Dynamic functional connectivity of the default mode network tracks daydreaming

Functional brain network dynamics of brooding in depression: Insights from real-time fMRI neurofeedback

BACKGROUND

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

METHODS

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n = 18/18, HC-active/sham: n = 13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

RESULTS

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r = -0.4, p = 0.002, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z = -2.09, FWE-p = 0.034).

LIMITATIONS

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

CONCLUSION

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Brain dynamics alterations induced by partial sleep deprivation: An energy landscape study

Partial sleep deprivation (PSD) alters neural activity of intrinsic brain networks involved in cognitive functions. However, the age-related time-varying properties of large-scale brain functional networks after PSD remain unknown. Our study applied energy landscape analysis to resting-state functional magnetic resonance imaging data to characterize the dominant brain activity patterns in 36 healthy young (19 females, 23.53 ± 2.36 years) and 33 healthy older (18 females, 68.81 ± 2.41 years) adults after full sleep (FS) and PSD. Dynamic properties of these patterns, including appearance probability, duration and transitions, were then calculated. Finally, a 105 steps numerical simulation was performed on each energy landscape. We found that the energy landscapes of the younger and older groups had similar hierarchical structures, including two major states and two minor states. The two major states showed complementary spontaneous activation patterns. But the PSD has altered the temporal evolution of these major brain states in younger participants, manifested by significantly higher appearance frequency of the major states and the direct transitions between major states than FS. These changes were not significant in older participants. Additionally, the weaker functional segregation between two modules assigned by two complementary major states was found during PSD than FS in young group. We further demonstrated that such abnormal brain network functional coordination was associated with the atypical brain dynamics and behaviors. These findings suggested a low-dimensional and restricted dynamic landscape of brain activity in young adults after PSD and provided new insight into understand the neural effects of PSD.

Intranetwork and Internetwork Functional Connectivity Changes Related to Speech Disorders in Adults With Cleft Lip and Palate

Cleft lip and palate (CLP) may induce alterations in functional connectivity (FC) throughout the whole brain, potentially leading to speech dysfunctions; however, the precise neurobiological mechanisms involved remain unknown. This study aimed to systematically examine the consequences of neurological impairments associated with CLP on whole-brain FC and speech functionality. A total of 33 CLP individuals and 41 control participants were included in this study. Eight meaningful brain networks were identified through independent component analysis (ICA). The intergroup differences and correlations with speech scores for both intranetwork and internetwork FC were calculated. We observed decreased FC within the sensorimotor network (SMN), default mode network (DMN), and cerebellar network (CN) and increased FC within the executive control network (ECN). Additionally, FC was enhanced between the SMN and the auditory network (AN), attention network (ATN), and salience network (SAN); between the DMN and the visual network (VN) and ECN; and between two independent components of the DMN. Furthermore, significant correlations were observed between altered FC and speech assessment scores. Our research demonstrated that brain plasticity in CLP individuals with speech deficits involves widespread changes in brain connectivity, significantly improving our understanding of the neural basis of speech impairment in CLP individuals.

Functional brain networks predicting sustained attention are not specific to perceptual modality

Sustained attention is essential for daily life and can be directed to information from different perceptual modalities, including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study, we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults' visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N 1 = 29, N 2 = 60, N 3 = 17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.

Comparative Effects of Temporal Interference and High-Definition Transcranial Direct Current Stimulation on Spontaneous Neuronal Activity in the Primary Motor Cortex: A Randomized Crossover Study

Background: Modulating spontaneous neuronal activity is critical for understanding and potentially treating neurological disorders, yet the comparative effects of different non-invasive brain stimulation techniques remain underexplored. Objective: This study aimed to systematically compare the effects of temporal interference (TI) stimulation and high-definition transcranial direct current stimulation (HD-tDCS) on spontaneous neuronal activity in the primary motor cortex. Methods: In a randomized, crossover design, forty right-handed participants underwent two 20 min sessions of either TI or HD-tDCS. Resting-state fMRI data were collected at four stages: pre-stimulus baseline (S1), first half of stimulation (S2), second half of stimulation (S3), and post-stimulation (S4). We analyzed changes in regional homogeneity (ReHo), dynamic ReHo (dReHo), fractional amplitude of low-frequency fluctuations (fALFFs), and dynamic fALFFs (dfALFFs) to assess the impact on spontaneous neuronal activity. Results: The analysis revealed that TI had a more significant impact on ReHo, especially in the left superior temporal gyrus and postcentral gyrus, compared with HD-tDCS. Both stimulation methods exhibited their strongest effects during the second half of the stimulation period, but only TI maintained significant activity in the post-stimulation phase. Additionally, both TI and HD-tDCS enhanced fALFFs in real-time, with TI showing more pronounced effects in sensorimotor regions. Conclusions: These findings suggest that TI exerts a more potent and sustained influence on spontaneous neuronal activity than HD-tDCS. This enhanced understanding of their differential effects provides valuable insights for optimizing non-invasive brain stimulation protocols for therapeutic applications.

