Brain Network Activity Research Articles

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

The effects of deep TMS on purpose in life, quantitative EEG and event-related potentials in major depressive disorder

Background: Perceived purpose in life (PIL) is linked with many vital health outcomes, including Major Depressive Disorder (MDD). Methods: In this study, biomarkers associated with depression and PIL were investigated using Brain Network Activation (BNA) based quantitative electroencephalography (QEEG) and event-related potential (ERP) measures in a sample of individuals with MDD. Data were analyzed before and after a 36-session, Deep transcranial magnetic stimulation (TMS) program. Results: At baseline, the study observed that increased slow-frequency resting-state activity in the frontal and temporal regions correlated with higher levels of depression and reduced PIL. Additionally, a reduced P3b amplitude was found to predict elevated depressive symptoms. However, with the application of Deep TMS treatment notable improvements were observed in both depression (p < .001. d = 2.15) and PIL (p < .001, d = 1.59) levels. The treatment also successfully regulated resting-state QEEG (delta/beta) and ERP characteristics (P200 latency), bringing them closer to healthy levels. Conclusions: This attenuation of brain activity patterns relating to depression is encouraging as it suggests that effects were robust and are more likely to be sustained over time. This study represents the first exploration of the effects of Deep TMS on PIL and relevant QEEG and ERP biomarkers. The initial evidence suggests that Deep TMS holds promise in enhancing both PIL and neurophysiological health. Future investigations should continue exploring the utility of Deep TMS in targeting a wide range of neuropsychological and physical health conditions, leveraging objective biomarkers such as QEEG and ERP.

Transient destabilization of interhemispheric functional connectivity induced by spreading depolarization

ABSTRACT Cortical spreading depolarization (CSD), a slowly propagating wave of transient cellular depolarization, is a reliable cortical response to various brain insults (stroke, trauma, seizures) and underlying mechanism of migraine aura. Little is known about CSD effects on brain network activity. Using undirected (mutual information, MI) and directed (transfer entropy, TE) measures, we studied the dynamics of cross-hemispheric connectivity associated with the development of unilateral CSD in freely behaving rats and the involvement of inhibitory transmission in mechanisms of the coupling changes. We show that the development of CSD in the cortex of one hemisphere is followed by the transient loss of undirected functional connectivity (MI) between ipsilateral and contralateral cortical regions. The post-CSD functional disconnection of the hemispheres was accompanied by an increase in driving force from an unaffected contralateral cortex to an affected one (TE). Mild cortical disinhibition produced by pretreatment with an inhibitory receptor blocking agent (penthylenetetrazole) did not affect CSD but attenuated (MI) or eliminated (TE) the CSD-induced connectivity changes. The effects of CSD on functional connectivity in awake rodents were similar at the individual and group levels, suggesting that the described connectivity response may be a promising network biomarker of CSD occurrence in patients.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia.

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Exploring memory-related network via dorsal hippocampus suppression

Abstract Memory is a complex brain process that requires coordinated activities in a large-scale brain network. However, the relationship between coordinated brain network activities and memory-related behavior is not well understood. In this study, we investigated this issue by suppressing the activity in the dorsal hippocampus (dHP) using chemogenetics and measuring the corresponding changes in brain-wide resting-state functional connectivity (RSFC) and memory behavior in awake rats. We identified an extended brain network contributing to the performance in a spatial memory related task. Our results were cross-validated using two different chemogenetic actuators, clozapine (CLZ) and clozapine-N-oxide (CNO). This study provides a brain network interpretation of memory performance, indicating that memory is associated with coordinated brain-wide neural activities.

Ventral tegmental area dopamine neural activity switches simultaneously with rule representations in the prefrontal cortex and hippocampus.

