An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task

Human reaction times during sensory-motor tasks vary considerably. To begin to understand how this variability arises, we examined neuronal populational response time variability at early versus late visual processing stages. The conventional view is that precise temporal information is gradually lost as information is passed through a layered network of mean-rate "units." We tested in humans whether neuronal populations at different processing stages behave like mean-rate "units". A blind source separation algorithm was applied to MEG signals from sensory-motor integration tasks. Response time latency and variability for multiple visual sources were estimated by detecting single-trial stimulus-locked events for each source. In two subjects tested on four visual reaction time tasks, we reliably identified sources belonging to early and late visual processing stages. The standard deviation of response latency was smaller for early rather than late processing stages. This supports the hypothesis that human populational response time variability increases from early to late visual processing stages.

[1]  E. Vaadia,et al.  Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. , 1993, Journal of neurophysiology.

[2]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[3]  Tzyy-Ping Jung,et al.  Analyzing and Visualizing Single-Trial Event-Related Potentials , 1998, NIPS.

[4]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[5]  G D Lewen,et al.  Reproducibility and Variability in Neural Spike Trains , 1997, Science.

[6]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[7]  B. Knight,et al.  Response variability and timing precision of neuronal spike trains in vivo. , 1997, Journal of neurophysiology.

[8]  John H. R. Maunsell,et al.  On the relationship between synaptic input and spike output jitter in individual neurons. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[9]  D. Poeppel,et al.  Magnetoencephalography and magnetic source imaging. , 1998, Neuropsychiatry, neuropsychology, and behavioral neurology.

[10]  T. Sejnowski,et al.  Effects of cholinergic modulation on responses of neocortical neurons to fluctuating input. , 1997, Cerebral cortex.

[11]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[12]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[13]  Christof Koch,et al.  Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey , 1999, Neural Computation.

[14]  T. Sejnowski,et al.  Reliability of spike timing in neocortical neurons. , 1995, Science.

[15]  Michael J. Berry,et al.  The structure and precision of retinal spike trains. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[16]  R. Christopher deCharms,et al.  Primary cortical representation of sounds by the coordination of action-potential timing , 1996, Nature.

[17]  J. Lewine,et al.  CHAPTER 9 – Magnetoencephalography and Magnetic Source Imaging , 1995 .

[18]  D. Snodderly,et al.  Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys , 1997, The Journal of Neuroscience.

[19]  T. Ens,et al.  Blind signal separation : statistical principles , 1998 .