Temporal instability of salience network activity in migraine with aura.

This study aims to investigate whether intra-network dynamic functional connectivity and causal interactions of the salience network is altered in the interictal term of migraine. 32 healthy controls, 37 migraineurs without aura and 20 migraineurs with aura were recruited. Participants underwent a T1-weighted scan and resting-state fMRI protocol inside a 1.5T MR scanner. We obtained average spatial maps of resting-state networks using group independent component analysis, which yielded subject-specific time series via a dual regression approach. Salience network ROIs (bilateral insulae and prefrontal cortices, dorsal anterior cingulate cortex) were obtained from the group average map via cluster-based thresholding. To describe intra-network connectivity, average and dynamic conditional correlation was calculated. Causal interactions between the default-mode, dorsal attention and salience network were characterised by spectral Granger's causality. Time-averaged correlation was lower between the right insula and prefrontal cortex in migraine without aura vs. with aura and healthy controls (p<0.038, p<0.037). Variance of dynamic conditional correlation was higher in migraine with aura vs. healthy controls and migraine with aura vs. without aura between the right insula and dorsal anterior cingulate cortex (p<0.011, p<0.026), and in migraine with aura vs. healthy controls between the dorsal anterior cingulate and left prefrontal cortex (p<0.021). Causality was weaker in the <0.05 Hz frequency range between the salience and dorsal attention networks in migraine with aura (p<0.032). Overall, migraineurs with aura exhibit more fluctuating connections in the salience network, which also affect network interactions, and could be connected to altered cortical excitability and increased sensory gain

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures

The Acute Effects of the Atypical Dissociative Hallucinogen Salvinorin A on Functional Connectivity in the Human Brain

Salvinorin A (SA) is a κ-opioid receptor agonist and atypical dissociative hallucinogen found in Salvia divinorum. Despite the resurgence of hallucinogen studies, the effects of κ-opioid agonists on human brain function are not well-understood. This placebo-controlled, within-subject study used functional magnetic resonance imaging for the first time to explore the effects of inhaled SA on strength, variability, and entropy of functional connectivity (static, dynamic, and entropic functional connectivity, respectively, or sFC, dFC, and eFC). SA tended to decrease within-network sFC but increase between-network sFC, with the most prominent effect being attenuation of the default mode network (DMN) during the first half of a 20-min scan (i.e., during peak effects). SA reduced brainwide dFC but increased brainwide eFC, though only the former effect survived multiple comparison corrections. Finally, using connectome-based classification, most models trained on dFC network interactions could accurately classify the first half of SA scans. In contrast, few models trained on within- or between-network sFC and eFC performed above chance. Notably, models trained on within-DMN sFC and eFC performed better than models trained on other network interactions. This pattern of SA effects on human brain function is strikingly similar to that of other hallucinogens, necessitating studies of direct comparisons

Connectivity dynamics from wakefulness to sleep

Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 ​s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.Fil: Damaraju, Eswar. Instituto Tecnológico de Georgia; Estados UnidosFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Laufs, Helmut. Goethe Universitat Frankfurt; AlemaniaFil: Calhoun, Vince D.. Instituto Tecnológico de Georgia; Estados Unido

Why is Clinical fMRI in a Resting State?

While resting state fMRI (rs-fMRI) has gained widespread application in neuroimaging clinical research, its penetration into clinical medicine has been more limited. We surveyed a neuroradiology professional group to ascertain their experience with rs-fMRI, identify perceived barriers to using rs-fMRI clinically and elicit suggestions about ways to facilitate its use in clinical practice. The electronic survey also collected information about demographics and work environment using Likert scales. We found that 90% of the respondents had adequate equipment to conduct rs-fMRI and 82% found rs-fMRI data easy to collect. Fifty-nine percent have used rs-fMRI in their past research and 72% reported plans to use rs-fMRI for research in the next year. Nevertheless, only 40% plan to use rs-fMRI in clinical practice in the next year and 82% agreed that their clinical fMRI use is largely confined to pre-surgical planning applications. To explore the reasons for the persistent low utilization of rs-fMRI in clinical applications, we identified barriers to clinical rs-fMRI use related to the availability of robust denoising procedures, single-subject analysis techniques, demonstration of functional connectivity map reliability, regulatory clearance, reimbursement, and neuroradiologist training opportunities. In conclusion, while rs-fMRI use in clinical neuroradiology practice is limited, enthusiasm appears to be quite high and there are several possible avenues in which further research and development may facilitate its penetration into clinical practice

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli

One of human brain’s remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain’s dynamic changes during the processing of visual and auditory stimuli