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Reconstruction of Arm Movement Directions from Human Motor Cortex Using fMRI

Overview of attention for article published in Frontiers in Neuroscience, July 2017
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Title
Reconstruction of Arm Movement Directions from Human Motor Cortex Using fMRI
Published in
Frontiers in Neuroscience, July 2017
DOI 10.3389/fnins.2017.00434
Pubmed ID
Authors

Seungkyu Nam, Dae-Shik Kim

Abstract

Recent advances in functional magnetic resonance imaging (fMRI) have been used to reconstruct cognitive states based on brain activity evoked by sensory or cognitive stimuli. To date, such decoding paradigms were mostly used for visual modalities. On the other hand, reconstructing functional brain activity in motor areas was primarily achieved through more invasive electrophysiological techniques. Here, we investigated whether non-invasive fMRI responses from human motor cortex can also be used to predict individual arm movements. To this end, we conducted fMRI studies in which participants moved their arm from a center position to one of eight target directions. Our results suggest that arm movement directions can be distinguished from the multivoxel patterns of fMRI responses in motor cortex. Furthermore, compared to multivoxel pattern analysis, encoding models were able to also reconstruct unknown movement directions from the predicted brain activity. We conclude for our study that non-invasive fMRI signal can be utilized to predict directional motor movements in human motor cortex.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 35%
Student > Master 4 24%
Researcher 2 12%
Student > Doctoral Student 1 6%
Professor 1 6%
Other 0 0%
Unknown 3 18%
Readers by discipline Count As %
Neuroscience 4 24%
Computer Science 3 18%
Engineering 3 18%
Psychology 2 12%
Arts and Humanities 1 6%
Other 0 0%
Unknown 4 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 August 2017.
All research outputs
#14,393,794
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#5,642
of 11,542 outputs
Outputs of similar age
#156,512
of 327,247 outputs
Outputs of similar age from Frontiers in Neuroscience
#77
of 171 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,247 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 171 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.