Dynamic brain states underlying advanced concentrative absorption meditation: A 7-T fMRI-intensive case study

Advanced meditation consists of states and stages of practice that unfold with mastery and time. Dynamic functional connectivity (DFC) analysis of fMRI could identify brain states underlying advanced meditation. We conducted an intensive DFC case study of a meditator who completed 27 runs of jhāna advanced absorptive concentration meditation (ACAM-J), concurrently with 7-T fMRI and phenomenological reporting. We identified three brain states that marked differences between ACAM-J and nonmeditative control conditions. These states were characterized as a DMN-anticorrelated brain state, a hyperconnected brain state, and a sparsely connected brain state. Our analyses indicate higher prevalence of the DMN-anticorrelated brain state during ACAM-J than control states, and the prevalence increased significantly with deeper ACAM-J states. The hyperconnected brain state was also more common during ACAM-J and was characterized by elevated thalamocortical connectivity and somatomotor network connectivity. The hyperconnected brain state significantly decreased over the course of ACAM-J, associating with self-reports of wider attention and diminished physical sensations. This brain state may be related to sensory awareness. Advanced meditators have developed well-honed abilities to move in and out of different altered states of consciousness, and this study provides initial evidence that functional neuroimaging can objectively track their dynamics.

Mapping brain neural networks in stress brain connectivity

Stress can cause severe damage to the CNS and contribute to an increased risk of neurological and psychiatric disorders. Gaining more insight into the neurobiology of stress is essential to treating neurological disorders associated with stress, which account for a high percentage of the world's disease burden. However, because of complicated variations in stressor types, stress perception, and preceding exposure to stressors, studying the impacts of stress is challenging. Gender, age, and timing are other crucial variables that can influence the stress response. Behavioral, physiological, genetic, and cellular/molecular neuroscience methodologies have all been widely applied in various research contexts to examine the neurobiological impacts of stress. Furthermore, because these approaches are invasive and hence undesirable or impractical for use in humans, they are frequently challenging to adapt to a therapeutic context. As an alternative to invasive procedures, functional neuroimaging approaches are starting to be developed. We discuss in this chapter brain neural networks under stress brain connection.

Dynamic functional connectivity signatures of focused attention on the breath in adolescents

Breathing meditation typically consists of directing attention toward breathing and redirecting attention when the mind wanders. As yet, we do not have a full understanding of the neural mechanisms of breath attention, in particular, how large-scale network interactions may be different between breath attention and rest and how these interactions may be modulated during periods of on-task and off-task attention to the breath. One promising approach may be examining fMRI measures including static connectivity between brain regions as well as dynamic, time-varying brain states. In this study, we analyzed static and dynamic functional connectivity in 72 adolescents during a breath-counting task (BCT), leveraging physiological respiration data to detect objective on-task and off-task periods. During the BCT relative to rest, we identified increases in static connectivity within attention-direction and orienting networks and anticorrelations between attention networks and the DMN. Dynamic connectivity analysis revealed four distinct brain states, including a DMN-anticorrelated brain state, proportionally more present during the BCT than the rest. We found there were distinct brain state markers of (i) breathing tasks vs rest and (ii) momentary on-task vs off-task attention within the BCT, yet in this analysis, no identifiable brain states reflecting between-individual behavioral variability.

Improving brain health via the central executive network

Cognitive and physical stress have significant effects on brain health, particularly through their influence on the central executive network (CEN). The CEN, which includes regions such as the dorsolateral prefrontal cortex, anterior cingulate cortex and inferior parietal lobe, is central to managing the demands of cognitively challenging motor tasks. Acute stress can temporarily reduce connectivity within the CEN, leading to impaired cognitive function and emotional states. However a rebound in these states often follows, driven by motivational signals through the mesocortical and mesolimbic pathways, which help sustain inhibitory control and task execution. Chronic exposure to physical and cognitive challenges leads to long-term improvements in CEN functionality. These changes are supported by neurochemical, structural and systemic adaptations, including mechanisms of tissue crosstalk. Myokines, adipokines, anti-inflammatory cytokines and gut-derived metabolites contribute to a biochemical environment that enhances neuroplasticity, reduces neuroinflammation and supports neurotransmitters such as serotonin and dopamine. These processes strengthen CEN connectivity, improve self-regulation and enable individuals to adopt and sustain health-optimizing behaviours. Long-term physical activity not only enhances inhibitory control but also reduces the risk of age-related cognitive decline and neurodegenerative diseases. This review highlights the role of progressive physical stress through exercise as a practical approach to strengthening the CEN and promoting brain health, offering a strategy to improve cognitive resilience and emotional well-being across the lifespan.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Functional anatomy and topographical organization of the frontotemporal arcuate fasciculus

Traditionally, the frontotemporal arcuate fasciculus (AF) is viewed as a single entity in anatomo-clinical models. However, it is unclear if distinct cortical origin and termination patterns within this bundle correspond to specific language functions. We use track-weighted dynamic functional connectivity, a hybrid imaging technique, to study the AF structure and function in two distinct datasets of healthy subjects. Here we show that the AF can be subdivided based on dynamic changes in functional connectivity at the streamline endpoints. An unsupervised parcellation algorithm reveals spatially segregated subunits, which are then functionally quantified through meta-analysis. This approach identifies three distinct clusters within the AF - ventral, middle, and dorsal frontotemporal AF - each linked to different frontal and temporal termination regions and likely involved in various language production and comprehension aspects. Our findings may have relevant implications for the understanding of the functional anatomy of the AF as well as its contribution to linguistic and non-linguistic functions.

