Frequency Domain Analysis of Human Subdural Recordings

Magnetoencephalography Signal Processing, Forward Modeling, Magnetoencephalography Inverse Source Imaging, and Coherence Analysis

Magnetoencephalography (MEG) is a noninvasive functional imaging technique for the brain. MEG directly measures the magnetic signal due to neuronal activation in gray matter with high spatial localization accuracy. The first part of this article covers the overall concepts of MEG and the forward and inverse modeling techniques. It is followed by examples of analyzing evoked and resting-state MEG signals using a high-resolution MEG source imaging technique. Next, different techniques for connectivity and network analysis are reviewed with examples showing connectivity estimates from resting-state and epileptic activity.

Abnormal cortical asymmetry as a target for neuromodulation in neuropsychiatric disorders: A narrative review and concept proposal

Recent advances in knowledge relating to the organization of neural circuitry in the human brain have increased understanding of disorders involving brain circuit asymmetry. These asymmetries, which can be measured and identified utilizing EEG and LORETA analysis techniques, may be a factor in mental disorders. New treatments involving non-invasive brain stimulation (NIBS), including trans-cranial magnetic stimulation, direct current stimulation and vagal nerve stimulation, have emerged in recent years. We propose that EEG identification of circuit asymmetry geometries can direct non-invasive brain stimulation more specifically for treatments of mental disorders. We describe as a narrative review new NIBS therapies that have been developed and delivered, and suggest that they are proving effective in certain patient groups. A brief narrative of influence of classical and operant conditioning of neurofeedback on EEG coherence, phase, abnormalities and Loreta's significance is provided. We also discuss the role of Heart rate variability and biofeedback in influencing EEG co-relates. Clinical evidence is at an early stage, but the basic science evidence and early case studies suggest that this may be a promising new modality for treating mental disorders and merits further research.

Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification With Intracranial EEG and FEM Simulations

OBJECTIVE

Electric fields (EF) of approx. 0.2 V/m have been shown to be sufficiently strong to both modulate neuronal activity in the cerebral cortex and have measurable effects on cognitive performance. We hypothesized that the EF caused by the electrical activity of extracranial muscles during natural chewing may reach similar strength in the cerebral cortex and hence might act as an endogenous modality of brain stimulation. Here, we present first steps toward validating this hypothesis.

METHODS

Using a realistic volume conductor head model of an epilepsy patient having undergone intracranial electrode placement and utilizing simultaneous intracranial and extracranial electrical recordings during chewing, we derive predictions about the chewing-related cortical EF strength to be expected in healthy individuals.

RESULTS

We find that in the region of the temporal poles, the expected EF strength may reach amplitudes in the order of 0.1-1 V/m.

CONCLUSION

The cortical EF caused by natural chewing could be large enough to modulate ongoing neural activity in the cerebral cortex and influence cognitive performance.

SIGNIFICANCE

The present study lends first support for the assumption that extracranial muscle activity might represent an endogenous source of electrical brain stimulation. This offers a new potential explanation for the puzzling effects of gum chewing on cognition, which have been repeatedly reported in the literature.

Connectivity measurements for network imaging

Communication across the brain networks is dependent on neuronal oscillations. Detection of the synchronous activation of neurons can be used to determine the well-being of the connectivity in the human brain networks. Well-connected highly synchronous activity can be measured by MEG, EEG, fMRI, and PET and then analyzed with several types of mathematical algorithms. Coherence is one mathematical method that can detect how well 2 or more sensors or brain regions have similar oscillatory activity with each other. Phase synchrony can be used to determine if these oscillatory activities are in sync or out of sync with each other. Correlation is used to determine the strength of interaction between two locations or signals. Granger causality can be used to determine the direction of the information flow in the neuronal brain networks. Statistical analysis can be performed on the connectivity results to verify evidence of normal or abnormal network activity in a patient.