This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG). Specifically, we describe a factor-graph-based model with customized factor-functions defined based on domain knowledge, to infer pathologic brain activity with the goal of identifying seizure-generating brain regions in epilepsy patients. We utilize an inference technique based on the graph-cut algorithm to exactly solve graph inference in polynomial time. We validate the model by using clinically collected intracranial EEG data from 29 epilepsy patients to show that the model correctly identifies seizure-generating brain regions. Our results indicate that our model outperforms two conventional approaches used for seizure-onset localization (5-7% better AUC: 0.72, 0.67, 0.65) and that the proposed inference technique provides 3-10% gain in AUC (0.72, 0.62, 0.69) compared to sampling-based alternatives.

Prolonged grief disorder affects around 1 in 20 people, and we're starting to understand the neuroscience behind it For most people, the intense sting of grief eases with time. For some, however, persistent and painful grief remains, developing into prolonged grief disorder. A new review of the condition, which affects around 5 per cent of bereaved people, sheds light on how it develops. This could help doctors predict which recently bereaved people will benefit from extra support. The decision to include prolonged grief disorder (PGD) in the American Psychiatric Association's diagnostic manual in 2022 sparked intense debate over whether it was pathologising a normal human response to loss and imposing an arbitrary timeline on what constitutes "normal" grief.

Our visual experience in daily life are dominated by dynamic change.

However, in practice, new reconstruction methods could improve performance for at least three other reasons: learning more about the distribution of stimuli, becoming better at reconstructing text or images in general, or exploiting weaknesses in current image and/or text evaluation metrics. Here we disentangle how much of the reconstruction is due to these other factors vs. productively using the neural recordings.

Weusebrainimagingrecordings ofsubjectsreading complex natural text to interpret word and sequence embeddings from4 recent NLP models - ELMo, USE, BERT and Transformer-XL. We study how their representations differ across layer depth, contextlength, and attention type.

New research reinforces that it's a mind-altering, dynamic state that promotes focus, learning, and well-being. If you've ever considered practicing meditation, you might believe you should relax, breathe, and empty your mind of distracting thoughts. Novices tend to think of meditation as the brain at rest, but a new international study concludes that this ancient practice is quite the opposite: Meditation is a state of heightened cerebral activity that profoundly alters brain dynamics. Researchers from the University of Montreal and Italy's National Research Council recruited 12 monks of the Thai Forest Tradition at Santacittārāma, a Buddhist monastery outside Rome. In a laboratory in Chieti-Pescara, scientists analyzed the brain activity of these meditation practitioners using magnetoencephalography (MEG), technology capable of recording with great precision the brain's electrical signals.

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