Causal mechanisms of quadruple networks in pediatric bipolar disorder

BACKGROUND

Pediatric bipolar disorder (PBD) is characterized by abnormal functional connectivity among distributed brain regions. Increasing evidence suggests a role for the limbic network (LN) and the triple network model in the pathophysiology of bipolar disorder (BD). However, the specific relationship between the LN and the triple network in PBD remains unclear. This study aimed to investigate the aberrant causal connections among these four core networks in PBD.

METHOD

Resting-state functional MRI scans from 92 PBD patients and 40 healthy controls (HCs) were analyzed. Dynamic Causal Modeling (DCM) was employed to assess effective connectivity (EC) among the four core networks. Parametric empirical Bayes (PEB) analysis was conducted to identify ECs associated with group differences, as well as depression and mania severity. Leave-one-out cross-validation (LOOCV) was used to test predictive accuracy.

RESULT

Compared to HCs, PBD patients exhibited primarily excitatory bottom-up connections from the LN to the salience network (SN) and bidirectional excitatory connections between the default mode network (DMN) and SN. In PBD, top-down connectivity from the triple network to the LN was excitatory in individuals with higher depression severity but inhibitory in those with higher mania severity. LOOCV identified dysconnectivity circuits involving the caudate and hippocampus as being associated with mania and depression severity, respectively.

CONCLUSIONS

Disrupted bottom-up connections from the LN to the triple network distinguish PBD patients from healthy controls, while top-down disruptions from the triple network to LN relate to mood state differences. These findings offer insight into the neural mechanisms of PBD.

How Freely Moving Mind Wandering Relates to Creativity: Behavioral and Neural Evidence

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = -0.11, pFDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c' = 0.01, [-0.0181, -0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship.

Cardiorespiratory Fitness and Sleep, but not Physical Activity, are Associated with Functional Connectivity in Older Adults

BACKGROUND

Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes.

METHODS

In a large (n = 398) sample of healthy community dwelling older adults that were part of a larger interventional trial, we conducted cross sectional analyses of baseline data to examine the relationships between several modifiable behaviors and resting state functional connectivity within networks associated with cognition and emotional regulation. Additionally, maximal aerobic capacity, physical activity, quality of sleep, and body composition were assessed. Associations were explored both through correlation and best vs. worst group comparisons.

RESULTS

Greater cardiovascular fitness, but not larger quantity of daily physical activity, was associated with greater functional connectivity within the Default Mode (p = 0.008 r = 0.142) and Salience Networks (p = 0.005, r = 0.152). Better sleep (greater efficiency and fewer nighttime awakenings) was also associated with greater functional connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. When the population was split into quartiles, the highest body fat group displayed higher functional connectivity in the Dorsal Attentional Network compared to the lowest body fat percentage (p = 0.011; 95% CI - 0.0172 to - 0.0023).

CONCLUSION

These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep quality, but not greater quantity of physical activity, total sleep time, or lower body fat percentage are associated with increased functional connectivity within key resting state networks.

Neural rhythms of narcissism: Facets of narcissism are associated with different neural sources in resting-state EEG

Trait narcissism is characterized by significant heterogeneity across individuals. Despite advances in the conceptualization of narcissism, including the increasing recognition that narcissism is a multidimensional construct, the sources of this heterogeneity remain poorly understood. Here, we used a neural trait approach to help better understand "how," and shed light on "why," individuals vary in facets of trait narcissism. Participants (N = 58) first completed personality measures, including the Narcissistic Personality Inventory (NPI), and then in a second session sat passively while resting-state electroencephalography (rs-EEG) was recorded. We then regressed source-localized rs-EEG activity on the distinct facets of narcissism: Grandiose Exhibitionism (GE), Entitlement/Exploitativeness (EE), and Leadership/Authority (LA). Results revealed that each facet was associated with different (though sometimes overlapping) neural sources. Specifically, GE was associated with reduced activation in the dorsomedial prefrontal cortex (DMPFC). EE was associated with reduced activation in the DMPFC and right lateral PFC. LA was associated with increased activation in the left anterior temporal cortex. These findings support the idea that trait narcissism is a multidimensional construct undergirded by individual differences in neural regions related to social cognition (the DMPFC), self-regulation (right lateral PFC), and self-referential processing (left anterior temporal cortex).

Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

Silence between words: Is solitude important for relatedness?

