An assumption-free quantification of neural responses to electrical stimulations

Rotem Ruach, Rea Mitelman, Efrat Sherman, Oren Cohen, Yifat Prut*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Background: Connectivity between brain regions provides the fundamental infrastructure for information processing. The standard way to characterize these interactions is to stimulate one site while recording the evoked response from a second site. The average stimulus-triggered response is usually compared to the pre-stimulus activity. This requires a set of prior assumptions regarding the amplitude and duration of the evoked response. New method: We introduce an assumption-free method for detecting and clustering evoked responses. We used Independent Component Analysis to reduce the dimensions of the response vectors, and then clustered them according to a Gaussian mixture model. This enables both the detection and categorization of responsive sites into different subtypes. Results: Our method is demonstrated on recordings obtained from the sensory-motor cortex of behaving primates in response to stimulation of the cerebello-thalamo-cortical tract. We detected and classified the evoked responses of local field potential (LFP) and local spiking activity (multiunit activity-MUA). We found a strong association between specific input (LFP) and output (MUA) patterns across cortical sites, further supporting the physiological relevance of the proposed method. Comparison with existing methods: Our method detected the vast majority of sites found in the conventional, significant threshold-crossing method. However, we found a subgroup of sites with a robust response that were missed when using the conventional method. Conclusion: Our method provides a useful, assumption-free tool for detecting and classifying neural evoked responses in a physiologically-relevant manner.

Original languageAmerican English
Pages (from-to)10-17
Number of pages8
JournalJournal of Neuroscience Methods
StatePublished - 1 Oct 2015

Bibliographical note

Funding Information:
This work was supported in part by the Israel Science Foundation ( ISF-1787/13 ), the German–Israel Foundation ( I-1224-396.13/2012 ), and through the generous support of the Baruch Foundation . RM was supported in part by the “Hoffman Leadership and Responsibility” Fellowship program.

Publisher Copyright:
© 2015 Elsevier B.V.


  • Clustering
  • Evoked responses
  • Functional connectivity
  • Stimulus-triggered averaging


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