This study examines neural activity in mammalian brain networks that support the ability to respond flexibly to changing contexts. We use a rule-switching spatial task to examine whether the key reward-responsive and predictive dopamine (DA) activity changes in coordination with changes in rule representations in key cognitive regions, the prefrontal cortex (PFC) and hippocampus. We first established distinct rule representations in PFC and hippocampus, and predictive coding of reward outcomes by DA neuronal activity. We show that the rule-specific DA reward prediction after a rule switch develops in temporal coordination with changes in rule representations especially in PFC, eventually leading to behavioral changes. These results thus provide an integrated understanding of reward prediction, cognitive representations of rules and behavioral adaptation.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal–external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

An improved spectral clustering method for accurate detection of brain resting-state networks

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

Virtual reality-brain computer interface hand function enhancement rehabilitation system incorporating multi-sensory stimulation

Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.

Impact of transcranial electrical stimulation on simultaneous stereoelectroencephalography recordings: A randomized sham-controlled study

ObjectiveClinical exploitation of transcranial electrical stimulation for focal epilepsy treatment lacks quantification of the underlying neurophysiological changes. This study explores the immediate effects of transcranial alternating (tACS) and direct (tDCS) current stimulation on local and network brain activity using simultaneous stereoelectroencephalography (SEEG) recordings. MethodsPatients were randomized for personalized tACS (n = 5) or tDCS (n = 6). Active stimulation (20 min) was preceded by sham stimulation (20 min). Changes in interictal epileptiform discharges (IED), functional connectivity (FC) and power spectral density (PSD) were quantified against baseline. ResultsResults demonstrated variable responses. Spike rate decreased in 2/6 subjects following sham and tDCS, while 2/6 showed an increase. Alpha power and aperiodic PSD components generally increased during and after tDCS but decreased following tACS. FC changes varied among subjects and were detectable even during sham sessions. ConclusionsStrong variability suggests that tES does not have a univocal effect on immediate changes in IED or FC, possibly due to the single session format and challenges in affecting subcortical areas. SignificanceThis is the first study to examine intracranial FC changes during tACS and tDCS, highlighting the importance of sham comparisons and individual variability in tES response, offering valuable insights into its application for epilepsy treatment.

Electroencephalography microstate alterations reflect potential double-edged cognitive adaptation in Ménière's disease.

To explore the microstate characteristics and underlying brain network activity of Ménière's disease (MD) patients based on high-density electroencephalography (EEG), elucidate the association between microstate dynamics and clinical manifestation, and explore the potential of EEG microstate features as future neurobiomarkers for MD. Thirty-two patients diagnosed with MD and 29 healthy controls (HC) matched for demographic characteristics were included in the study. Dysfunction and subjective symptom severity were assessed by neuropsychological questionnaires, pure tone audiometry, and vestibular function tests. Resting-state EEG recordings were obtained using a 256-channel EEG system, and the electric field topographies were clustered into four dominant microstate classes (A, B, C, and D). The dynamic parameters of each microstate were analyzed and utilized as input for a support vector machine (SVM) classifier to identify significant microstate signatures associated with MD. The clinical significance was further explored through Spearman correlation analysis. MD patients exhibited an increased presence of microstate class C and a decreased frequency of transitions between microstate class A and B, as well as between class A and D. The transitions from microstate class A to C were also elevated. Further analysis revealed a positive correlation between equilibrium scores and the transitions from microstate class A to C under somatosensory challenging conditions. Conversely, transitions between class A and B were negatively correlated with vertigo symptoms. No significant correlations were detected between these characteristics and auditory test results or emotional scores. Utilizing the microstate features identified via sequential backward selection, the linear SVM classifier achieved a sensitivity of 86.21% and a specificity of 90.61% in distinguishing MD patients from HC. We identified several EEG microstate characteristics in MD patients that facilitate postural control yet exacerbate subjective symptoms, and effectively discriminate MD from HC. The microstate features may offer a new approach for optimizing cognitive compensation strategies and exploring potential neurobiological markers in MD.

A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices: a cross-sectional study.

Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease. Resting-state functional magnetic resonance imaging, along with its multi-level feature indices, has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease. It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity. However, the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge. Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases, a knowledge gap still exists in the field of freezing of gait in Parkinson's disease. This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging, along with clinical features, to distinguish between Parkinson's disease patients with and without freezing of gait. We recruited 28 patients with Parkinson's disease who had freezing of gait (15 men and 13 women, average age 63 years) and 30 patients with Parkinson's disease who had no freezing of gait (16 men and 14 women, average age 64 years). Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations, mean regional homogeneity, and degree centrality. Neurological and clinical characteristics were also evaluated. We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators. We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features. Subsequently, we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve. The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait, or from healthy controls, the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750 (with an accuracy of 70.9%) and 0.759 (with an accuracy of 65.3%), respectively. When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait, the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847 (with an accuracy of 74.3%). The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics: Montreal Cognitive Assessment and Hamilton Depression Scale scores. Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.

Therapeutic DBS for OCD Suppresses the Default Mode Network.

Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an emerging treatment for severe, refractory obsessive-compulsive disorder (OCD). The therapeutic effects of DBS are hypothesized to be mediated by direct modulation of a distributed cortico-striato-thalmo-cortical network underlying OCD symptoms. However, the exact underlying mechanism by which DBS exerts its therapeutic effects still remains unclear. In five participants receiving DBS for severe, refractory OCD (3 responders, 2 non-responders), we conducted a DBS On/Off cycling paradigm during the acquisition of functional MRI to determine the network effects of stimulation across a variety of bipolar configurations. We also performed tractography using diffusion-weighted imaging (DWI) to relate the functional impact of DBS to the underlying structural connectivity between active stimulation contacts and functional brain networks. We found that therapeutic DBS had a distributed effect, suppressing BOLD activity within regions such as the orbitofrontal cortex, dorsomedial prefrontal cortex, and subthalamic nuclei compared to non-therapeutic configurations. Many of the regions suppressed by therapeutic DBS were components of the default mode network (DMN). Moreover, the estimated stimulation field from the therapeutic configurations exhibited significant structural connectivity to core nodes of the DMN. Therapeutic DBS for OCD suppresses BOLD activity within a distributed set of regions within the DMN relative to non-therapeutic configurations. We propose that these effects may be mediated by interruption of communication through structural white matter connections surrounding the DBS active contacts.

Neural networks associated with eye movements in congenital blindness.

Recent studies have shown that during the typical resting-state, echo planar imaging (EPI) time series obtained from the eye orbit area correlate with brain regions associated with oculomotor control and lower-level visual cortex. Here, we asked whether congenitally blind (CB) shows similar patterns, suggesting a hard-wired constraint on connectivity. We find that orbital EPI signals in CB do correlate with activity in the motor cortex, but less so with activity in the visual cortex. However, the temporal patterns of this eye movement-related signal differed strongly between CB and sighted controls. Furthermore, in CB, a few participants showed uncoordinated orbital EPI signals between the two eyes, each correlated with activity in different brain networks. Our findings suggest a retained circuitry between motor cortex and eye movements in blind, but also a moderate reorganization due to the absence of visual input, and the inability of CB to control their eye movements or sense their positions.

The timing of sleep spindles is modulated by the respiratory cycle in humans

ObjectiveCoupling of sleep spindles with cortical slow waves and hippocampus sharp-waves ripples is crucial for sleep-related memory consolidation. Recent literature evidenced that nasal respiration modulates neural activity in large-scale brain networks. In rodents, this respiratory drive strongly varies according to vigilance states. Whether sleep oscillations are also respiration-modulated in humans remains open. In this work, we investigated the influence of breathing on sleep spindles during non-rapid-eye-movement sleep in humans. MethodsFull night polysomnography of twenty healthy participants were analysed. Spindles and slow waves were automatically detected during N2 and N3 stages. Spindle-related sigma power as well as spindle and slow wave events were analysed according to the respiratory phase. ResultsWe found a significant coupling between both slow and fast spindles and the respiration cycle, with enhanced sigma activity and occurrence probability of spindles during the middle part of the expiration phase. A different coupling was observed for slow waves negative peaks which were rather distributed around the two respiration phase transitions. ConclusionOur findings suggest that breathing cycle influences the dynamics of brain activity during non-rapid-eye-movement sleep. SignificanceThis coupling may enable sleep spindles to synchronize with other sleep oscillations and facilitate information transfer between distributed brain networks.