Chronic loneliness is a risk factor for physical and health problems, in part due to dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system. In contrast, temporary moments of positive solitude (passing good times alone and not feeling lonely) appear to have positive effects on mental health, social life, and creativity, and seems to be a buffer against loneliness. Herein, three ways of how solitude may have positive effects on health and relatedness are discussed, namely effects on enhancement of mind-wandering, interoceptive awareness, and spirituality. Solitude may facilitate (1) activation of the default mode network (DMN) underlying mind-wandering including daydreaming about other people; (2) activation of brain areas supporting interoceptive awareness; (3) deactivation of prefrontal cortex, or deactivation and decreased connectivity of the DMN, giving raise to susceptibility to spiritual experiences. The capacity to handle and enjoy solitude is a developmental process that may be difficult for many persons. Craving for social connections and external stimulation with digital technologies (e.g., internet, smartphones, social media) might be interfering with the development of the capacity for solitude and thereby increasing loneliness; this might be partly due to impaired interoceptive awareness and impaired functional mind-wandering (common in solitude). Congruently, overuse of digital technologies was associated with reduced activity, and reduced gray matter volume and density, in brain areas supporting interoceptive awareness, as well as with decreased connectivity of the DMN supporting creative insights. Solitude has been a relatively dismissed topic in neuroscience and health sciences, but a growing number of studies is highlighting its importance for well-being.

Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback

Background

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

Methods

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n=18/18, HC-active/sham: n=13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

Results

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r=-0.5, p= 1.7E-3, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z=-2.09, p=0.037).

Limitations

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

Conclusion

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification

Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.

Towards a Real-Life Understanding of the Altered Functional Behaviour of the Default Mode and Salience Network in Chronic Pain: Are People with Chronic Pain Overthinking the Meaning of Their Pain?

Chronic pain is a source of substantial physical and psychological suffering, yet a clear understanding of the pathogenesis of chronic pain is lacking. Repeated studies have reported an altered behaviour of the salience network (SN) and default mode network (DMN) in people with chronic pain, and a majority of these studies report an altered behaviour of the dorsal ventromedial prefrontal cortex (vmPFC) within the anterior DMN. In this topical review, we therefore focus specifically on the role of the dorsal vmPFC in chronic pain to provide an updated perspective on the cortical mechanisms of chronic pain. We suggest that increased activity in the dorsal vmPFC may reflect maladaptive overthinking about the meaning of pain for oneself and one's actions. We also suggest that such overthinking, if negative, may increase the personal "threat" of a given context, as possibly reflected by increased activity in, and functional connectivity to, the anterior insular cortex within the SN.

A three-dimensional model of neural activity and phenomenal-behavioral patterns

How phenomenal experience and behavior are related to neural activity in physiology and psychopathology represents a fundamental question in neuroscience and psychiatry. The phenomenal-behavior patterns may be deconstructed into basic dimensions, i.e., psychomotricity, affectivity, and thought, which might have distinct neural correlates. This work provides a data overview on the relationship of these phenomenal-behavioral dimensions with brain activity across physiological and pathological conditions (including major depressive disorder, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, anxiety disorders, addictive disorders, Parkinson's disease, Tourette syndrome, Alzheimer's disease, and frontotemporal dementia). Accordingly, we propose a three-dimensional model of neural activity and phenomenal-behavioral patterns. In this model, neural activity is organized into distinct units in accordance with connectivity patterns and related input/output processing, manifesting in the different phenomenal-behavioral dimensions. (1) An external neural unit, which involves the sensorimotor circuit/brain's sensorimotor network and is connected with the external environment, processes external inputs/outputs, manifesting in the psychomotor dimension (processing of exteroception/somatomotor activity). External unit hyperactivity manifests in psychomotor excitation (hyperactivity/hyperkinesia/catatonia), while external unit hypoactivity manifests in psychomotor inhibition (retardation/hypokinesia/catatonia). (2) An internal neural unit, which involves the interoceptive-autonomic circuit/brain's salience network and is connected with the internal/body environment, processes internal inputs/outputs, manifesting in the affective dimension (processing of interoception/autonomic activity). Internal unit hyperactivity manifests in affective excitation (anxiety/dysphoria-euphoria/panic), while internal unit hypoactivity manifests in affective inhibition (anhedonia/apathy/depersonalization). (3) An associative neural unit, which involves the brain's associative areas/default-mode network and is connected with the external/internal units (but not with the environment), processes associative inputs/outputs, manifesting in the thought dimension (processing of ideas). Associative unit hyperactivity manifests in thought excitation (mind-wandering/repetitive thinking/psychosis), while associative unit hypoactivity manifests in thought inhibition (inattention/cognitive deficit/consciousness loss). Finally, these neural units interplay and dynamically combine into various neural states, resulting in the complex phenomenal experience and behavior across physiology and neuropsychiatric disorders.

Discovering individual fingerprints in resting-state functional connectivity using deep neural networks

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks

Independent component analysis (ICA) is one of the leading approaches for studying brain functional networks. There is increasing interest in neuroscience studies to investigate individual differences in brain networks and their association with demographic characteristics and clinical outcomes. In this work, we develop a sparse Bayesian group hierarchical ICA model that offers significant improvements over existing ICA techniques for identifying covariate effects on the brain network. Specifically, we model the population-level ICA source signals for brain networks using a Dirichlet process mixture. To reliably capture individual differences on brain networks, we propose sparse estimation of the covariate effects in the hierarchical ICA model via a horseshoe prior. Through extensive simulation studies, we show that our approach performs considerably better in detecting covariate effects in comparison with the leading group ICA methods. We then perform an ICA decomposition of a between-subject meditation study. Our method is able to identify significant effects related to meditative practice in brain regions that are consistent with previous research into the default mode network, whereas other group ICA approaches find few to no effects.

Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.

Recent advances in the neuroscience of spontaneous and off-task thought: implications for mental health

People spend a remarkable 30-50% of awake life thinking about something other than what they are currently doing. These experiences of being "off-task" can be described as spontaneous thought when mental dynamics are relatively flexible. Here we review recent neuroscience developments in this area and consider implications for mental wellbeing and illness. We provide updated overviews of the roles of the default mode network and large-scale network dynamics, and we discuss emerging candidate mechanisms involving hippocampal memory (sharp-wave ripples, replay) and neuromodulatory (noradrenergic and serotonergic) systems. We explore how distinct brain states can be associated with or give rise to adaptive and maladaptive forms of thought linked to distinguishable mental health outcomes. We conclude by outlining new directions in the neuroscience of spontaneous and off-task thought that may clarify mechanisms, lead to personalized biomarkers, and facilitate therapy developments toward the goals of better understanding and improving mental health.

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography

Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. We leverage stereotactic intracranial recordings to directly resolve local-field potentials from otherwise inaccessible structures to study moment-to-moment attentional activity in children with epilepsy performing a flexible attentional task. On an individual subject level, we employed deep learning to decode neural features predictive of task performance indexed by single-trial reaction time. These models were subsequently aggregated across participants to identify predictive brain regions based on AAL atlas and FIND functional network parcellations. Through this approach, we show that fluctuations in beta (12-30 Hz) and gamma (30-80 Hz) power reflective of increased top-down attentional control and local neuronal processing within relevant large-scale networks can accurately predict single-trial task performance. We next performed connectomic profiling of these highly predictive nodes to examine task-related engagement of distributed functional networks, revealing exclusive recruitment of the dorsal default mode network during shifts in attention. The identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention.

20 years of the default mode network: A review and synthesis

The discovery of the default mode network (DMN) has revolutionized our understanding of the workings of the human brain. Here, I review developments that led to the discovery of the DMN, offer a personal reflection, and consider how our ideas of DMN function have evolved over the past two decades. I summarize literature examining the role of the DMN in self-reference, social cognition, episodic and autobiographical memory, language and semantic memory, and mind wandering. I identify unifying themes and propose new perspectives on the DMN's role in human cognition. I argue that the DMN integrates and broadcasts memory, language, and semantic representations to create a coherent "internal narrative" reflecting our individual experiences. This narrative is central to the construction of a sense of self, shapes how we perceive ourselves and interact with others, may have ontogenetic origins in self-directed speech during childhood, and forms a vital component of human consciousness.

Altered brain dynamic in major depressive disorder: state and trait features

Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.

Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice

MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.

A revisit of the amygdala theory of autism: Twenty years after

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.

Temporal and spatial variability of dynamic microstate brain network in early Parkinson's disease

Changes of brain network dynamics reveal variations in macroscopic neural activity patterns in behavioral and cognitive aspects. Quantification and application of changed dynamics in brain functional connectivity networks may contribute to a better understanding of brain diseases, and ultimately provide better prognostic indicators or auxiliary diagnostic tools. At present, most studies are focused on the properties of brain functional connectivity network constructed by sliding window method. However, few studies have explored evidence-based brain network construction algorithms that reflect disease specificity. In this work, we first proposed a novel approach to characterize the spatiotemporal variability of dynamic functional connectivity networks based on electroencephalography (EEG) microstate, and then developed a classification framework for integrating spatiotemporal variability of brain networks to improve early Parkinson's disease (PD) diagnostic performance. The experimental results indicated that compared with the brain network construction method based on conventional sliding window, the proposed method significantly improved the performance of early PD recognition, demonstrating that the dynamic spatiotemporal variability of microstate-based brain networks can reflect the pathological changes in the early PD brain. Furthermore, we observed that the spatiotemporal variability of early PD brain network has a specific distribution pattern in brain regions, which can be quantified as the degree of motor and cognitive impairment, respectively. Our work offers innovative methodological support for future research on brain network, and provides deeper insights into the spatiotemporal interaction patterns of brain activity and their variabilities in early PD.

