Loreta Neurofeedback References
- “BESA Research Tutorial 1: Introduction to Discrete Source Analysis” and “BESA Research Tutorial 4: Distributed Source Imaging.” BESA.
- Budzynski TH, Budzynski HK, Evans JR, Abarbanel A (2009). Introduction to Quantitative EEG and Neurofeedback: Advanced Theory and Applications. Second Edition. Academic Press Mass.
- Cannon RL (2012). Low Resolution Electromagnetic tomography (LORETA)—Basic Concepts and Clinical Applications. Texas: BMED Press.
- Cannon RL1, Baldwin DR, Diloreto DJ, Phillips ST, Shaw TL, Levy JJ.
- LORETA Neurofeedback in the Precuneus: Operant Conditioning in Basic Mechanisms of Self-Regulation. Clin EEG Neurosci. 2014 Mar 3.
- Cannon RL1, Baldwin DR, Shaw TL, Diloreto DJ, Phillips SM, Scruggs AM, Riehl TC.
- Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days. Neurosci Lett. 2012 Jun 14;518(1):27-31.
- Pittau F, Grouiller F, Spinelli L, Seeck M, Michel, & CM, Vulliemoz. The role of functional neuroimaging in pre-surgical epilepsy evaluation. Frontiers in Neurology (2014) 5, article 31: 1-16. doi: 10.3389/fneur.2014.00031
- Thatcher RW. (2012). Handbook of Quantitative Electroencephalography and EEG Biofeedback. Florida: Anipublishing Co.
- Thatcher RW and Lubar JF. (2014). Z Score Neurofeedback: Clinical Applications. San Diego: Academic Press.
- Simkin DR, Thatcher RW, Lubar J. Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury. Child Adolesc Psychiatr Clin N Am. 2014 Jul;23(3):427-64.
Front Hum Neurosci. 2014; 8: 1005.
Herbert Bauer* and Avni Pllana
Along with the development of distributed EEG source modeling methods, basic approaches to local brain activity (LBA-) neurofeedback (NF) have been suggested. Meanwhile several attempts using LORETA and sLORETA have been published. This article specifically reports on “EEG-based LBA-feedback training” developed by Bauer et al. (2011). Local brain activity-feedback has the advantage over other sLORETA-based approaches in the way that feedback is exclusively controlled by EEG-generating sources within a selected cortical region of training (ROT): feedback is suspended if there is no source. In this way the influence of sources in the vicinity of the ROT is excluded. First applications have yielded promising results: aiming to enhance activity in left hemispheric linguistic areas, five experimental subjects increased significantly the feedback rate whereas five controls receiving sham feedback did not, both after 13 training runs (U-test, p < 0.01). Preliminary results of another study that aims to document effects of LBA-feedback training of the Anterior Cingulate Cortex (ACC) and Dorso-Lateral Prefrontal Cortex (DLPFC) by fMRI revealed more local ACC-activity after successful training (Radke et al., 2014).
Front Hum Neurosci. 2014 Jul 23;8:529. Thatcher RW1, North DM1, Biver CJ1.
The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG).
The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences.
Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300-350 ms and (2) 350-450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas.
The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a “shutter” that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.