Classifying brain states and pupillary responses associated with the processing of old and new information.

Memory retrieval of consolidated memories has been extensively studied using "old-new tasks", meaning tasks in which participants are instructed to discriminate between stimuli they have experienced before and new ones. Significant differences in the neural processing of old and new elements have been demonstrated using different techniques, such as electroencephalography and pupillometry. In this work, using the data from a previously published study (Campos-Arteaga, Forcato et al. 2020), we investigated whether machine learning methods can classify, based on single trials, the brain activity and pupil responses associated with the processing of old and new information. Specifically, we used the EEG and pupillary information of 39 participants who completed an associative recall old-new task in which they had to discriminate between previously seen or new pictures and, for the old ones, to recall an associated word. Our analyses corroborated the differences in neural processing of old and new items reported in previous studies. Based on these results, we hypothesized that the application of machine learning methods would allow an optimal classification of old and new conditions. Using a Windowed Means approach (WM) and two different machine learning algorithms - Logistic Regression (WM-LR) and Linear Discriminant Analysis (WM-LDA) - mean classification performances of 0.75 and 0.74 (AUC) were achieved when EEG and pupillary signals were combined to train the models, respectively. In both cases, when the EEG and pupillary data were merged, the performance was significantly better than when they were used separately. In addition, our results showed similar classification performances when fused classification models (i.e., models created with the concatenated information of 38 participants) were applied to individuals whose EEG and pupillary information was not considered for the model training. Similar results were found when alternative preprocessing methods were used. Taken together, these findings show that it is possible to classify the neurophysiological activity associated with the processing of experienced and new stimuli using machine learning techniques. Future research is needed to determine how this knowledge might have potential implications for memory research and clinical practice.

Differential frontal alpha oscillations and mechanisms underlying loss of consciousness: a comparison between slow and fast propofol infusion rates.

Mechanisms underlying loss of consciousness following propofol administration remain incompletely understood. The objective of this study was to compare frontal lobe electroencephalography activity and brainstem reflexes during intravenous induction of general anaesthesia, in patients receiving a typical bolus dose (fast infusion) of propofol compared with a slower infusion rate. We sought to determine whether brainstem suppression ('bottom-up') predominates over loss of cortical function ('top-down'). Sixteen ASA physical status-1 patients were randomly assigned to either a fast or slow propofol infusion group. Loss of consciousness and brainstem reflexes were assessed every 30 s by a neurologist blinded to treatment allocation. We performed a multitaper spectral analysis of all electroencephalography data obtained from each participant. Brainstem reflexes were present in all eight patients in the slow infusion group, while being absent in all patients in the fast infusion group, at the moment of loss of consciousness (p = 0.010). An increase in alpha band power was observed before loss of consciousness only in participants allocated to the slow infusion group. Alpha band power emerged several minutes after the loss of consciousness in participants allocated to the fast infusion group. Our results show a predominance of 'bottom-up' mechanisms during fast infusion rates and 'top-down' mechanisms during slow infusion rates. The underlying mechanisms by which propofol induces loss of consciousness are potentially influenced by the speed of infusion.

Real-time fMRI neurofeedback: progress and challenges.

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Prediction of brain-computer interface aptitude from individual brain structure.

OBJECTIVE: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. METHODS: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. RESULTS: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). CONCLUSIONS: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. SIGNIFICANCE: This confirms that structural brain traits contribute to individual performance in BCI use.

Wilms' tumour 1 can suppress hTERT gene expression and telomerase activity in clear cell renal cell carcinoma via multiple pathways.

BACKGROUND: Wilms' tumour 1 (WT1) gene was discovered as a tumour suppressor gene. Later findings have suggested that WT1 also can be oncogenic. This complexity is partly explained by the fact that WT1 has a number of target genes. METHOD: WT1 and its target gene human telomerase reverse transcriptase (hTERT) were analysed in clear cell renal cell carcinoma (ccRCC). In vitro experiments were performed to examine the functional link between WT1 and hTERT by overexpression of WT1 isoforms in the ccRCC cell line, TK-10. RESULTS: WT1 demonstrated lower RNA expression in ccRCC compared with renal cortical tissue, whereas hTERT was increased, showing a negative correlation between WT1 and hTERT (P=0.005). These findings were experimentally confirmed in vitro. The WT1 generated effect on hTERT promoter activity seemed complex, as several negative regulators of hTERT transcription, such as SMAD3, JUN (AP-1) and ETS1, were activated by WT1 overexpression. Downregulation of potential positive hTERT regulators, such as cMyc, AP-2α, AP-2γ, IRF1, NFX1 and GM-CSF, were also observed. Chromatin immunoprecipitation analysis verified WT1 binding to the hTERT, cMyc and SMAD3 promoters. CONCLUSION: The collected data strongly indicate multiple pathways for hTERT regulation by WT1 in ccRCC.