Modulation of mind wandering using transcranial direct current stimulation: A meta-analysis based on electric field modeling

Mind wandering (MW) is a heterogeneous construct involving task-unrelated thoughts. Recently, the interest in modulating MW propensity via non-invasive brain stimulation techniques has increased. Single-session transcranial direct current stimulation (tDCS) in healthy controls has led to mixed results in modulating MW propensity, possibly due to methodological heterogeneity. Therefore, our aim was to conduct a systematic meta-analysis to examine the influence of left dorsolateral prefrontal cortex (lDLPFC) and right inferior parietal lobule (rIPL) targeted tDCS on MW propensity. Importantly, by computational modeling of tDCS-induced electric fields, we accounted for differences in tDCS-dose across studies that varied strongly in their applied methodology. Fifteen single-session, sham-controlled tDCS studies published until October 2021 were included. All studies involved healthy adult participants and used cognitive tasks combined with MW thought-probes. Heterogeneity in tDCS electrode placement, stimulation polarity and intensity were controlled for by means of electric field simulations, while overall methodological quality was assessed via an extended risk of bias (RoB) assessment. We found that RoB was the strongest predictor of study outcomes. Moreover, the rIPL was the most promising cortical area for influencing MW, with stronger anodal electric fields in this region being negatively associated with MW propensity. Electric field strength in the lDLPFC was not related to MW propensity. We identified several severe methodological problems that could have contributed to overestimated effect sizes in this literature, an issue that needs urgent attention in future research in this area. Overall, there is no reliable evidence for tDCS influencing MW in the healthy. However, the analysis also revealed that increasing neural excitability in the rIPL via tDCS might be associated with reduced MW propensity. In an exploratory approach, we also found some indication that targeting prefrontal regions outside the lDLPFC with tDCS could lead to increased MW propensity.

Segregation, integration and balance in resting-state brain functional networks associated with bipolar disorder symptoms

Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested-spectral partition (NSP) method to study the segregation, integration, and balance in resting-state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine-learning approach, we identified different system-level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention-deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.

Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.

The continuum of attention dysfunction: Evidence from dynamic functional network connectivity analysis in neurotypical adolescents

The question of whether attention-related disorders such as attention-deficit/hyperactivity disorder (ADHD) are best understood as clinical categories or as extreme ends of a spectrum is an ongoing debate. Assessing individuals with varying degrees of attention problems and utilizing novel methodologies to assess relationships between attention and brain activity may provide key information to support the spectrum hypothesis. We scanned 91 neurotypical adolescents during rest using functional magnetic resonance imaging. We conducted static and dynamic functional network connectivity (FNC) analysis and correlated findings to behavioral metrics of ADHD, attention problems, and impulsivity. We found that dynamic FNC analysis detects significant differences in large-scale neural connectivity as a function of individual differences in attention and impulsivity that are obscured in static analysis. We show ADHD manifestations and attention problems are associated with diminished Salience Network-centered FNC and that ADHD manifestations and impulsivity are associated with prolonged periods of dynamically hyperconnected states. Importantly, our meta-state analysis results reveal a relationship between ADHD manifestations and exhibiting variable and volatile dynamic behavior such as changing meta-states more often and traveling over a greater dynamic range. These findings in non-clinical adolescents provide support for the continuum model of attention disorders.

Neural representations of self-generated thought during think-aloud fMRI

Is the brain at rest during the so-called resting state? Ongoing experiences in the resting state vary in unobserved and uncontrolled ways across time, individuals, and populations. However, the role of self-generated thoughts in resting-state fMRI remains largely unexplored. In this study, we collected real-time self-generated thoughts during "resting-state" fMRI scans via the think-aloud method (i.e., think-aloud fMRI), which required participants to report whatever they were currently thinking. We first investigated brain activation patterns during a think-aloud condition and found that significantly activated brain areas included all brain regions required for speech. We then calculated the relationship between divergence in thought content and brain activation during think-aloud and found that divergence in thought content was associated with many brain regions. Finally, we explored the neural representation of self-generated thoughts by performing representational similarity analysis (RSA) at three neural scales: a voxel-wise whole-brain searchlight level, a region-level whole-brain analysis using the Schaefer 400-parcels, and at the systems level using the Yeo seven-networks. We found that "resting-state" self-generated thoughts were distributed across a wide range of brain regions involving all seven Yeo networks. This study highlights the value of considering ongoing experiences during resting-state fMRI and providing preliminary methodological support for think-aloud fMRI.

Is Mindfulness Associated With Lower Pain Reactivity and Connectivity of the Default Mode Network? A Replication and Extension Study in Healthy and Episodic Migraine Participants

Formal training in mindfulness-based practices promotes reduced experimental and clinical pain, which may be driven by reduced emotional pain reactivity and undergirded by alterations in the default mode network, implicated in mind-wandering and self-referential processing. Recent results published in this journal suggest that mindfulness, defined here as the day-to-day tendency to maintain a non-reactive mental state in the absence of training, associates with lower pain reactivity, greater heat-pain thresholds, and resting-state default mode network functional connectivity in healthy adults in a similar manner to trained mindfulness. The extent to which these findings extend to chronic pain samples and replicate in healthy samples is unknown. Using data from healthy adults (n = 36) and episodic migraine patients (n = 98) and replicating previously published methods, we observed no significant association between mindfulness and heat-pain threshold, pain intensity or unpleasantness, or pain catastrophizing in healthy controls, or between mindfulness and headache frequency, severity, impactor pain catastrophizing in patients. There was no association between default mode network connectivity and mindfulness in either sample when probed via seed-based functional connectivity analyses. In post-hoc whole brain exploratory analyses, a meta-analytically derived default mode network node (ie, posterior cingulate cortex) showed connectivity with regions unassociated with pain processing as a function of mindfulness, such that healthy adults higher in mindfulness showed greater functional connectivity between the posterior cingulate cortex-and cerebellum. Collectively, these findings suggest that the relationship between mindfulness and default mode network functional connectivity may be nuanced or non-robust, and encourage further investigation of how mindfulness relates to pain. PERSPECTIVE: This study found few significant associations between dispositional mindfulness and pain, pain reactivity and default mode connectivity in healthy adults and migraine patients. The relationship between mindfulness and default mode network connectivity may be nuanced or non-robust.

