Evidence is presented that a neurophysiologically-inspired mathematical model, originally developed for the generation of spontaneous EEG (electroencephalogram) activity, can produce VEP (visual evoked potential)-like waveforms when pulse-like signals serve as input. It was found that the simulated VEP activity was mainly due to intracortical excitatory connections rather than direct thalamic input. Also, the model-generated VEPs exhibited similar relationships between prestimulus EEG characteristics and subsequent VEP morphology, as seen in human data. Specifically, the large correlation between the N1 amplitude and the prestimulus alpha phase angle, and the insensitivity of P2 to the latter feature, as observed in actual VEPs to low intensity flashes, was also found in the model-generated data. These findings provide support for the hypothesis that the spontaneous EEG and the VEP are generated by some of the same neural structures and that the VEP is due to distributed activity, rather than dipolar sources.

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients.Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.

In a paired click "conditioning (S1), Testing (S2)" paradigm the amplitudes of responses to (S1) as well as the degree of attenuation of S2 as compared to S1 (S2/S1) were studied in two schizophrenic groups. Thirteen undifferentiated/disorganized (US) and thirteen paranoid (PS) patients were compared to thirteen age and sex matched normal volunteers. The US patients had significantly lower (S1) response amplitudes (P less than 0.001), as well as degree of attenuation of the response to (S2) (P less than 0.001) than the other two groups. No significant differences were found between the PS and N groups. Our data replicates the prior finding of decreased attenuation of the amplitude of the P50 wave in a paired click paradigm in schizophrenia. In addition, we showed that this phenomenon is significant mainly in the disorganized/undifferentiated patients as compared to the paranoid schizophrenics.