Decoding of cortex-wide brain activity from local recordings of neural potentials

Abstract

Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain–computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.

Cellular Calcium Activity at Depth Predicted from Surface Potential Recordings using Ultra-high Density Transparent Graphene Arrays

“…Given the correlation between the MUA power recorded from the surface and the cellular calcium signals imaged at 225 μm depth, we asked whether it is possible to predict the brain activity at deeper layers by only harnessing high-resolution electrical recordings from the cortical surface. To that end, we implemented a simple neural network model that consists of a linear hidden layer, a single-layer LSTM network, and a linear readout layer ( Figure 5a ) [22, 72]. The neural networks were trained to learn the nonlinear relationships between cellular calcium activities and surface potentials.…”

“…To that end, we implemented a simple neural network model that consists of a linear hidden layer, a single-layer LSTM network, and a linear readout layer (Figure 5a) [22,72]. The neural networks were trained to learn the nonlinear relationships between cellular calcium activities and surface To evaluate the contributions spatially provided by different channels, we performed the decoding using subsets of channels starting from those closest to the FoV (Figure S7a).…”

“…PEDOT:PSS might exhibit chronic reliability issues due to delamination [53]. Transparent graphene electrodes have been successfully employed in multimodal studies previously [22, 25–27, 35, 54–60]. However, all graphene arrays demonstrated to date had low channel counts (∼16) and large electrode opening sizes (∼50 um or larger) limiting the spatiotemporal resolution of recorded signals.…”

“…To date, multimodal experiments have been used to investigate the neural dynamics with applications ranging from studies of neural circuits [4][5][6][7][8][9][10] or pathophysiology of brain disorders such as Parkinson's disease [11], Alzheimer's disease [12], and Schizophrenia [13][14][15][16][17] to hybrid brain computer interfaces (BCI) combining two different modalities with complementary strengths to enhance performance [18][19][20][21]. Among these multimodal approaches, experiments concurrently recording electrophysiological during optical imaging and optogenetic stimulation has become a powerful approach to (i) combine temporal resolution advantage of electrophysiology with high spatial resolution and cell-type specificity of optical methods, (ii) to bridge the knowledge gap between basic neuroscience research relying on optical methods employing genetic modifications and clinical research mainly using electrical recordings, and (iii) to expand spatial reach of neural recordings [22] and (iv) to identify cell types through opto-tagging during electrophysiological recordings of neuronal spikes [23,24]. To enable crosstalk and artifact free integration of electrical and optical modalities, transparent graphene electrodes with artifact-free recording capability have been proposed [25][26][27][28][29].…”

Functional states shape the spatiotemporal representation of local and cortex-wide neural activity in mouse sensory cortex

“…Studies taking into account the related underlying state of cortical or thalamic neuronal populations demonstrated that these locally recorded signals themselves can predict whole brain activity ( 20 ) and functional connectivity ( 15 ). It has further been shown that local recordings of neuronal activity carry information that allows decoding of cortex-wide brain activity across modalities ( 76 ). We reported here that the two exemplary states, SWA and PA, differ regarding spatiotemporal characteristics on the local and global scales and show state-dependent propagation modes, which might explain these findings.…”