A systematic approach to selecting task relevant neurons.
BACKGROUND
Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics.


METHODS
This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test.


RESULTS
MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples.


METHODS
The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates.


CONCLUSIONS
These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: kkahn6@jhmi.edu.
Kevin
K
Kahn
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Shreya
S
Saxena
Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Emad
E
Eskandar
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Nitish
N
Thakor
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY 14642, USA.
Marc
M
Schieber
Department of Neurosciences and Center for Neurological Restoration, Cleveland Clinic, Cleveland, OH 44195, USA.
John T
JT
Gale
Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA.
Bruno
B
Averbeck
Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA.
Uri
U
Eden
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Sridevi V
SV
Sarma
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D003198:Computer Simulation; D005385:Fingers / Q000294:innervation; D006801:Humans; D008253:Macaca mulatta; D008297:Male; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D011930:Reaction Time / Q000502:physiology; D012438:Saccades / Q000502:physiology; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology
Model based; Neuron selection; Point processes; Task-related neurons