Different pre-scanning instructions induce distinct psychological and resting brain states during functional magnetic resonance imaging

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters

Resting-state fMRI (rs-fMRI) scans-namely those lacking experimentally-controlled stimuli or cognitive demands-are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.

Two models of mind blanking

Mind blanking is a mental state in which attention does not bring any perceptual input into conscious awareness. As this state is still largely unexplored, we suggest that a comprehensive understanding of mind blanking can be achieved through a multifaceted approach combining self-assessment methods, neuroimaging and neuromodulation. In this article, we explain how electroencephalography and transcranial magnetic stimulation could be combined to help determine whether mind blanking is associated with a lack of mental content or a lack of linguistically or conceptually determinable mental content. We also question whether mind blanking occurs spontaneously or intentionally and whether these two forms are instantiated by the same or different neural correlates.

Whole-Brain Deactivations Precede Uninduced Mind-Blanking Reports

Mind-blanking (MB) is termed as the inability to report our immediate-past mental content. In contrast to mental states with reportable content, such as mind-wandering or sensory perceptions, the neural correlates of MB started getting elucidated only recently. A notable particularity that pertains to MB studies is the way MB is instructed for reporting, like by deliberately asking participants to "empty their minds." Such instructions were shown to induce fMRI activations in frontal brain regions, typically associated with metacognition and self-evaluative processes, suggesting that MB may be a result of intentional mental content suppression. Here, we aim at examining this hypothesis by determining the neural correlates of MB without induction. Using fMRI combined with experience-sampling in 31 participants (22 female), univariate analysis of MB reports revealed deactivations in occipital, frontal, parietal, and thalamic areas, but no activations in prefrontal regions. These findings were confirmed using Bayesian region-of-interest analysis on areas previously shown to be implicated in induced MB, where we report evidence for frontal deactivations during MB reports compared with other mental states. Contrast analysis between reports of MB and content-oriented mental states also revealed deactivations in the left angular gyrus. We propose that these effects characterize a neuronal profile of MB, where key thalamocortical nodes are unable to communicate and formulate reportable content. Collectively, we show that study instructions for MB lead to differential neural activation. These results provide mechanistic insights linked to the phenomenology of MB and point to the possibility of MB being expressed in different forms.SIGNIFICANCE STATEMENT This study explores how brain activity changes when individuals report unidentifiable thoughts, a phenomenon known as mind-blanking (MB). It aims to detect changes in brain activations and deactivations when MB is reported spontaneously, as opposed to the neural responses that have been previously reported when MB is induced. By means of brain imaging and experience-sampling, the study points to reduced brain activity in a wide number of regions, including those mesio-frontally which were previously detected as activated during induced MB. These results enhance our understanding of the complexity of spontaneous thinking and contribute to broader discussions on consciousness and reportable experience.

Thalamocortical Coherence Predicts Persistent Postconcussive Symptoms

The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.

Disrupted functional connectivity of the cerebellum with default mode and frontoparietal networks in young adults with major depressive disorder

Cerebellar dysconnectivity has repeatedly been documented in major depressive disorder (MDD). The cerebellum is composed of multiple functionally distinct subunits, and whether those subunits show similar or distinct dysconnectivity patterns with the cerebrum in MDD, is still unclear and needs to be further clarified. In this study, 91 MDD patients (23 male and 68 female) and 59 demographically matched healthy controls (22 male and 37 female) were enrolled to explore the cerebellar-cerebral dysconnectivity pattern in MDD by using the cutting-edge cerebellar partition atlas. Results showed that MDD patients exhibit decreased cerebellar connectivity with cerebral regions of default mode (DMN), frontoparietal networks (FPN), and visual areas. The dysconnectivity pattern was statistically similar across cerebellar subunits, with no significant diagnosis-by-subunit interactions. Correlation analyzes showed that cerebellar-dorsal lateral prefrontal cortex (DLPFC) connectivity is significantly correlated with anhedonia in MDD patients. Such dysconnectivity pattern was not affected by sex, which, however, should be further replicated in larger samples. These findings suggest a generalized disrupted cerebellar-cerebral connectivity pattern in MDD across all cerebellar subunits, which partially accounts for depressive symptoms in MDD, thus highlighting the pivotal role of the disrupted connectivity of cerebellum with DMN and FPN in the neuropathology of depression.