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, placebo-controlled and delayed-start, neuroimaging trial in depression

BackgroundMajor depressive disorder (MDD) is a leading cause of disability worldwide across domains of health and cognition, affecting overall quality of life. Approximately one third of individuals with depression do not fully respond to treatments (e.g., conventional antidepressants, psychotherapy) and alternative strategies are needed. Recent early phase trials suggest psilocybin may be a safe and efficacious intervention with rapid-acting antidepressant properties. Psilocybin is thought to exert therapeutic benefits by altering brain network connectivity and inducing neuroplastic changes that endure for weeks post-treatment. Although early clinical results are encouraging, psilocybin’s acute neurobiological effects on neuroplasticity have not been fully investigated. We aim to examine for the first time how psilocybin acutely (intraday) and subacutely (weeks) alters functional brain networks implicated in depression.MethodsFifty participants diagnosed with MDD or persistent depressive disorder (PDD) will be recruited from a tertiary mood disorders clinic and undergo 1:1 randomization into either an experimental or control arm. Participants will be given either 25 mg psilocybin or 25 mg microcrystalline cellulose (MCC) placebo for the first treatment. Three weeks later, those in the control arm will transition to receiving 25 mg psilocybin. We will investigate whether treatments are associated with changes in arterial spin labelling and blood oxygenation level-dependent contrast neuroimaging assessments at acute and subacute timepoints. Primary outcomes include testing whether psilocybin demonstrates acute changes in (1) cerebral blood flow and (2) functional brain activity in networks associated with mood regulation and depression when compared to placebo, along with changes in MADRS score over time compared to placebo. Secondary outcomes include changes across complementary clinical psychiatric, cognitive, and functional scales from baseline to final follow-up. Serum peripheral neurotrophic and inflammatory biomarkers will be collected at baseline and follow-up to examine relationships with clinical response, and neuroimaging measures.DiscussionThis study will investigate the acute and additive subacute neuroplastic effects of psilocybin on brain networks affected by depression using advanced serial neuroimaging methods. Results will improve our understanding of psilocybin’s antidepressant mechanisms versus placebo response and whether biological measures of brain function can provide early predictors of treatment response.Trial registrationClinicalTrials.gov Identifier: NCT06072898. Registered on 6 October 2023.

Transcriptomic Evaluation of a Stress Vulnerability Network Using Single-Cell RNA Sequencing in Mouse Prefrontal Cortex

BackgroundIncreased vulnerability to stress is a major risk factor for several mood disorders, including major depressive disorder. Although cellular and molecular mechanisms associated with depressive behaviors following stress have been identified, little is known about the mechanisms that confer the vulnerability that predisposes individuals to future damage from chronic stress. MethodsWe used multisite in vivo neurophysiology in freely behaving male and female C57BL/6 mice (n = 12) to measure electrical brain network activity previously identified as indicating a latent stress vulnerability brain state. We combined this neurophysiological approach with single-cell RNA sequencing of the prefrontal cortex to identify distinct transcriptomic differences between groups of mice with inherent high and low stress vulnerability. ResultsWe identified hundreds of differentially expressed genes (padjusted < .05) across 5 major cell types in animals with high and low stress vulnerability brain network activity. This unique analysis revealed that GABAergic (gamma-aminobutyric acidergic) neuron gene expression contributed most to the network activity of the stress vulnerability brain state. Upregulation of mitochondrial and metabolic pathways also distinguished high and low vulnerability brain states, especially in inhibitory neurons. Importantly, genes that were differentially regulated with vulnerability network activity significantly overlapped (above chance) with those identified by genome-wide association studies as having single nucleotide polymorphisms significantly associated with depression as well as genes more highly expressed in postmortem prefrontal cortex of patients with major depressive disorder. ConclusionsThis is the first study to identify cell types and genes involved in a latent stress vulnerability state in the brain.

