To evaluate the role of molecular genetics in the routine clinic, we investigated allelic imbalance at 1p36, 19q13, 17p13, 10p12-15, and 10q22-26 and p53 mutation in 100 oligodendroglial neoplasms diagnosed at a single treatment center between 2000 and 2003. The -1p/-19q genotype, seen in 64, 34, 77, and 30% of OII, OAII, OIII, and OAIII respectively, was inversely related to p53 mutation and 17p13 loss. Genotype was unrelated to tumor location and could not distinguish high-grade tumors that presented de novo from those that progressed from a previous lower grade malignancy. Presentation with seizures was more common in cases with the -1p/-19q genotype, and these remained stable for longer before treatment. In longitudinal samples, 74% retained their initial histological differentiation, whereas 29% showed new genetic alterations, the -1p/-19q genotype being acquired in three cases. Loss of 1p36 and 19q13, 17p13, chromosome 10, and p53 mutation were significantly associated with survival from presentation in Kaplan-Meier analysis (p < 0.01), and loss of 1p36 and 19q13 and loss of 17p13 retained significance in multivariate analysis. In this recently diagnosed unselected series, clinical differences in tumors with and without the -1p/-19q genotype support a genetic approach to aid diagnosis and prognostication for oligodendroglial neoplasms.
The Computerised Multiple Elements Test (CMET) is a novel executive task to assess goal management and maintenance suitable for use within the fMRI environment. Unlike classical executive paradigms, it resembles neuropsychological multi-elements tests that capture goal management in a more ecological way, by requiring the participant to switch between four simple games within a specified time period. The present study aims to evaluate an fMRI version of the CMET and examine its brain correlates. Thirty-one healthy participants performed the task during fMRI scanning. During each block, they were required to play four simple games, with the transition between games being made either voluntarily (executive condition) or automatically (control condition). The executive condition was associated with increased activity in fronto-parietal and cingulo-opercular regions, with anterior insula activity linked to better task performance. In an additional analysis, the activated regions showed to form functional networks during resting-state and to overlap the executive fronto-parietal and cingulo-opercular networks identified in resting-state with independently defined seeds. These results show the ability of the CMET to elicit activity in well-known executive networks, becoming a potential tool for the study of executive impairment in neurological and neuropsychiatric populations in a more ecological way than classical paradigms.
AimsDifferences in static and dynamic resting-state functional connectivity have been identified in patients with schizophrenia, individuals at high risk of psychosis and those with psychotic-like experiences. Analysis of dynamic connectivity is important to understand the temporal fluctuations in functional connectivity. Studies of dynamic functional connectivity have been conducted using methods such as the sliding-window technique and co-activation patterns (CAPs). In this study edge-centric analysis has been used to examine differences in time-varying connectivity in patients with schizophrenia compared to healthy controls. This method overcomes some of the limitations of other methods as it has higher temporal resolution and unwraps the data without applying additional modelling or requiring user-defined specification of parameters.MethodsWe analysed resting-state fMRI data from 67 patients with schizophrenia and 81 healthy controls using the Center for Biomedical Research Excellence (COBRE) dataset. The edge-time series for these subjects was calculated by omitting the averaging step when calculating the correlation between time series at each node. This effectively unwrapped the functional connectivity correlations and produced a measure of cofluctuation at each timeframe. The edge time series can be aggregated into a single measure of dynamic whole brain co-fluctuation by calculating the root sum square (RSS). We analysed the frequency and amplitude of the high amplitude peaks of cofluctuation and the patterns of activity seen during peaks and troughs.ResultsThe results showed that mean peak amplitude was lower in patients with schizophrenia compared to controls (t-stat= -3.13, p = 0.0021). Patients with schizophrenia also had significantly less frequent peaks (t-stat= -2.80, p = 0.0058).The pattern of activation at peaks in controls was more homogenous between control subjects compared to patients with schizophrenia. We identified networks that were significantly less activated in patients than in controls during peaks, troughs and transitionary time points.ConclusionThis study suggests that in patients with schizophrenia the whole brain cofluctuations during resting-state are less frequent and lower in amplitude. This is in keeping with previous studies which have identified that patients with schizophrenia spend significantly less time than healthy controls in states of large-scale connectivity. Further studies looking at larger transdiagnostic samples and antipsychotic naïve patients will be important to build on these results.
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