Neurophysiology

Neurofeedback is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which neurofeedback is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of neurofeedback. The objective is to provide a firmly neurophysiological account of neurofeedback, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a ‘black box’. To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from ‘bottom-up’ mechanisms of neural synchronization, followed by ‘top-down’ regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of neurofeedback in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). The central thesis put forward is that neurofeedback tunes brain oscillations toward a homeostatic set-point which maintains optimal network flexibility and stability (i.e. self-organized criticality).

OBJECTIVE: A widely accepted theoretical model suggests that vertebral hypomobility can cause pain and abnormal spinal mechanics because of changes in sensory input from spinal and paraspinal tissues. The purpose of this pilot study was 3-fold: (1) to make a preliminary determination if chronic vertebral hypomobility at L4 through L6 in the rat would affect synaptic density and/or morphology in the superficial dorsal horn of the L2 spinal cord level, (2) to identify relevant outcome variables for future studies, and (3) to obtain preliminary data that would permit estimating an appropriate sample size for future studies. METHODS: Using an established rat model, we fixed 3 contiguous lumbar segments (L4-L6) for 8 weeks with a specially engineered vertebral fixation device. Electron micrographs were obtained from 2 animals from the experimental (fixed) group and each of 3 control groups (no surgery, surgery but no devices implanted, and devices implanted but not fixed). Synapses were randomly selected using a stereological approach and were analyzed for symmetry, curvature, type of postsynaptic profile, and perforations. The synaptic density was also estimated. RESULTS: There was increased synaptic density and percentage of positively curved synapses in the dorsal horn of experimental animals as compared with controls. Experimental animals had a lower percentage of axospinous synapses, with a concomitant increase in the percentage of synapses on dendritic shafts. CONCLUSIONS: These preliminary data suggest for the first time that chronic vertebral hypomobility at L4 through L6 in the rat affects synaptic density and morphology in the superficial dorsal horn of the L2 spinal cord level. More definitive studies are warranted, and the biologic significance of these finding should be investigated.

A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected using interictal and preictal references, is estimated based on the values obtained from the current epoch and the epochs of the last 5 min. The resulting distribution for each selected bin is then compared to two reference distributions (interictal and preictal), and a seizure prediction index is developed. Comparing this index with a patient-specific threshold for all EEG channels, a seizure prediction alarm is finally generated. The algorithm was tested on approximately 15.5 hours of multichannel scalp EEG recordings from three patients with temporal lobe epilepsy, including 14 seizures....