Decoding Adaptive Visuomotor Behavior Mediated by Non-linear Phase Coupling in Macaque Area MT

Abstract

The idea that a flexible behavior relies on synchronous neural activity within intra-and inter-associated cortical areas has been a matter of intense research in human and animal neuroscience. The neurophysiological mechanisms underlying this behavioral correlate of the synchronous activity are still unknown. It has been suggested that the strength of neural synchrony at the level of population is an important neural code to guide an efficient transformation of the sensory input into the behavioral action. In this study, we have examined the non-linear synchronization between neural ensembles in area MT of the macaque visual cortex by employing a non-linear cross-frequency coupling technique, namely bicoherence. We trained a macaque monkey to detect a brief change in the cued stimulus during a visuomotor detection task. The results show that the non-linear phase synchronization in the high-gamma frequency band (100-250 Hz) predicts the animal's reaction time. The strength of non-linear phase synchronization is selective to the target stimulus location. In addition, the non-linearity characteristics of neural synchronization are selectively modulated when the monkey covertly attends to the stimulus inside the neuron's receptive field. This additional evidence indicates that non-linear neuronal synchronization may be affected by a cognitive function like spatial attention. Our neural and behavioral observations reflect that two crucial processes may be involved in processing of visuomotor information in area MT: (I) a non-linear cortical process (measured by the bicoherence) and (II) a linear process (measured by the spectral power).

Characterizing the orientation selectivity in V1 and V4 of macaques by quadratic phase coupling

“…Generally, there are several measures to quantify the strength of QPC between all frequency pairs. For example, the TotalBic is the total bicoherence in the signal between specific frequency bands, which reflects the rhythmic synchronization between the oscillations in neuronal populations at different frequencies; the maximum eigenvalue and Shannon entropy of eigenvalues can be regarded as indices of global phase coupling for the whole bicoherence matrix, which measures the synchronization between oscillatory activities in the neuronal population; the DiagBic is a subset of the bicoherence values along the diagonal line of the bicoherence matrix, which reveals the self-frequency and self-phase coupling presented in neural circuits [34][35][36]. The Total-Bic was employed in this study because it performed better than the other indices in characterizing the orientation selectivity.…”

“…Specifically, the gamma-band LFPs play a critical role in the visual information processing. For example, the higher gamma rhythms are mostly linked to the fastspiking visual neurons [22]; the orientation selectivity in V1 of awake monkey is modulated by gammaband LFPs [23]; the distinct bands (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) Hz for low gamma and 60-80 Hz for high gamma) of stimulustriggered gamma oscillations are systematically linked to the orientation selectivity index of neurons in the cat primary visual cortex [24]. Therefore, studying the gamma-band LFPs is of great significance to understand the mechanism for generating the orientation selectivity.…”

“…It has been used to describe the interaction between neuronal oscillations of LFP in different frequency bands. For example, the low-beta (13-20 Hz) band LFP modulates the high-beta (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) band LFP in Parkinson's disease patients [32]; the low frequency (10-13 Hz) and a higher frequency (77-82 Hz) rhythm in a ventral region of the premotor cortex can produce a sum of these two frequencies in a distant motor site [33]; the strength of nonlinear phase synchronization in the high-gamma frequency band (100-250 Hz) is selective to the target stimulus location during a visuomotor detection task [34]. However, to the best of our knowledge, there are no researches using QPC to evaluate the interaction between different frequencies of gamma-band LFPs in visual cortex, which may be helpful to explain how the orientation selectivity generates.…”

Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke

“…More and more studies have shown complex brain information interaction between different frequency bands, known as cross-frequency coupling (CFC). Several brain regions of human and non-human primates found CFC phenomena, such as the hippocampus, prefrontal cortex, and sensory cortex (Mormann et al, 2005 ; Canolty et al, 2006 ; Jensen and Colgin, 2007 ; Khamechian and Daliri, 2020 ). Besides, increasing researchers use CFC to analyze cognitive and perceptual processes.…”

Predicting Grating Orientations With Cross-Frequency Coupling and Least Absolute Shrinkage and Selection Operator in V1 and V4 of Rhesus Monkeys

“…Based on the three properties of a signal, i.e., frequency, amplitude, and phase, there are four fundamental types of CFC, including phase–frequency coupling (PFC) (Roberts et al, 2013 ), phase–phase coupling (PPC) (Belluscio et al, 2012 ), phase–amplitude coupling (PAC) (Tort et al, 2010 ), and amplitude–amplitude coupling (AAC) (Yeh et al, 2016 ). PAC reflects the degree that the amplitude of higher-frequency oscillations is modulated by the phase of lower-frequency oscillations (Canolty et al, 2006 ), which is the most common and important type of CFC and plays a major role in the brain functions such as motion (Cheung et al, 2019 ; Khamechian and Daliri, 2020 ), memory (Tseng et al, 2019 ), learning (Zaleshin and Merzhanova, 2019 ), and sleep (Cox et al, 2019 ). There are many algorithms to estimate PAC, such as mean vector length (MVL) (Canolty et al, 2006 ), modulation index (MI) (Tort et al, 2008 ), and generalized eigendecomposition-based cross-frequency coupling framework (gedCFC) (Cohen, 2017 ).…”