An assumption-free quantification of neural responses to electrical stimulations

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.

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.

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.

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.

Our method provides a useful, assumption-free tool for detecting and classifying neural evoked responses in a physiologically-relevant manner.