Semi-supervised Training Data Selection Improves Seizure Forecasting in Canines with Epilepsy.

OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. METHODS: We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. RESULTS: In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. CONCLUSION: Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. SIGNIFICANCE: The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.

A pilot study of GABAB correlates with resting-state functional connectivity in five depressed female adolescents.

Connectivity features based on resting-state (RS) functional magnetic resonance imaging (fMRI) demonstrate great promise as biomarkers to guide diagnosis and treatment in major depressive disorder (MDD). However, there is a pressing need for valid, reliable biomarkers closer to the bedside for clinical research and practice. This study directly compared RS-fMRI connectivity features with transcranial magnetic stimulation (TMS) neurophysiological measures, long interval cortical inhibition (LICI) and cortical silent period (CSP), in female adolescents with MDD. LICI-200 showed the most significant associations with RS functional connectivity features, demonstrating its potential to evaluate the neurochemical underpinnings of network features in MDD.

The neural basis of a deficit in abstract thinking in patients with schizophrenia.

Abnormal abstract thinking is a major cause of social dysfunction in patients with schizophrenia, but little is known about its neural basis. In this study, we aimed to determine the characteristic abstract thinking-related brain responses in patients using a task reflecting social situations. We conducted functional magnetic resonance imaging while 16 patients with schizophrenia and 16 healthy controls performed a theme-identification task, in which various emotional pictures depicting social situations were presented. Compared with healthy controls, the patients showed significantly decreased activity in the left frontopolar and right orbitofrontal cortices during theme identification. Activity in these two regions correlated well in the controls, but not in patients. Instead, the patients exhibited a close correlation between activity in both sides of the frontopolar cortex, and a positive correlation between the right orbitofrontal cortex activity and degrees of theme identification. Reduced activity in the left frontopolar and right orbitofrontal cortices and the underlying aberrant connectivity may be implicated in the patients' deficits in abstract thinking. These newly identified features of the neural basis of abnormal abstract thinking are important as they have implications for the impaired social behavior of patients with schizophrenia during real-life situations.