Electrophysiological and diffusion tensor imaging evidence of delayed corollary discharges in patients with schizophrenia

Patients with schizophrenia (SZ) characteristically exhibit supranormal levels of cortical activity to self-induced sensory stimuli, ostensibly because of abnormalities in the neural signals (corollary discharges, CDs) normatively involved in suppressing the sensory consequences of self-generated actions. The nature of these abnormalities is unknown. This study investigated whether SZ patients experience CDs that are abnormally delayed in their arrival at the sensory cortex. Twentyone patients with SZ and 25 matched control participants underwent electroencephalography (EEG). Participants’ level of cortical suppression was calculated as the amplitude of the N1 component evoked by a button press-elicited auditory stimulus, subtracted from the N1 amplitude evoked by the same stimulus presented passively. In the three experimental conditions, the auditory stimulus was delivered 0, 50 or 100 ms subsequent to the button-press. Fifteen SZ patients and 17 healthy controls (HCs) also underwent diffusion tensor imaging (DTI), and the fractional anisotropy (FA) of participants’ arcuate fasciculus was used to predict their level of cortical suppression in the three conditions. While the SZ patients exhibited subnormal N1 suppression to undelayed, self-generated auditory stimuli, these deficits were eliminated by imposing a 50-ms, but not a 100-ms, delay between the button-press and the evoked stimulus. Furthermore, the extent to which the 50-ms delay normalized a patient’s level of N1 suppression was linearly related to the FA of their arcuate fasciculus. These data suggest that SZ patients experience temporally delayed CDs to self-generated auditory stimuli, putatively because of structural damage to the white-matter (WM) fasciculus connecting the sites of discharge initiation and destination

Respiratory Motion Modelling Using cGANs

Respiratory motion models in radiotherapy are considered as one possible approach for tracking mobile tumours in the thorax and abdomen with the goal to ensure target coverage and dose conformation. We present a patient-specific motion modelling approach which combines navigator-based 4D MRI with recent developments in deformable image registration and deep neural networks. The proposed regression model based on conditional generative adversarial nets (cGANs) is trained to learn the relation between temporally related US and MR navigator images. Prior to treatment, simultaneous ultrasound (US) and 4D MRI data is acquired. During dose delivery, online US imaging is used as surrogate to predict complete 3D MR volumes of different respiration states ahead of time. Experimental validations on three volunteer lung datasets demonstrate the potential of the proposed model both in terms of qualitative and quantitative results, and computational time required