Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

Over the past two decades, the study of resting-state functional magnetic resonance imaging (fMRI) has revealed the existence of multiple brain areas displaying synchronous functional blood oxygen level-dependent signals (BOLD) - resting-state networks (RSNs). The variation in functional connectivity between the different areas of a resting-state network or between multiple networks, have been extensively studied and linked to cognitive states and pathologies. However, the white matter connections supporting each network remain only partially described. In this work, we developed a data-driven method to systematically map the white and grey matter contributing to resting-state networks. Using the Human Connectome Project, we generated an atlas of 30 resting-state networks, each with two maps: white matter and grey matter. By integrating structural and functional neuroimaging data, this method builds an atlas that unlocks the joint anatomical exploration of white and grey matter to resting-state networks. The method also allows highlighting the overlap between networks, which revealed that most (89%) of the brain's white matter is shared amongst multiple networks, with 16% shared by at least 7 resting-state networks. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the correlations and the communication within resting-state networks. We provide an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage on RSNs.

Weaker connectivity in resting state networks is associated with disinhibited eating in older adults

Background/objectives Obesity affects more than forty percent of adults over the age of sixty. Aberrant eating styles such as disinhibition have been associated with the engagement of brain networks underlying executive functioning, attentional control, and interoception. However, these effects have been exclusively studied in young samples overlooking those most at risk of obesity related harm. Methods Here we assessed associations between resting-state functional connectivity and disinhibited eating (using the Three Factor Eating Questionnaire) in twenty-one younger (aged 19–34 years, BMI range: 18–31) and twenty older (aged 60–73 years, BMI range: 19–32) adults matched for BMI. The Alternative Healthy Eating Index was used to quantify diet quality. Results Older, compared to younger, individuals reported lower levels of disinhibited eating, consumed a healthier diet, and had weaker connectivity in the frontoparietal (FPN) and default mode (DMN) networks. In addition, associations between functional connectivity and eating behaviour differed between the two age groups. In older adults, disinhibited eating was associated with weaker connectivity in the FPN and DMN––effects that were absent in the younger sample. Importantly, these effects could not be explained by differences in habitual diet. Conclusions These findings point to a change in interoceptive signalling as part of the ageing process, which may contribute to behavioural changes in energy intake, and highlight the importance of studying this under researched population.

Abnormal Static and Dynamic Functional Network Connectivity in Patients With Presbycusis

Aim: This study aimed to investigate abnormal static and dynamic functional network connectivity (FNC) and its association with cognitive function in patients with presbycusis.Methods: In total, 60 patients with presbycusis and 60 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and cognitive assessments. Group independent component analysis (ICA) was carried out on the rs-fMRI data, and eight resting-state networks (RSNs) were identified. Static and dynamic FNCs (sFNC and dFNC) were then constructed to evaluate differences in RSN connectivity between the patients with presbycusis and the HCs. Furthermore, the correlations between these differences and cognitive scores were analyzed.Results: Patients with presbycusis had differences in sFNC compared with HCs, mainly reflected in decreased sFNC in the default mode network (DMN)-left frontoparietal network (LFPN) and attention network (AN)-cerebellum network (CN) pairs, but they had increased sFNC in the auditory network (AUN) between DMN domains. The decreased sFNC in the DMN-LFPN pair was negatively correlated with their TMT-B score (r = –0.441, p = 0.002). Patients with presbycusis exhibited aberrant dFNCs in State 2 and decreased dFNCs between the CN and AN and the visual network (VN). Moreover, the presbycusis group had a shorter mean dwell time (MDT) and fraction time (FT) in State 3 (p = 0.0027; p = 0.0031, respectively).Conclusion: This study highlighted differences in static and dynamic functional connectivity in patients with presbycusis and suggested that FNC may serve as an important biomarker of cognitive performance since abnormal alterations can better track cognitive impairment in presbycusis.

Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

Effective connectivity of LSD-induced ego dissolution

Classic psychedelic-induced ego dissolution involves a shift in the sense of self and blurring of boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated to the balance of anticorrelated activity between the default mode network (DMN) – which directs attention inwards – and the salience network (SN) – which recruits the dorsal attention network (DAN) to direct attention outward. To test whether change in anticorrelated networks underlie the peak effects of LSD, we applied dynamic causal modeling to infer effective connectivity of resting state functional MRI scans from a study of 25 healthy adults who were administered 100mg of LSD, or placebo. We found that change in inhibitory effective connectivity from the SN to DMN became excitatory, and inhibitory effective connectivity from DMN to DAN decreased under the peak effect of LSD. These changes in connectivity reflect diminution of the anticorrelation between resting state networks that may be a key neural mechanism of LSD-induced ego dissolution. Our findings suggest the hierarchically organised balance of resting state networks is a central feature in the construct of self.SignificanceThe findings can inform the parallel between the maintenance of subject-object boundary and changes to anticorrelated canonical resting state brain networks. Effective connectivity informs the hierarchical organisation of brain networks underlying modes of perception. Moreover, the anticorrelation of brain networks is an important measure of mental function. Understanding the neural mechanisms of anticorrelation change under psychedelics help identify its relationship to psychosis and its association to psychedelic assisted therapeutic outcomes.

Canonical EEG Microstates Transitions Reflect Switching Among BOLD Resting State Networks and Predict fMRI Signal

Objective. Electroencephalography microstates (EEG-ms), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. EEG-ms topographies are quasi-stable lasting between 60-120 milliseconds. Some evidence suggests that EEG-ms are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of EEG-ms and their association with functional MRI (fMRI) remains unclear. Approach. In a large cohort of healthy subjects (n = 52), we conducted several statistical and machine learning approaches analyses on the association among EEG-ms spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and machine learning approaches. Main results. Our results using a generalized linear model unraveled that EEG-ms transitions were largely and negatively associated with blood-oxygenation-level dependent (BOLD) signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between EEG-ms transitioning and fMRI signal while revealing that EEG-ms dynamics can predict BOLD signals and vice versa. Significance. Results suggest that EEG-ms transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities can measure common aspects of undergoing brain neuronal activities. Moreover, our results may help to better understand the electrophysiological interpretation of EEG-ms and solve several contradicting findings in the literature.

Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs

Epilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.

Increased excursions to functional networks in schizophrenia in the absence of task

Brain activity during rest has been demonstrated to evolve through a repertoire of functional connectivity (FC) patterns, whose alterations may provide biomarkers of schizophrenia - a psychotic disorder characterized by dysfunctional brain connectivity. In this study, differences between the dynamic exploration of resting-state networks using functional magnetic resonance imaging (fMRI) data from 71 schizophrenia patients and 74 healthy controls were investigated using a method focusing on the dominant fMRI signal phase coherence pattern at each time point. Through the lens of dynamical systems theory, brain activity in the form of temporal FC state trajectories was examined for intergroup differences by calculating the fractional occupancy, dwell time, limiting probability of each state and the transition probabilities between states. Results showed reduced fractional occupancy of a globally synchronized state in schizophrenia. Conversely, FC states overlapping with canonical functional subsystems exhibited increased fractional occupancy and limiting probability in schizophrenia. Furthermore, state-to-state transition probabilities were altered in schizophrenia. This revealed a reduced probability of remaining in a global integrative state, increased probability of switching from this state to functionally meaningful networks and reduced probability of remaining in a state related to the Default Mode network. These results revealed medium to large effect sizes. Finally, this study showed that using K-medoids clustering did not influence the observed intergroup differences - highlighting the utility of dynamical systems theory to better understand brain activity. Combined, these findings expose pronounced differences between schizophrenia patients and healthy controls - supporting and extending current knowledge regarding disrupted brain dynamics in schizophrenia.