Sara L. Gonzalez Andino

Recordings of brain electrophysiological activity provide the most direct reflect of neural function. Information contained in these signals varies as a function of the spatial scale at which recordings are done: from single cell recording to large scale macroscopic fields, e.g., scalp EEG. Microscopic and macroscopic measurements and models in Neuroscience are often in conflict. Solving this conflict might require the developments of a sort of bio-statistical physics, a framework for relating the microscopic properties of individual cells to the macroscopic or bulk properties of neural circuits. Such a framework can only emerge in Neuroscience from the systematic analysis and modeling of the diverse recording scales from simultaneous measurements. In this article we briefly review the different measurement scales and models in modern neuroscience to try to identify the sources of conflict that might ultimately help to create a unified theory of brain electromagnetic fields. We argue that seen the different recording scales, from the single cell to the large scale fields measured by the scalp electroencephalogram, as derived from a unique physical magnitude—the electric potential that is measured in all cases—might help to conciliate microscopic and macroscopic models of neural function as well as the animal and human neuroscience literature.

Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that – in stroke – brain machine interfaces (BMIs) can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI-electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and – the particularly important but unsolved issue of – how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

Invasive recordings of local field potentials (LFPs) have been used to "read the mind" of monkeys in real time. Here we investigated whether noninvasive field potentials estimated from the scalp-recorded electroencephalogram (EEG) using the ELECTRA source localization algorithm could provide real-time decoding of mental states in healthy humans. By means of pattern recognition techniques on 500-ms EEG epochs, we were able to discriminate accurately from single trials which of four categories of visual stimuli the subjects were viewing. Our results show that it is possible to reproduce the decoding accuracy previously obtained in animals with invasive recordings. A comparison between the decoding results and the subjects' behavioral performance indicates that oscillatory activity (OA), elicited in specific brain regions) codes better for the visual stimulus category presented than the subjects' actual response, i.e., is insensitive to voluntary or involuntary errors. The identification of brain regions participating in the decoding process allowed us to construct 3D-functional images of the task-related OA. These images revealed the activation of brain regions known for their involvement in the processing of this type of visual stimuli. Electrical neuroimaging therefore appears to have the potential to establish what the brain is processing while the stimuli are being seen or categorized, i.e., concurrently with sensory-perceptual processes.

The brain electrical activity measured as the EEG signal is a powerful tool for the understanding of normal and pathological brain function. The EEG presents a high temporal resolution but a low spatial resolution. The latter can be improved solving the so-called inverse problem, if some a priori physiological and anatomical information is included. The present review presents the biophysical bases that allows brain source localization in realistic head models, as well as the two basic source models used: dipolar models and distributed source models. Finally, an example of practical application for the localization of epileptic focus using the distributed solution EPIFOCUS will be presented.

Response to a stimulus is faster when a subject is attending and knows beforehand how to respond. It has been suggested recently that this occurs because ongoing neuronal activity is spatially and temporally structured during states of expectancy preceding a stimulus. This mechanism is believed to mediate top‐down processing, facilitating the early grouping and selection of distributed neuronal ensembles implicated in ensuing sensory–motor processing. To validate this model, it must be shown that some features of this early ongoing neural activity are correlated with subsequent perceptual decisions or behavioral events. We investigated this hypothesis in an electrophysiologic study in 12 subjects carrying out a simple visuomotor reaction‐time task. Local field potentials (LFP) at each brain voxel were estimated using a linear distributed inverse solution termed “ELECTRA” for each single trial of each subject. The energy of oscillations for different frequency bands was computed for the period between the warning cue and visual stimuli by applying a time–frequency decomposition to the estimated LFP. A nonparametric correlation coefficient was then calculated between energy of oscillations and reaction times for each single sweep. Gamma band oscillatory activity in a frontoparietal network before stimulus onset significantly correlated with reaction time for a significant amount of subjects. These results provide direct evidence for the role of neural oscillations as a top‐down attentional control mechanism that mediates the speed of motor actions. Hum. Brain Mapping 24:50–58, 2005.