Literature DB >> 23637162

Feature-specific information processing precedes concerted activation in human visual cortex.

Pavan Ramkumar1, Mainak Jas, Sebastian Pannasch, Riitta Hari, Lauri Parkkonen.   

Abstract

Current knowledge about the precise timing of visual input to the cortex relies largely on spike timings in monkeys and evoked-response latencies in humans. However, quantifying the activation onset does not unambiguously describe the timing of stimulus-feature-specific information processing. Here, we investigated the information content of the early human visual cortical activity by decoding low-level visual features from single-trial magnetoencephalographic (MEG) responses. MEG was measured from nine healthy subjects as they viewed annular sinusoidal gratings (spanning the visual field from 2 to 10° for a duration of 1 s), characterized by spatial frequency (0.33 cycles/degree or 1.33 cycles/degree) and orientation (45° or 135°); gratings were either static or rotated clockwise or anticlockwise from 0 to 180°. Time-resolved classifiers using a 20 ms moving window exceeded chance level at 51 ms (the later edge of the window) for spatial frequency, 65 ms for orientation, and 98 ms for rotation direction. Decoding accuracies of spatial frequency and orientation peaked at 70 and 90 ms, respectively, coinciding with the peaks of the onset evoked responses. Within-subject time-insensitive pattern classifiers decoded spatial frequency and orientation simultaneously (mean accuracy 64%, chance 25%) and rotation direction (mean 82%, chance 50%). Classifiers trained on data from other subjects decoded the spatial frequency (73%), but not the orientation, nor the rotation direction. Our results indicate that unaveraged brain responses contain decodable information about low-level visual features already at the time of the earliest cortical evoked responses, and that representations of spatial frequency are highly robust across individuals.

Entities:  

Mesh:

Year:  2013        PMID: 23637162      PMCID: PMC6618946          DOI: 10.1523/JNEUROSCI.3905-12.2013

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  19 in total

1.  Categorizing objects from MEG signals using EEGNet.

Authors:  Ran Shi; Yanyu Zhao; Zhiyuan Cao; Chunyu Liu; Yi Kang; Jiacai Zhang
Journal:  Cogn Neurodyn       Date:  2021-09-17       Impact factor: 5.082

Review 2.  IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG).

Authors:  Riitta Hari; Sylvain Baillet; Gareth Barnes; Richard Burgess; Nina Forss; Joachim Gross; Matti Hämäläinen; Ole Jensen; Ryusuke Kakigi; François Mauguière; Nobukatzu Nakasato; Aina Puce; Gian-Luca Romani; Alfons Schnitzler; Samu Taulu
Journal:  Clin Neurophysiol       Date:  2018-04-17       Impact factor: 3.708

3.  Real-world structure facilitates the rapid emergence of scene category information in visual brain signals.

Authors:  Daniel Kaiser; Greta Häberle; Radoslaw M Cichy
Journal:  J Neurophysiol       Date:  2020-06-10       Impact factor: 2.714

Review 4.  The brain timewise: how timing shapes and supports brain function.

Authors:  Riitta Hari; Lauri Parkkonen
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2015-05-19       Impact factor: 6.237

5.  Dynamic information processing states revealed through neurocognitive models of object semantics.

Authors:  Alex Clarke
Journal:  Lang Cogn Neurosci       Date:  2015-04-21       Impact factor: 2.331

6.  Parallel processing of face and house stimuli by V1 and specialized visual areas: a magnetoencephalographic (MEG) study.

Authors:  Yoshihito Shigihara; Semir Zeki
Journal:  Front Hum Neurosci       Date:  2014-11-07       Impact factor: 3.169

7.  Characterizing the dynamics of mental representations: the temporal generalization method.

Authors:  J-R King; S Dehaene
Journal:  Trends Cogn Sci       Date:  2014-03-02       Impact factor: 20.229

Review 8.  Neural population coding: combining insights from microscopic and mass signals.

Authors:  Stefano Panzeri; Jakob H Macke; Joachim Gross; Christoph Kayser
Journal:  Trends Cogn Sci       Date:  2015-02-07       Impact factor: 24.482

9.  Decoding Rich Spatial Information with High Temporal Resolution.

Authors:  Mark G Stokes; Michael J Wolff; Eelke Spaak
Journal:  Trends Cogn Sci       Date:  2015-10-01       Impact factor: 20.229

10.  Two distinct dynamic modes subtend the detection of unexpected sounds.

Authors:  Jean-Rémi King; Alexandre Gramfort; Aaron Schurger; Lionel Naccache; Stanislas Dehaene
Journal:  PLoS One       Date:  2014-01-27       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.