Rotem Ruach1, Rea Mitelman2, Efrat Sherman3, Oren Cohen3, Yifat Prut4. 1. Department of Medical Neurobiology, the Hebrew University-Hadassah Medical School, Jerusalem, 91120, Israel. 2. Department of Medical Neurobiology, the Hebrew University-Hadassah Medical School, Jerusalem, 91120, Israel; The Interdisciplinary Center for Neural Computation, the Hebrew University, Jerusalem, 91904, Israel; Edmond and Lily Safra Center for Brain Sciences, the Hebrew University, Jerusalem, 91904, Israel. 3. Department of Medical Neurobiology, the Hebrew University-Hadassah Medical School, Jerusalem, 91120, Israel; Edmond and Lily Safra Center for Brain Sciences, the Hebrew University, Jerusalem, 91904, Israel. 4. Department of Medical Neurobiology, the Hebrew University-Hadassah Medical School, Jerusalem, 91120, Israel; Edmond and Lily Safra Center for Brain Sciences, the Hebrew University, Jerusalem, 91904, Israel. Electronic address: yifatpr@ekmd.huji.ac.il.
Abstract
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.
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.