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

Mind blanking is a distinct mental state linked to a recurrent brain profile of globally positive connectivity during ongoing mentation

Mind blanking (MB) is a waking state during which we do not report any mental content. The phenomenology of MB challenges the view of a constantly thinking mind. Here, we comprehensively characterize the MB's neurobehavioral profile with the aim to delineate its role during ongoing mentation. Using functional MRI experience sampling, we show that the reportability of MB is less frequent, faster, and with lower transitional dynamics than other mental states, pointing to its role as a transient mental relay. Regarding its neural underpinnings, we observed higher global signal amplitude during MB reports, indicating a distinct physiological state. Using the time-varying functional connectome, we show that MB reports can be classified with high accuracy, suggesting that MB has a unique neural composition. Indeed, a pattern of global positive-phase coherence shows the highest similarity to the connectivity patterns associated with MB reports. We interpret this pattern's rigid signal architecture as hindering content reportability due to the brain's inability to differentiate signals in an informative way. Collectively, we show that MB has a unique neurobehavioral profile, indicating that nonreportable mental events can happen during wakefulness. Our results add to the characterization of spontaneous mentation and pave the way for more mechanistic investigations of MB's phenomenology.

Robustness of individualized inferences from longitudinal resting state EEG dynamics

Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification. We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-EEG was acquired over five consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision-rule, an individual could be re-identified from 2-second samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded-RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day vs same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of longitudinal RS for robust individualized inferences about neurophysiological change in health and disease.

Overview of (f)MRI Studies of Cognitive Aging for Non-Experts: Looking through the Lens of Neuroimaging

This special issue concerning Brain Functional and Structural Connectivity and Cognition aims to expand our understanding of brain connectivity. Herein, I review related topics including the principle and concepts of functional MRI, brain activation, and functional/structural connectivity in aging for uninitiated readers. Visuospatial attention, one of the well-studied functions in aging, is discussed from the perspective of neuroimaging.

Spontaneous and deliberate creative cognition during and after psilocybin exposure

Creativity is an essential cognitive ability linked to all areas of our everyday functioning. Thus, finding a way to enhance it is of broad interest. A large number of anecdotal reports suggest that the consumption of psychedelic drugs can enhance creative thinking; however, scientific evidence is lacking. Following a double-blind, placebo-controlled, parallel-group design, we demonstrated that psilocybin (0.17 mg/kg) induced a time- and construct-related differentiation of effects on creative thinking. Acutely, psilocybin increased ratings of (spontaneous) creative insights, while decreasing (deliberate) task-based creativity. Seven days after psilocybin, number of novel ideas increased. Furthermore, we utilized an ultrahigh field multimodal brain imaging approach, and found that acute and persisting effects were predicted by within- and between-network connectivity of the default mode network. Findings add some support to historical claims that psychedelics can influence aspects of the creative process, potentially indicating them as a tool to investigate creativity and subsequent underlying neural mechanisms. Trial NL6007; psilocybin as a tool for enhanced cognitive flexibility; https://www.trialregister.nl/trial/6007 .

Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia

Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.

EEG state-trajectory instability and speed reveal global rules of intrinsic spatiotemporal neural dynamics

