Here, we characterized the dynamics of sleep spindles, focusing on their damping, which we estimated using a metric called oscillatory-Quality (o-Quality), derived by fitting an autoregressive model to electrophysiological signals, recorded from the cortex in mice. The o-Quality of sleep spindles correlates weakly with their amplitude, shows marked laminar differences and regional topography across cortical regions, reflects the level of synchrony within and between cortical networks, is strongly modulated by sleep-wake history, reflects the degree of sensory disconnection, and correlates with the strength of coupling between spindles and slow waves. As most spindle events are highly localized and not detectable with conventional low-density recording approaches, o-Quality thus emerges as a valuable metric that allows us to infer the spread and dynamics of spindle activity across the brain and directly links their spatiotemporal dynamics with local and global regulation of brain states, sleep regulation, and function.

The aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy. Clinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated. A total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66). This study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency.

In classical Pavlovian conditioning, it is well-established that midbrain dopamine neurons respond to conditioned stimuli (CS) that predict a reward. However, how the dopamine neurons associate a neutral CS to a reward remains unknown. Here, we show that in male rats and in both male and female mice, the superior colliculus (SC) develops neuronal responses to a visual CS during conditioning, which in turn drive the responses of dopamine neurons. Visual responses in the SC were only potentiated when a behaviorally meaningful time interval separated the visual stimulus and reward. Potentiation also required the convergence of visual, dopamine and serotonin inputs to the SC. Importantly, blocking potentiation of the visual response was sufficient to suppress the dopamine responses following a CS. These results reveal a mechanism for how the brain forms associations between unconditioned stimuli and behaviorally meaningful visual information during classical conditioning.

Background/Objectives: Haemodynamic changes following ischaemic large-vessel occlusion (LVO) stroke might affect clinical outcome, including after endovascular recanalization. Using non-invasive quantitative MRA (qMRA), we report for the first time serial intracranial flow rate measurements following LVO and investigate flow rate changes, collateral pathway development, and their possible clinical significance. Methods: We report data from the prospective IMPreST study (Interplay of Microcirculation and Plasticity after Ischemic Stroke, registered at clinicaltrials.gov, no. NCT04035746). Patients with first-ever unilateral internal carotid artery (ICA) and/or M1/2 middle cerebral artery (MCA) occlusions were included. After being evaluated for gold-standard treatment, including endovascular thrombectomy, patients underwent early (<3 days) and late subacute (7 ± 3 days) qMRA flow measurements of the M1-MCA, A2-ACA (anterior cerebral artery), and P2-PCA (posterior cerebral artery) segments bilaterally. Results: Among 31 patients enrolled, 23 patients (17 recanalized, 6 non-recanalized) received both qMRA sessions. M1 volume flow rate (VFR) ratios (ischaemic/non-ischaemic hemisphere) in recanalized patients were symmetric (0.98–1.01) over time, while in non-recanalized patients M1 VFR ratios remained lower (0.74–0.77). P2 VFR ratios increased over time and were negatively correlated with M1 VFR ratios in late measurements (p = 0.016), possibly reflecting subacute activation of leptomeningeal collaterals via P2-PCA. In recanalized patients, lower M1 VFR ratios at 7 ± 3 days were significantly associated with a higher NIHSS at discharge after adjusting for infarct size, age, and NIHSS at admission (p = 0.02). Total hemispheric flow significantly decreased by up to 9% between early and late measurements in both the ischaemic and non-ischaemic hemispheres (p = 0.005). Conclusions: qMRA may help to understand flow status and collateral pathway activation after LVO stroke. Past the acute phase, low arterial flow to the affected region is associated with poorer neurological status, including after successful recanalization.