The MEG Core staff works interactively with an extensive group of PI's in NIMH, NINDS and NIDCD for study design, task programming development, acquisition protocols, and signal processing and data analysis. Procedures have been setup for data security, transfer and storage. A substantial Policy and Procedures Manual has been established. We have also worked with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis. This work has been extended to utilize the instant correlation feature of AFNI to investigate connectivty pattern in MEG data. Technical and scientific results have been excellent. Signal analysis development includes event-related SAM (synthetic aperture magnetometry) and 275 channel ICA (independent component analysis). Development of time-frequency analysis methods has included Stockwell and wavelet transforms as well as multi-taper techniques. Of particular interest is coherence analysis of virtual channels as a method to investigate interacting brain regions. Staff are working with other MEG groups to integrate several signal processing packages including FieldTrip, NUTMEG and BrainStorm. The goal is to have a unified tool package with a user-friendly interface available to the user community. The SAM software is being successfully run on the Biowulf Cluster (utilizing the open source OCTAVE software installed by MEG Core staff)allowing for tremendous increase in computing power. Dr Robinson has introduced new time slice and entropy based source analysis methods to the MEG user community. Dr Robinson's extion of complexity measures to MEG time series data has received considerable interest not only among IRP investigators but from MEG laboratories around the world. Groups at the University of Tubingen, the University of Nottingham, Aston University and the Brain Research Center at Bar Ilan are specially investigating these new methods in their respective studies. A former MEG Core user, Qian Luo, now at the St Louis MEG center, is applying to patients with TBi, Traumatic Brain Injury, an especially very potentially inporant biomarker aea. The ability to localize not only cortial surface sources but deeper structures has been demonstrated. For a working memory task MEG activation patterns for beta band have shown exceptional agreement with fMRI (functional magnetic resonance imaging) results in the same subject group. Beta desynchronization patterns agree highly with the network of bilateral DLPFC (dorsolateral prefrontal cortex) and posterior parietal cortex seen during working memory in fMRI tasks. Altamura et al have shown that there are anticipatory signals seen in the modulation of prefrontal activity that appear to arise from preparation for upcoming task demands. In earlier work Brian Cornwell and colleagues have demonstrated that MEG can reliably discriminate amygdala and hippocampal signals using MEG beamforming techniques. Continuing studies have shown that hippocampal function is impaired in patients with major depression as well as other brain changes when treated with ketamine. These studies are of particular interest to possibly elucidate the mechanism of the anti-depressant action of ketamine infusion. previous results have shown that increased anterior cingulate activity may be a biomarker that predicts the rapid antidepressant response to ketamine. Salvadore and Cornwell have found that functional connectivity during a working memory task can predict the antidepressant response of ketemine. A commentary has suggested that 'psychiatric stress testing'may become a strategy for translational psychphamacology. This work continues in several treatment and pharmacology based studies by Dr Zarate and others. Dr Cormwell has extended his early work on spatial navigation and has found specific interactions of MEG recorded hippocampal theta activity in spatial cognition and anxiety. Studying how the brain organizes itself into functional networks is key to understanding normal human cognition as well as when it becomes disordered in mental illness. To this end Bassett and co-workers used the spatial and temporal ability of MEG to study how the brain changes configuration during a motor task compared to when at rest. They found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. They have continued this work to demonstrate that dysfuctional networks can be detected and related to behavioral differences in clinial groups. We have also found differences in resting network patterns in patient groups. The continued interest in 'resting activity'has spurred several addtional MEG studies. A new NINDS investigator, Dr Biyu He, has begun studies of scale free properties in functional imaging distinquished at rest and during tasks. Reorganization of functional brain networks can also be investigated using these methods. Dr Horwitz and his NIDCD group have extended large scale neural models to examine connectiivty measures that can reflect cortical dynamics at milliseciond resolution. Dr Braun's group has used MEG to examine patterns of syntactic comprehension in language comprehension as an an application to speech and language disruptions.
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