Spatiotemporal dynamics of EEG/MEG (electro-/magneto-encephalogram) have typically been investigated by applying time-frequency decomposition and examining amplitude-amplitude, phase-phase, or phase-amplitude associations between combinations of frequency bands and scalp sites, primarily to identify neural correlates of behaviors and traits. Instead, we directly extracted global EEG spatiotemporal dynamics as trajectories of k-dimensional state vectors (k = the number of estimated current sources) to investigate potential global rules governing neural dynamics. We chose timescale-dependent measures of trajectory instability (approximately the 2^nd temporal derivative) and speed (approximately the 1^st temporal derivative) as state variables, that succinctly characterized trajectory forms. We compared trajectories across posterior, central, anterior, and lateral scalp regions as the current sources under those regions may serve distinct functions. We recorded EEG while participants rested with their eyes closed (likely engaged in spontaneous thoughts) to investigate intrinsic neural dynamics. Some potential global rules emerged. Time-averaged trajectory instability from all five regions tightly converged (with their variability minimized) at the level of generating nearly unconstrained but slightly conservative turns (~100° on average) on the timescale of ~25 ms, suggesting that spectral-amplitude profiles are globally adjusted to maintain this convergence. Further, within-frequency and cross-frequency phase relations appear to be independently coordinated to reduce average trajectory speed and increase the variability in trajectory speed and instability in a relatively timescale-invariant manner, and to make trajectories less oscillatory. Future research may investigate the functional relevance of these intrinsic global-dynamics rules by examining how they adjust to various sensory environments and task demands or remain invariant. The current results also provide macroscopic constraints for quantitative modeling of neural dynamics as the timescale dependencies of trajectory instability and speed are relatable to oscillatory dynamics.

The association of motivation with mind wandering in trait and state levels

Mind wandering (MW) is a phenomenon in which attention drifts away from task-related thoughts toward task-unrelated thoughts. Recent studies have demonstrated that MW occurs during tasks in which participants are unmotivated. However, motivation ranges on a continuum from trait to state. We examined the association between trait-state motivation and trait-state MW. Participants (176 undergraduate students 18–24 years old; 68 male) completed three questionnaires for our trait level investigation. State level indices were measured using the experience sampling method with 104 students completing a sustained attention to response task. Through correlation analyses, we demonstrated an association between motivation and MW within the same dimension (trait and state, respectively) but found no association across dimensions in which the correlation coefficient was nearly zero. We show the significant association between motivation and MW whose novelty is especially evident in the trait level. Although the relationship between motivation and MW is substantial, trait-state dimensionality would be important for them. The state MW is a phasic phenomenon driven by a range of factors, one being state motivation. The causality and confounding factors remain to be further studied.

The neural correlates of "mind blanking": When the mind goes away

Mind blanking (MB) is the state where our minds are seemingly "nowhere," and attention calls no perceptual input into conscious awareness. It is little investigated, perhaps partly because it is difficult to detect the mysterious periods of blanking. In this study, we found that our participants could intentionally produce a state of MB whose neural correlates were deactivation of Broca's area and parts of the default mode network (namely, the hippocampus) which would be active during mind wandering (MW), in addition to activity in another region in the default mode network (namely, anterior cingulate cortex). Because the behavioral finding replicates a previous report of ours, we suggest that the simple instructions that we used to induce MB should be effective. From the neuroimaging data, we conclude that we cannot define the content of our thoughts during MB because our inner speech system does not work at that time. Another possibility is that we actually think of nothing in the MB state. Although more sophisticated studies would be needed to uncover the mechanism of such a phenomenon, the present study provides a methodology and clues for understanding MB and related concepts such as MW, awareness, and metacognitive ability.

When to collect resting-state data: The influence of odor on post-task resting-state connectivity

The human brain networks at rest represent spontaneous activity that is highly correlated between different brain regions. Previous studies have shown that these resting-state networks are flexible and dynamic, and they can be affected by performance of different types of tasks. Moreover, it has been suggested that the re-activation of a task-related brain network during rest promotes learning and improves the expertise on that task. However, it is still unclear whether the presence of different sensory information in the on-task state affects functional connectivity in subsequent resting-state fMRI even though the perception of the sensory information did not induce significant behavioral effects. To clarify this issue, we compared pre- and post-task resting-state fMRI of two groups of participants performing the same task either with an odor context (ODOR group) or without an odor context (AIR group). Seed-based functional connectivity analyses were performed with orbitofrontal cortex, piriform cortex and working-memory core network as seeds. The results showed that an odor context presented during an encoding task induced significant changes in the functional connectivity only within the olfactory network of the post-task resting-state compared to the same post-task situation without previous odor context. No significant difference in functional connectivity were found for the working-memory core network. This evidence emphasizes how the sensory context, in which a task is performed, is relevant for understanding the observed changes of functional connectivity during rest.