Time-resolved detection of stimulus/task-related networks, via clustering of transient intersubject synchronization

Functional Connectivity of the Brain Across Rodents and Humans

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.

Functional Imaging of Visuospatial Attention in Complex and Naturalistic Conditions

One of the ultimate goals of cognitive neuroscience is to understand how the brain works in the real world. Functional imaging with naturalistic stimuli provides us with the opportunity to study the brain in situations similar to the everyday life. This includes the processing of complex stimuli that can trigger many types of signals related both to the physical characteristics of the external input and to the internal knowledge that we have about natural objects and environments. In this chapter, I will first outline different types of stimuli that have been used in naturalistic imaging studies. These include static pictures, short video clips, full-length movies, and virtual reality, each comprising specific advantages and disadvantages. Next, I will turn to the main issue of visual-spatial orienting in naturalistic conditions and its neural substrates. I will discuss different classes of internal signals, related to objects, scene structure, and long-term memory. All of these, together with external signals about stimulus salience, have been found to modulate the activity and the connectivity of the frontoparietal attention networks. I will conclude by pointing out some promising future directions for functional imaging with naturalistic stimuli. Despite this field of research is still in its early days, I consider that it will play a major role in bridging the gap between standard laboratory paradigms and mechanisms of brain functioning in the real world.

Measuring shared responses across subjects using intersubject correlation

Our capacity to jointly represent information about the world underpins our social experience. By leveraging one individual's brain activity to model another's, we can measure shared information across brains-even in dynamic, naturalistic scenarios where an explicit response model may be unobtainable. Introducing experimental manipulations allows us to measure, for example, shared responses between speakers and listeners or between perception and recall. In this tutorial, we develop the logic of intersubject correlation (ISC) analysis and discuss the family of neuroscientific questions that stem from this approach. We also extend this logic to spatially distributed response patterns and functional network estimation. We provide a thorough and accessible treatment of methodological considerations specific to ISC analysis and outline best practices.

Co-activation patterns in resting-state fMRI signals

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns.