Exploring altered oscillatory activity in the anterior cingulate cortex after nerve injury: Insights into mechanisms of neuropathic allodynia.

While neural oscillations play a critical role in sensory perception, it remains unclear how these rhythms function under conditions of neuropathic allodynia. Recent studies demonstrated that the anterior cingulate cortex (ACC) is associated with the affective-aversive component of pain, and plasticity changes in this region are closely linked to abnormal allodynic sensations. Here, to study the mechanisms of allodynia, we recorded local field potentials (LFPs) in the bilateral ACC of awake-behaving rats and compared the spectral power and center frequency of brain oscillations between healthy and CCI (chronic constriction injury) induced neuropathic pain conditions. Our results indicated that activation of the ACC occurs bilaterally in the presence of neuropathic pain, similar to the healthy condition. Furthermore, CCI affects both spontaneous and stimulus-induced activity of ACC neurons. Specifically, we observed an increase in spontaneous beta activity after nerve injury compared to the healthy condition. By stimulating operated or unoperated paws, we found more intense event-related desynchronization (ERD) responses in the theta, alpha, and beta frequency bands and faster alpha center frequency after CCI compared to before CCI. Although the behavioral manifestation of allodynia was more pronounced in the operated paw than the unoperated paw following CCI, there was no significant difference in the center frequency and ERD responses observed in the ACC between stimulation of the operated and unoperated limbs. Our findings offer evidence supporting the notion that aberrancies in ACC oscillations may contribute to the maintenance and development of neuropathic allodynia.

Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement.

Area 2 of the primary somatosensory cortex (S1), encodes proprioceptive information of limbs. Several studies investigated the encoding of movement parameters in this area. However, the single-trial decoding of these parameters, which can provide additional knowledge about the amount of information available in sub-regions of this area about instantaneous limb movement, has not been well investigated. We decoded kinematic and kinetic parameters of active and passive hand movement during center-out task using conventional and state-based decoders. Our results show that this area can be used to accurately decode position, velocity, force, moment, and joint angles of hand. Kinematics had higher accuracies compared to kinetics and active trials were decoded more accurately than passive trials. Although the state-based decoder outperformed the conventional decoder in the active task, it was the opposite in the passive task. These results can be used in intracortical micro-stimulation procedures to provide proprioceptive feedback to BCI subjects.

Decoding locomotion speed and slope from local field potentials of rat motor cortex.

BACKGROUND AND OBJECTIVE: Local Field Potentials (LFPs) recorded from the primary motor cortex (M1) have been shown to be very informative for decoding movement parameters, and these signals can be used to decode forelimb kinematic and kinetic parameters accurately. Although locomotion is one of the most basic and important motor abilities of humans and animals, the potential of LFPs in decoding abstract hindlimb locomotor parameters has not been investigated. This study investigates the feasibility of decoding speed and slope of locomotion, as two important abstract parameters of walking, using the LFP signals. METHODS: Rats were trained to walk smoothly on a treadmill with different speeds and slopes. The brain signals were recorded using the microwire arrays chronically implanted in the hindlimb area of M1 while rats walked on the treadmill. LFP channels were spatially filtered using optimal common spatial patterns to increase the discriminability of speeds and slopes of locomotion. Logarithmic wavelet band powers were extracted as basic features, and the best features were selected using the statistical dependency criterion before classification. RESULTS: Using 5 s LFP trials, the average classification accuracies of four different speeds and seven different slopes reached 90.8% and 86.82%, respectively. The high-frequency LFP band (250-500 Hz) was the most informative band about these parameters and contributed more than other frequency bands in the final decoder model. CONCLUSIONS: Our results show that the LFP signals in M1 accurately decode locomotion speed and slope, which can be considered as abstract walking parameters needed for designing long-term brain-computer interfaces for hindlimb locomotion control.