Information mapping with pattern classifiers: A comparative study

Pereira, Francisco; Botvinick, Matthew

doi:10.1016/j.neuroimage.2010.05.026

Cited by 128 publications

(122 citation statements)

References 22 publications

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Mentioning

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“…Likewise, L-HG and R-HG are primary auditory structures and thus are not expected to carry sustained planrelated predictive information. Null results should always be interpreted with caution in pattern classification [because they may reflect limitations in the classification algorithms rather than the data (Pereira and Botvinick, 2011)]; nevertheless, the absence of decoding during planning in these areas is certainly consistent with expectations.…”

Section: Resultssupporting

confidence: 55%

“…That is, voxels coding for one particular movement (reflected by the positive or negative direction of the weight) tended to lie adjacent to one another within the ROI, although these sub-ROI clusters were not necessarily consistent between comparisons. Although caution should be applied to interpreting the magnitude of the voxel weights assigned by any classifier (Pereira and Botvinick, 2011), this general result is to be expected based on the structure of the surrounding vasculature and spatial resolution of the BOLD response (Logothetis and Wandell, 2004), further reinforcing the notion that spatial voxel patterns directly reflect underlying physiological changes. Furthermore, and more generally, the findings from this voxel weight analysis are highly consistent with expectations from monkey neurophysiology.…”

Section: Resultsmentioning

confidence: 94%

“…To do this, for each iteration of the cross-validation procedure (1002 or 1026 iterations, depending on the number of runs per subject), a different SVM discriminant function was refined based on the subset of trials included for training. We calculated the voxel weights for each function and then averaged across all iterations to produce a set of mean voxel weights; this procedure was repeated for each pairwise comparison, ROI, and subject (note that the weight of each voxel provides a measure of its relationship with the class label as learned by the classifier; in this case, GT, GB, or touch planned actions) (for details, see Pereira and Botvinick, 2011). Both across and within subjects for each ROI and pairwise comparison, we found little structured relationship of voxel weights according to the action being planned.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Decoding Action Intentions from Preparatory Brain Activity in Human Parieto-Frontal Networks

Gallivan

McLean²,

Valyear³

et al. 2011

Journal of Neuroscience

237

217

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How and where in the human brain high-level sensorimotor processes such as intentions and decisions are coded remain important yet essentially unanswered questions. This is in part because, to date, decoding intended actions from brain signals has been primarily constrained to invasive neural recordings in nonhuman primates. Here we demonstrate using functional MRI (fMRI) pattern recognition techniques that we can also decode movement intentions from human brain signals, specifically object-directed grasp and reach movements, moments before their initiation. Subjects performed an event-related delayed movement task toward a single centrally located object (consisting of a small cube attached atop a larger cube). For each trial, after visual presentation of the object, one of three hand movements was instructed: grasp the top cube, grasp the bottom cube, or reach to touch the side of the object (without preshaping the hand). We found that, despite an absence of fMRI signal amplitude differences between the planned movements, the spatial activity patterns in multiple parietal and premotor brain areas accurately predicted upcoming grasp and reach movements. Furthermore, the patterns of activity in a subset of these areas additionally predicted which of the two cubes were to be grasped. These findings offer new insights into the detailed movement information contained in human preparatory brain activity and advance our present understanding of sensorimotor planning processes through a unique description of parieto-frontal regions according to the specific types of hand movements they can predict.

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Section: Resultssupporting

confidence: 55%

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Decoding Action Intentions from Preparatory Brain Activity in Human Parieto-Frontal Networks

Gallivan

McLean²,

Valyear³

et al. 2011

Journal of Neuroscience

237

217

View full text Add to dashboard Cite

show abstract

“…For each individual dataset, labels were shuffled 1,000 times across the training and test sets to create an empirical null distribution and classification was performed on the randomized data at the time point achieving the highest classification performance across subjects on the real data. For searchlight classification, p values were calculated for each subject and combined to achieve a group map quantifying the proportion of subjects achieving significance in each searchlight (Pereira, & Botvinickck, 2011). For all other analyses, randomization was performed within‐subject and empirical null distributions were calculated in an identical manner as the observed statistic (i.e., average accuracy over subjects).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

Dima

Perry

Messaritaki

et al. 2018

Human Brain Mapping

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Recognizing emotion in faces is important in human interaction and survival, yet existing studies do not paint a consistent picture of the neural representation supporting this task. To address this, we collected magnetoencephalography (MEG) data while participants passively viewed happy, angry and neutral faces. Using time‐resolved decoding of sensor‐level data, we show that responses to angry faces can be discriminated from happy and neutral faces as early as 90 ms after stimulus onset and only 10 ms later than faces can be discriminated from scrambled stimuli, even in the absence of differences in evoked responses. Time‐resolved relevance patterns in source space track expression‐related information from the visual cortex (100 ms) to higher‐level temporal and frontal areas (200–500 ms). Together, our results point to a system optimised for rapid processing of emotional faces and preferentially tuned to threat, consistent with the important evolutionary role that such a system must have played in the development of human social interactions.

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“…Data preprocessing and analysis were performed using the Searchmight Toolbox (35) for Matlab, AFNI functions (36), and custom scripts. Further methodological details are provided in Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

An architecture for encoding sentence meaning in left mid-superior temporal cortex

Frankland

Greene

2015

Proc. Natl. Acad. Sci. U.S.A.

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Human brains flexibly combine the meanings of words to compose structured thoughts. For example, by combining the meanings of “bite,” “dog,” and “man,” we can think about a dog biting a man, or a man biting a dog. Here, in two functional magnetic resonance imaging (fMRI) experiments using multivoxel pattern analysis (MVPA), we identify a region of left mid-superior temporal cortex (lmSTC) that flexibly encodes “who did what to whom” in visually presented sentences. We find that lmSTC represents the current values of abstract semantic variables (“Who did it?” and “To whom was it done?”) in distinct subregions. Experiment 1 first identifies a broad region of lmSTC whose activity patterns (i) facilitate decoding of structure-dependent sentence meaning (“Who did what to whom?”) and (ii) predict affect-related amygdala responses that depend on this information (e.g., “the baby kicked the grandfather” vs. “the grandfather kicked the baby”). Experiment 2 then identifies distinct, but neighboring, subregions of lmSTC whose activity patterns carry information about the identity of the current “agent” (“Who did it?”) and the current “patient” (“To whom was it done?”). These neighboring subregions lie along the upper bank of the superior temporal sulcus and the lateral bank of the superior temporal gyrus, respectively. At a high level, these regions may function like topographically defined data registers, encoding the fluctuating values of abstract semantic variables. This functional architecture, which in key respects resembles that of a classical computer, may play a critical role in enabling humans to flexibly generate complex thoughts.

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Information mapping with pattern classifiers: A comparative study

Cited by 128 publications

References 22 publications

Decoding Action Intentions from Preparatory Brain Activity in Human Parieto-Frontal Networks

Decoding Action Intentions from Preparatory Brain Activity in Human Parieto-Frontal Networks

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

An architecture for encoding sentence meaning in left mid-superior temporal cortex

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