Community-based studies suggest that cannabis products that are high in Δ⁹-tetrahydrocannabinol (THC) but low in cannabidiol (CBD) are particularly hazardous for mental health. Laboratory-based studies are ideal for clarifying this issue because THC and CBD can be administered in pure form, under controlled conditions. In a between-subjects design, we tested the hypothesis that pre-treatment with CBD inhibited THC-elicited psychosis and cognitive impairment. Healthy participants were randomised to receive oral CBD 600 mg (n=22) or placebo (n=26), 210 min ahead of intravenous (IV) THC (1.5 mg). Post-THC, there were lower PANSS positive scores in the CBD group, but this did not reach statistical significance. However, clinically significant positive psychotic symptoms (defined a priori as increases ≥ 3 points) were less likely in the CBD group compared with the placebo group, odds ratio (OR)=0.22 (χ²=4.74, p<0.05). In agreement, post-THC paranoia, as rated with the State Social Paranoia Scale (SSPS), was less in the CBD group compared with the placebo group (t=2.28, p<0.05). Episodic memory, indexed by scores on the Hopkins Verbal Learning Task-revised (HVLT-R), was poorer, relative to baseline, in the placebo pre-treated group (-10.6 ± 18.9%) compared with the CBD group (-0.4% ± 9.7 %) (t=2.39, p<0.05). These findings support the idea that high-THC/low-CBD cannabis products are associated with increased risks for mental health.

The main ingredient in cannabis, Δ(9)-tetrahydrocannabinol (THC), can elicit acute psychotic reactions in healthy individuals and precipitate relapse in schizophrenic patients. However, the neural mechanism of this is unknown. We tested the hypothesis that THC psychopathology is related to changes in electroencephalography (EEG) power or inter-regional coherence. In a within-subjects design, participants (n=16) were given intravenous THC (1.25 mg) or placebo under double-blind conditions, during EEG recordings. Using fast-Fourier transform, EEG data were analyzed for power and coherence in the delta (1-3.5 Hz), theta (3.5-7 Hz), alpha (8-13 Hz), beta (14-25 Hz), low-gamma (30-40 Hz), and high-gamma (60-70 Hz) bands during engagement in the n-back test of working memory (WM). Compared with placebo, THC evoked positive and negative psychotic symptoms, as measured by the positive and negative syndrome scale (p<0.001) and slowed WM performance (p<0.05). Under THC, theta power was specifically reduced, (p<0.001) regardless of WM load; however, the reduction showed no relationship with psychotic symptoms or WM impairment. Coherence between bi-frontal electrodes in the theta band was also reduced by THC (p<0.05) and these reductions correlated with the change-in positive psychotic symptoms (rho=0.79, p<0.001). Bi-frontal specificity was suggested by the absence of a relationship between psychotic symptoms and fronto-parietal coherence. The results reveal that the pro-psychotic effects of THC might be related to impaired network dynamics with impaired communication between the right and left frontal lobes.

Abnormal gamma oscillations, measured by electroencephalography (EEG), have been associated with chronic psychotic disorders, but their prevalence in the early phase of psychosis is less clear. We sought to address this by systematically reviewing the relevant literature. We searched for EEG studies of gamma band oscillations in subjects at high risk for psychosis and in patients with first episode psychosis. The following measures of gamma oscillations were extracted: resting power, evoked power, induced power, connectivity and peak frequency. Forty-five studies with a total of 3099 participants were included. There were potential sources of bias in the study designs and potential artefacts. Although there were few consistent findings, several studies reported decreased evoked or induced power in both high risk subjects and first episode patients. Studies using larger samples with serial EEG measurements, and designs that minimise artefacts that occur at the gamma frequency may advance work in this area.

BackgroundFinding reliable endophenotypes for psychosis could lead to an improved understanding of aetiology, and provide useful alternative phenotypes for genetic association studies. Resting quantitative electroencephalography (QEEG) activity has been shown to be heritable and reliable over time. However, QEEG research in patients with psychosis has shown inconsistent and even contradictory findings, and studies of at-risk populations are scarce. Hence, this study aimed to investigate whether resting QEEG activity represents a candidate endophenotype for psychosis.MethodQEEG activity at rest was compared in four frequency bands (delta, theta, alpha, and beta), between chronic patients with psychosis (N = 48), first episode patients (N = 46), at-risk populations (“at risk mental state”, N = 33; healthy relatives of patients, N = 45), and healthy controls (N = 107).ResultsResults showed that chronic patients had significantly increased resting QEEG amplitudes in delta and theta frequencies compared to healthy controls. However, first episode patients and at-risk populations did not differ from controls in these frequency bands. There were no group differences in alpha or beta frequency bands.ConclusionSince no abnormalities were found in first episode patients, ARMS, or healthy relatives, resting QEEG activity in the frequency bands examined is unlikely to be related to genetic predisposition to psychosis. Rather than endophenotypes, the low frequency abnormalities observed in chronic patients are probably related to illness progression and/or to the long-term effects of treatments.