Functional connectivity impairment of thalamus-cerebellum-scratching neural circuits in pruritus of chronic spontaneous urticaria

Pruritus of chronic spontaneous urticaria (CSU) is one of the most common and irritating sensations that severely affects the quality of life. However, the changes in the functional connectivity (FC) between thalamic subregions and other brain regions have not been fully elucidated. This study aimed to explore the potential changes in brain neural circuits by focusing on various subregions of the thalamus in patients with CSU pruritus to contribute to the understanding of chronic pruritus from the perspective of central mechanisms. A total of 56 patients with CSU and 30 healthy controls (HCs) completed the data analysis. Urticaria Activity Score 7 (UAS7), pruritus visual analog score (VAS-P), Dermatological Life Quality Index (DLQI), and immunoglobulin E (IgE) values were collected to assess clinical symptoms. Seed-based resting-state functional connectivity (rs-FC) analysis was used to assess relevant changes in the neural circuits of the brain. Compared to HCs, seeds within the caudal temporal thalamus (cTtha) on the right side of patients with CSU showed increased rs-FC with the cerebellum anterior lobe (CAL). Seeds within the lateral prefrontal thalamus (lPFtha) on the right side showed increased rs-FC with both CAL and pons, while those within the medial prefrontal thalamus (mPFtha) on the right side showed increased rs-FC with both CAL and the dorsal lateral prefrontal cortex (dlPFC) on the right side. Seeds within the posterior parietal thalamus (PPtha) on the right side showed increased rs-FC with the cerebellum posterior lobe (CPL) on the left side. The UAS7 values and IgE levels were positively correlated with the rs-FC of the right dlPFC. Our results suggest that patients with CSU may exhibit stronger rs-FC alterations between certain thalamic subregions and other brain regions. These changes affect areas of the brain involved in sensorimotor and scratching.

Trial registration number

[http://www.chictr.org.cn], identifier [ChiCTR1900022994].

BOLD cofluctuation 'events' are predicted from static functional connectivity

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure.

The global neuronal workspace as a broadcasting network

A new strategy for moving forward in the characterization of the global neuronal workspace (GNW) is proposed. According to Dehaene, Changeux, and colleagues (Dehaene, 2014, pp. 304, 312; Dehaene & Changeux, 2004, 2005), broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network.

Aberrant brain dynamics in major depressive disorder with suicidal ideation

BACKGROUND

Suicidal ideation (SI) is a common symptom of major depressive disorder (MDD). Accumulating studies demonstrated that MDD with SI was associated with static alterations in brain activity and functional connectivity. However, given that brain is a highly dynamic system, the changes of brain dynamic patterns in MDD with SI remain unknown.

METHODS

We included 60 MDD patients with SI (MDD-SI), 58 MDD patients without SI (MDD-NSI), and 58 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging. The sliding-window approach was used to calculate the dynamic fractional amplitude of low-frequency fluctuation (dfALFF) and dynamic degree centrality (dDC) to characterize the temporal dynamic regional activity and distant functional connectivity. We compared dfALFF and dDC across groups and further conducted correlations between abnormal dynamic metrics and the severity of suicidality.

RESULTS

In terms of the dynamic regional activity, MDD-SI showed decreased dfALFF in the left lingual gyrus and right middle occipital gyrus compared with MDD-NSI; in terms of the dynamic distant connectivity, MDD-SI showed decreased dDC in the right middle frontal gyrus compared with MDD-NSI. The decreased dDC in the right middle frontal gyrus was correlated with increased severity of suicidality.

LIMITATIONS

The relatively small sample size.

CONCLUSIONS

We demonstrate the specific brain dynamic patterns of MDD-SI in regional activity and distant functional connectivity compared to MDD-NSI. Especially the decreased temporal variability of the distant connectivity in the middle frontal gyrus was associated with SI. These altered dynamic patterns may represent a potential neurobiological diathesis of SI in MDD.

