A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex

Abstract

Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (PCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, PCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 ± 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 ± 11.73 mm), OLE (20.17 ± 6.43 mm), PCA (19.13 ± 0.75 mm), SFS (22.75 ± 2.01 mm), and NN (16.75 ± 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

“…For each rat, an 8-channel laboratory-made stainless microwire electrode array (product # M177390, diameter of 0.002 ft., California Fine Wire Co., Grover Beach, CA, USA; the electrodes were spaced 500 µm apart) was vertically implanted into the layer V of the forelimb territory of the primary motor (M1) cortex (anterior-posterior [AP]: +2.0 mm to −1.0 mm, medial-lateral [ML]: +2.7 mm, dorsal-ventral [DV]: 1.5 mm. For determining the location of the forelimb representation of M1 for the electrode implantation, the intracortical microstimulation was applied to confirm via forelimb muscle twitches observed (Yang et al, 2016). Following a 1-week post-surgery recovery period, the animals received the water reward-related lever-pressing training.…”

“…Figure 2 showed an example of the rat forelimb movement while pressing the lever and corresponding neural spike trains. A time-lag was known to exist between neuronal firing and the associated forelimb state because of their causal relationship (Paninski et al, 2004;Wu et al, 2004;Yang et al, 2016). Furthermore, the decoding accuracy was improved when the optimal timelag is considered.…”

“…Our prior work demonstrated a linear decoding model of the relationship between neural firing and kinematic parameters (Yang et al, 2016). A sliced inverse regression (SIR) with errorfeedback learning (SIR-EF) was implemented based on an SIR linear neural decoder to fairly compare to the ECPNN-EF algorithm (see Supplementary Note 11).…”

“…Rodent models with small numbers of implanted electrodes have been widely used to investigate state-of-the-art neural prostheses. Previous studies have demonstrated the decoding performances of various methods at the motor cortex (Zhou et al, 2010;Yang et al, 2016), somatosensory cortex (Pais-Vieira et al, 2013), and hippocampus (Tampuu et al, 2019) in rodent models. The results indicated that good decoding methods should be considerably more robust to small sample sizes caused by limited neurons or limited recording times.…”