In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13–30 Hz) and low-gamma bands (30–80 Hz), and with strongly preserved local synchrony in all bands.

This article utilizes social network analysis in addition to a measure of genre diversity to quantify the quality and capacity of actors in the Malay language film industry. We built a dataset by collecting data from various websites pertaining to Malay films. The data consists of 180 Malay films released from 2015 until 2020. The actor network is then built by connecting actors co-starring in a movie together and is compared to small world networks. We quantified the quality of actors in the network using five measures: number of films (TFA), degree centrality (DC), strength centrality (SC), betweenness centrality (BC), and normalized Herfindahl–Hirschman Index (NHHI). TFA, DC and SC indicate experience in the industry, since a high TFA shows that an actor has acted in more films. A high DC shows an actor has worked with many co-stars, and a high SC reflects an actor’s frequency of co-occurrence relationship. Actors with high TFA, DC, and SC are popular in this sense. Meanwhile, BC highlights the social importance of an actor in the network where they are the middlemen that connect actors from different genres of movies in the network, and we found that high BC actors are voice actors that may not have a high TFA, DC, or SC. NHHI highlights the actor’s capability to work with different types of film, and it serves as an important measure of an actor’s versatility. Moreover, we also calculated the average shortest path in the network to search for the “Kevin Bacon” of the Malay language film actor network. Using NHHI as an indicator of genre diversity, we also show that most of the actors diversify their work over the years and that genre diversity is an important benchmark for an actor.

Transformer models have reshaped natural language processing by using attention mechanisms to model relationships across sequences. Here, we adapt this architecture to brain imaging with Neurobiologically-Optimized Transformers (Neuro-BOTs)—a framework that embeds prior neurobiological knowledge directly into the model's attention layers. Rather than learning attention weights solely from data, Neuro-BOTs incorporate fixed spatial filters derived from brain maps—such as neurotransmitter distributions, mitochondrial density, or anatomical connectivity. These priors guide how the model attends to functional MRI features across brain regions. We evaluate Neuro-BOTs on three classification tasks using resting-state fMRI. In a Parkinson’s disease dataset, incorporating a noradrenergic filter improves classification accuracy from 71.3% to 89.7%, suggesting that early-stage noradrenergic dysfunction is a key discriminative signal. To assess specificity, we test the model on healthy ageing datasets—where no single biological system should dominate—and find no spurious performance gains across diverse filters and configurations. To assess sensitivity, we apply the model to two small datasets measuring LSD-induced brain responses where we hypothesised knowledge about the pharmacological profile of the drug (serotonergic priors) would improve classification despite limited sample size. These results show that embedding biologically meaningful priors into Transformer architectures enhances both accuracy and interpretability across a wide range of contexts and applications. More broadly, Neuro-BOTs provide a principled way to integrate multiscale brain knowledge into deep learning models, enabling new forms of biologically grounded inference in clinical prediction and cross-species translation.