over time.
Decoding approaches therefore provide a particularly sensitive way to determine what types of information are represented in the spatially distributed pattern of brain responses recorded with current neuroimaging techniques. However, for more general applications, important technical and methodological barriers remain to be overcome, including the ability of such approaches to generalize across individuals and different cognitive and perceptual states.
As these techniques have the possibility to reveal covert or unconscious mental states, they raise important ethical and privacy concerns. These can be addressed within existing ethical frameworks, but nevertheless necessitate careful and considered engagement by the neuroimaging community.
Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.
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This work was supported by the Wellcome Trust and the Mind–Science Foundation. We thank V. Lamme for bringing the reference to Nikola Tesla to our attention, and thank J. Driver, C. Frith and K.-E. Stephan for helpful comments on the manuscript.
The authors declare no competing financial interests.
- Multivariate analysis
An analytical technique that considers (or solves) multiple decision variables. In the present context, multivariate analysis takes into account patterns of information that might be present across multiple voxels measured by neuroimaging techniques.
- Univariate analysis
Univariate statistical analysis considers only single-decision variables at any one time. Conventional brain imaging data analyses are mass univariate in that they consider how responses vary at very many single voxels, but consider each individual voxel separately.
- Blood-oxygen-leveldependent (BOLD) signal
Functional MRI measures local changes in the proportion of oxygenated blood in the brain; the BOLD signal. This proportion changes in response to neural activity. Therefore, the BOLD signal, or haemodynamic response, indicates the location and magnitude of neural activity.
- Primary visual cortex
Considered to be the first visual cortical area in primates, and receives the majority of its input from the retina via the lateral geniculate nucleus.
A voxel is the three-dimensional (3D) equivalent of a pixel; a finite volume within 3D space. This corresponds to the smallest element measured in a 3D anatomical or functional brain image volume.
- Pattern vector
A vector is a set of one or more numerical elements. Here, a pattern vector is the set of values that together represent the value of each individual voxel in a particular spatial pattern.
- Orientation tuning
Many neurons in the mammalian early visual cortex evoke spikes at a greater rate when the animal is presented with visual stimuli of a particular orientation. The stimulus orientation that evokes the greatest firing rate for a particular cell is known as its preferred orientation, and the orientation tuning curve of a cell describes how that firing rate changes as the orientation of the stimulus is varied away from the preferred orientation.
- Spatial anisotropy
An anisotropic property is one where a measurement made in one direction differs from the measurement made in another direction. For example, the orientation tuning preferences of neurons in V1 change in a systematic but anisotropic way across the surface of the cortex.
(EEG). The continuously changing electrical signal recorded from the scalp in humans that reflects the summated postsynaptic potentials of cortical neurons in response to changing cognitive or perceptual states. The EEG can be measured with extremely high temporal resolution.
A non-invasive technique that allows the detection of the changing magnetic fields that are associated with brain activity on the timescale of milliseconds.
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Haynes, JD., Rees, G. Decoding mental states from brain activity in humans. Nat Rev Neurosci 7, 523–534 (2006). https://doi.org/10.1038/nrn1931
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