Early life stress is associated with the default mode and fronto-limbic network connectivity among young adults

Exposure to early life stress (ELS) is associated with a variety of detrimental psychological and neurodevelopmental effects. Importantly, ELS has been associated with regional alterations and aberrant connectivity in the structure and functioning of brain regions involved in emotion processing and self-regulation, creating vulnerability to mental health problems. However, longitudinal research regarding the impact of ELS on functional connectivity between brain regions in the default mode network (DMN) and fronto-limbic network (FLN), both implicated in emotion-related processes, is relatively scarce. Neuroimaging research on ELS has mostly focused on single nodes or bi-nodal connectivity instead of functional networks. We examined how ELS is associated with connectivity patterns within the DMN and FLN during rest in early adulthood. The participants (n = 86; 47 females) in the current functional magnetic resonance imaging (fMRI) study were young adults (18-21 years old) whose families had participated in a longitudinal study since pregnancy. ELS was assessed both prospectively (parental reports of family relationship problems and mental health problems during pregnancy and infancy) and retrospectively (self-reported adverse childhood experiences). Inter-subject representational similarity analysis (IS-RSA) and multivariate distance matrix regression (MDMR) were used to analyze the association between ELS and the chosen networks. The IS-RSA results suggested that prospective ELS was associated with complex alterations within the DMN, and that retrospective ELS was associated with alterations in the FLN. MDMR results, in turn, suggested that that retrospective ELS was associated with DMN connectivity. Mean connectivity of the DMN was also associated with retrospective ELS. Analyses further showed that ELS-related alterations in the FLN were associated with increased connectivity between the prefrontal and limbic regions, and between different prefrontal regions. These results suggest that exposure to ELS in infancy might have long-lasting influences on functional brain connectivity that persist until early adulthood. Our results also speak for the importance of differentiating prospective and retrospective assessment methods to understand the specific neurodevelopmental effects of ELS.

Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder

Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.

A test-retest resting, and cognitive state EEG dataset during multiple subject-driven states

Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with both short-term (within 90 mins) and long-term (one-month apart) designs. 60 participants were recorded during three EEG sessions. Each session includes EEG and behavioral data along with rich samples of behavioral assessments testing demographic, sleep, emotion, mental health and the content of self-generated thoughts (mind wandering). This data enables the investigation of both intra- and inter-session variability not only limited to electrophysiological changes, but also including alterations in resting and cognitive states, at high temporal resolution. Also, this dataset is expected to add contributions to the reliability and validity of EEG measurements with open resource.

Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder

The interaction between brain regions changes over time, which can be characterized using time-varying functional connectivity (tvFC). The common approach to estimate tvFC uses sliding windows and offers limited temporal resolution. An alternative method is to use the recently proposed edge-centric approach, which enables the tracking of moment-to-moment changes in co-fluctuation patterns between pairs of brain regions. Here, we first examined the dynamic features of edge time series and compared them to those in the sliding window tvFC (sw-tvFC). Then, we used edge time series to compare subjects with autism spectrum disorder (ASD) and healthy controls (CN). Our results indicate that relative to sw-tvFC, edge time series captured rapid and bursty network-level fluctuations that synchronize across subjects during movie-watching. The results from the second part of the study suggested that the magnitude of peak amplitude in the collective co-fluctuations of brain regions (estimated as root sum square (RSS) of edge time series) is similar in CN and ASD. However, the trough-to-trough duration in RSS signal is greater in ASD, compared to CN. Furthermore, an edge-wise comparison of high-amplitude co-fluctuations showed that the within-network edges exhibited greater magnitude fluctuations in CN. Our findings suggest that high-amplitude co-fluctuations captured by edge time series provide details about the disruption of functional brain dynamics that could potentially be used in developing new biomarkers of mental disorders.

Mixtures of large-scale dynamic functional brain network modes

Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM's while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM's assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.

The brain under cognitive workload: Neural networks underlying multitasking performance in the multi-attribute task battery

Multitasking is a common requirement in many occupations. Considerable research has demonstrated that performance declines as a result of multitasking, and that it engages multiple brain regions. Despite growing evidence suggesting that brain regions operate as networks, minimal research has investigated the cognitive brain networks implicated in multitasking. The Multi-Attribute Task Battery II (MATB) is a common method for assessing multitasking ability that simulates a pilot's operational environment inside an aircraft cockpit. The aim of the present study was to examine multitasking performance on the MATB, and the associated neural patterns underlying performance with functional magnetic resonance imaging (fMRI). Twenty-four participants completed the MATB in the fMRI scanner. Participants completed four runs of the MATB in a 2 (Task: multitasking vs. single tasking) × 2 (Difficulty: hard vs. easy) design. MATB performance was measured as a function of accuracy. We analyzed the fMRI brain scans using both static and dynamic functional connectivity to determine whether there were differences in the connectivity patterns associated with each of the four conditions. A significant interaction between Task and Difficulty was observed such that multitasking performance accuracy, which was derived from the average across tasks, was lower than single tasking in the hard, but not easy, condition. The fMRI data revealed that static and dynamic functional connectivity between the default mode and dorsal attention networks was stronger during multitasking relative to single tasking. The static functional connectivity between the default mode and left frontoparietal networks, along with the dynamic functional connectivity between the dorsal attention and left frontoparietal networks, were both more anti-correlated during multitasking relative to single tasking. Taken together, the static and dynamic functional connectivity analyses provide complementary information to reveal the interactions among cognitive networks that support multitasking performance. Targeting these networks may offer a path to enhance multitasking ability through the application of neurostimulation and neuroenhancement techniques.

Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites

BACKGROUND

The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity.

RESULTS

In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = -0.38, P < 0.001; three classes classification: R = -0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls.

CONCLUSIONS

The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD.

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement

The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.