Pharmacological modulation of septins restores calcium homeostasis and is neuroprotective in models of Alzheimer's disease.

Abnormal calcium signaling is a central pathological component of Alzheimer's disease (AD). Here, we describe the identification of a class of compounds called ReS19-T, which are able to restore calcium homeostasis in cell-based models of tau pathology. Aberrant tau accumulation leads to uncontrolled activation of store-operated calcium channels (SOCCs) by remodeling septin filaments at the cell cortex. Binding of ReS19-T to septins restores filament assembly in the disease state and restrains calcium entry through SOCCs. In amyloid-β and tau-driven mouse models of disease, ReS19-T agents restored synaptic plasticity, normalized brain network activity, and attenuated the development of both amyloid-β and tau pathology. Our findings identify the septin cytoskeleton as a potential therapeutic target for the development of disease-modifying AD treatments.

Жалобы на когнитивные нарушения при унилатеральной компрессии височной доли

&lt;p&gt;Awareness of cognitive deficits, as a rule, occurs in the form of &amp;laquo;memory complaints&amp;raquo;. Spontaneous narration of problems reveals their semantic hierarchy and can be a model for generating a spontaneous flow of thoughts and memories with the activity of the default brain network. The objective of the study was to study the nature of spontaneously expressed complaints about memory by patients with mild compression of the temporal regions of the resting network of the brain. The study was conducted in a homogeneous clinical group of 48 patients with extracerebral benign neoplasms located in close proximity to the medio-basal parts of the left (25 people) or right (28 people) temporal lobe. The tumor compresses these parts, but does not infiltrate the brain substance. With left-sided compression, complaints about verbal processes dominated, and their quantitative predominance over similar complaints was recorded in the group with right-sided compression and in the control group of healthy subjects (24 people). The severity of cognitive complaints with left-sided exposure to the brain revealed a negative correlation with experimental indicators of the success of spatial distribution of attention and simultaneous perception. With right-sided compression, spontaneously generated cognitive complaints revealed a dependence on the number of recognition errors in the AVP test, but this dependence was paradoxical: the worse this memory indicator was, the fewer cognitive complaints patients presented. The phenomenology of neural network compression makes it possible to register hemispheric specificity in spontaneously generated thoughts and memories.&lt;/p&gt;

Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents.

Adolescents born very preterm (VPT; <32 weeks of gestation) face an elevated risk of executive, behavioral, and socioemotional difficulties. Evidence suggests beneficial effects of mindfulness-based intervention (MBI) on these abilities. This study seeks to investigate the association between the effects of MBI on executive, behavioral, and socioemotional functioning and reliable changes in large-scale brain networks dynamics during rest in VPT young adolescents who completed an 8-week MBI program. Neurobehavioral assessments and resting-state functional magnetic resonance imaging were performed before and after MBI in 32 VPT young adolescents. Neurobehavioral abilities in VPT participants were compared with full-term controls. In the VPT group, dynamic functional connectivity was extracted by using the innovation-driven coactivation patterns framework. The reliable change index was used to quantify change after MBI. A multivariate data-driven approach was used to explore associations between MBI-related changes on neurobehavioral measures and temporal brain dynamics. Compared with term-born controls, VPT adolescents showed reduced executive and socioemotional functioning before MBI. After MBI, a significant improvement was observed for all measures that were previously reduced in the VPT group. The increase in executive functioning, only, was associated with reliable changes in the duration of activation of large-scale brain networks, including frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks. The improvement in executive functioning after an MBI was associated with reliable changes in large-scale brain network dynamics during rest. These changes encompassed frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks that are related to different executive processes including self-regulation, attentional control, and attentional awareness of relevant sensory stimuli.