Neural oscillations in the gamma band are of increasing interest, but separating them from myogenic electrical activity has proved difficult. A novel algorithm has been developed to reduce the effect of tonic scalp and neck muscle activity on the gamma band of the EEG. This uses mathematical modelling to fit individual muscle spikes and then subtracts them from the data. The method was applied to the detection of motor associated gamma in two separate groups of eight subjects using different sampling rates. A reproducible increase in high gamma (65–85 Hz) magnitude occurred immediately after the motor action in the left central area (p = 0.02 and p = 0.0002 for the two cohorts with individually optimized algorithm parameters, compared to p = 0.03 and p = 0.16 before correction). Whilst the magnitude of this event-related gamma synchronisation was not reduced by the application of the EMG reduction algorithm, the baseline left central gamma magnitude was significantly reduced by an average of 23 % with a faster sampling rate (p < 0.05). In comparison, at left and right temporo-parietal locations the gamma amplitude was reduced by 60 and 54 % respectively (p < 0.05). The reduction of EMG contamination by fitting and subtraction of individual spikes shows promise as a method of improving the signal to noise ratio of high frequency neural oscillations in scalp EEG.

In this paper, a novel source extraction method is proposed for removing ballistocardiogram (BCG) artifact from EEG. BCG appears in EEG signals recorded simultaneously with functional magnetic resonance imaging. The proposed method is a semiblind source extraction algorithm based on linear prediction technique. We define a cost function according to a joint short- and long-term prediction strategy to extract the BCG sources. We call this method SLTP-BSE standing for short- and long-term prediction blind source extraction. The objective of this work is to 1) model the temporal structure of the sources using short-term prediction and 2) impose the prior information about the BCG sources using long-term prediction. These two procedures are simultaneously implemented to optimize the system. The performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to remove the BCG artifact while preserving the task-related parts of the signal. The results of SLTP-BSE are compared with those of well-known BCG removal techniques confirming the superiority of the proposed method.

Gamma oscillations (>30 Hz) in the brain are involved in attention, perception and memory. They are altered in various pathological states, as well as by neuropharmaceuticals, so that they are of interest in drug and clinical investigations. However, when the human electroencephalogram is recorded on the scalp, this neural high-frequency signal is buried under a range of other electrical signals such that, without careful handling, recordings of the high-frequency electroencephalogram cannot be considered reliable. The artefacts of concern originate from: power line noise, saccade-associated contraction of the extra-ocular muscles, activity of muscles in the scalp, face and neck, screen refresh artefacts and activity of the muscles associated with blinking. Recent progress in dealing with these artefacts is described, including either noise cancellation or phased noise template subtraction for power line noise, regression or independent component analysis for correcting extra-ocular muscle activity and mathematical modelling for reducing scalp, face and neck muscle activity. If the artefacts are properly addressed, the neural gamma signal can be uncovered.

Gamma is an important frequency band of the electroencephalogram (EEG), but its study has been impaired by problems with artefacts. This paper focuses on the artefacts caused by contraction of the extra-ocular muscles at the start of a saccade, which produces spurious gamma oscillations in the EEG. An algorithm was written and tested which detects and reduces the effect of this artefact. It involves novel adaptations of standard regression techniques which have traditionally been used to reduce blink artefacts, so as to render them applicable to the gamma band ocular artefact. Before the algorithm can be applied any power-line noise must be removed by noise cancellation and not notch filtering. The sharp, broadband gamma peak at around 200 ms was substantially reduced by the algorithm in all three subjects tested. However, there may be lower amplitude, task related, modulations in gamma which are uncovered when the artefact is reduced. The amplitude of the artefact had its largest positive value at the most anterior electrodes and its largest negative value at midline central and parietal electrodes, and these two sets of locations also showed the greatest reductions in gamma band magnitude after application of the algorithm. This study demonstrates the feasibility of reducing the saccade linked gamma band artefact.