RESUMO
Narratives can synchronize neural and physiological signals between individuals, but the relationship between these signals, and the underlying mechanism, is unclear. We hypothesized a top-down effect of cognition on arousal and predicted that auditory narratives will drive not only brain signals but also peripheral physiological signals. We find that auditory narratives entrained gaze variation, saccade initiation, pupil size, and heart rate. This is consistent with a top-down effect of cognition on autonomic function. We also hypothesized a bottom-up effect, whereby autonomic physiology affects arousal. Controlled breathing affected pupil size, and heart rate was entrained by controlled saccades. Additionally, fluctuations in heart rate preceded fluctuations of pupil size and brain signals. Gaze variation, pupil size, and heart rate were all associated with anterior-central brain signals. Together, these results suggest bidirectional causal effects between peripheral autonomic function and central brain circuits involved in the control of arousal.
Assuntos
Encéfalo , Frequência Cardíaca , Humanos , Encéfalo/fisiologia , Feminino , Masculino , Frequência Cardíaca/fisiologia , Adulto , Pupila/fisiologia , Adulto Jovem , Nível de Alerta/fisiologia , Percepção Auditiva/fisiologia , Movimentos Sacádicos/fisiologia , Cognição/fisiologia , Sistema Nervoso Autônomo/fisiologia , Estimulação AcústicaRESUMO
Deep neural networks have demonstrated promising performance in screening mammography with recent studies reporting performance at or above the level of trained radiologists on internal datasets. However, it remains unclear whether the performance of these trained models is robust and replicates across external datasets. In this study, we evaluate four state-of-the-art publicly available models using four publicly available mammography datasets (CBIS-DDSM, INbreast, CMMD, OMI-DB). Where test data was available, published results were replicated. The best-performing model, which achieved an area under the ROC curve (AUC) of 0.88 on internal data from NYU, achieved here an AUC of 0.9 on the external CMMD dataset (N = 826 exams). On the larger OMI-DB dataset (N = 11,440 exams), it achieved an AUC of 0.84 but did not match the performance of individual radiologists (at a specificity of 0.92, the sensitivity was 0.97 for the radiologist and 0.53 for the network for a 1-year follow-up). The network showed higher performance for in situ cancers, as opposed to invasive cancers. Among invasive cancers, it was relatively weaker at identifying asymmetries and was relatively stronger at identifying masses. The three other trained models that we evaluated all performed poorly on external datasets. Independent validation of trained models is an essential step to ensure safe and reliable use. Future progress in AI for mammography may depend on a concerted effort to make larger datasets publicly available that span multiple clinical sites.
RESUMO
BACKGROUND: Notwithstanding advances with low-intensity transcranial electrical stimulation (tES), there remain questions about the efficacy of clinically realistic electric fields on neuronal function. OBJECTIVE: To measure electric fields magnitude and their effects on neuronal firing rate of hippocampal neurons in freely moving rats, and to establish calibrated computational models of current flow. METHODS: Current flow models were calibrated on electric field measures in the motor cortex (n = 2 anesthetized rats) and hippocampus. A Neuropixels 2.0 probe with 384 channels was used in an in-vivo rat model of tES (n = 4 freely moving and 2 urethane anesthetized rats) to detect effects of weak fields on neuronal firing rate. High-density field mapping and computational models verified field intensity (1 V/m in hippocampus per 50 µA of applied skull currents). RESULTS: Electric fields of as low as 0.35 V/m (0.25-0.47) acutely modulated average firing rate in the hippocampus. At these intensities, firing rate effects increased monotonically with electric field intensity at a rate of 11.5 % per V/m (7.2-18.3). For the majority of excitatory neurons, firing increased for soma-depolarizing stimulation and diminished for soma-hyperpolarizing stimulation. While more diverse, the response of inhibitory neurons followed a similar pattern on average, likely as a result of excitatory drive. CONCLUSION: In awake animals, electric fields modulate spiking rate above levels previously observed in vitro. Firing rate effects are likely mediated by somatic polarization of pyramidal neurons. We recommend that all future rodent experiments directly measure electric fields to insure rigor and reproducibility.