Resting-state Networks Research Articles

Exploring causal association between functional/structural connectivity and major depression disorder: A bidirectional Mendelian randomization study

ObjectivesPrior observational studies have suggested a correlation between major depressive disorder (MDD) and communication imbalances within the resting-state brain network (RSN), but the causal relationship remains unclear. This research uses Mendelian randomization (MR) analysis to explore the potential causal effects between functional connectivity (FC), structural connectivity (SC) and MDD. MethodsTwo-sample bidirectional MR analysis was employed in this study. Inverse variance weighted (IVW) was used to explore the causal relationship between the FC/SC and MDD, with various methods such as MR-Egger to conduct sensitivity analyses. ResultsThe IVW analysis results showed that higher genetic predicted dorsal attention network FC, limbic network SC, and dorsal attention network SC were associated with an increased risk of MDD (β: 15.08, 95%CI: 5.89–24.27, p = 0.001; β: 3.79, 95%CI: −0.22-7.8, p = 0.034; β: 9.89, 95%CI: 0.88–18.90, p = 0.031). Reverse MR analysis demonstrated that a genetically predicted elevated risk of MDD was associated with reduced frontal parietal network FC (β: −0.00046, p = 0.041). ConclusionsThe study suggests a causal relationship between the FC and SC within specific RSNs and the risk of MDD. Abnormalities in the dorsal attention network FC/SC and the limbic network SC were risk factors for MDD. The FC abnormality of the frontal parietal network may be the downstream influence following the MDD onset. Further investigation is needed to determine the potential utility of these neuroimaging markers in the prevention of MDD or the evaluation of treatment efficacy.

The surface-based degree centrality of patients with lifelong premature ejaculation: A resting-state fMRI study

The aim of this study was to investigate alterations in the resting-state brain functional network characteristics of lifelong premature ejaculation (PE) patients using surface-based degree centrality (DC), and to analyze the correlation between these alterations and clinical symptoms in PE patients. The study included individuals with lifelong PE (patient group, n = 36) and a control group matched by age and education level (control group, n = 22). Resting-state functional magnetic resonance imaging (fMRI) scans were performed on all participants. Surface-based degree centrality analysis was conducted and the differences between the two groups were compared using t-tests. Further, the DC values of brain regions showing significant differences were correlated with clinical symptoms. Compared to the control group, the patient group exhibited significantly reduced degree centrality (DC) values in the left precuneus and significantly increased DC values in the right supplementary motor area (SMA). Furthermore, intravaginal ejaculatory latency time (IELT) and Chinese Index of Premature Ejaculation (CIPE) values were positively correlated with left precuneus DC values and negatively correlated with right SMA DC values. Patients with primary lifelong ejaculation demonstrate abnormalities in key brain network nodes and their connections with relevant brain regions, which are strongly associate with clinical symptoms. These findings enhance our understanding of the neuronal pathological changes in PE patients.

Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Neural correlates of pain acceptance and the role of the cerebellum: Functional connectivity and anatomical differences in individuals with headaches versus matched controls.

Despite functional connectivity network dysfunction among individuals with headaches, no studies have examined functional connectivity neural correlates and anatomical differences in coping with headaches. This study investigated inter-individual variability in whole-brain functional connectivity and anatomical differences among 37 individuals with primary headaches and 24 age- and gender-matched controls, and neural correlates of psychological flexibility (PF) that was previously found to contribute to headache adjustment. Participants (84% women; M headache severity = 4/10; M age = 43 years) underwent functional magnetic resonance imaging scans and completed questionnaires to examine global and subnetwork brain areas, and their relations with PF components, controlling for age, gender, education, and head-motion. Seed and voxel-based contrast analyses between groups showed atypical functional connectivity of regions involved in pain matrix and core resting-state networks. Pain acceptance was the sole PF component that correlated with the cerebellum (x, y, z: 28, -72, -34, p-false discovery rate <0.001), where individuals with headaches showed higher grey matter density compared to controls. The cerebellum, recently implicated in modulating emotional and cognitive processes, was indicated to process information resembling what individuals do when practicing pain acceptance. Our findings establish for the first time this connection of the cerebellum and its role in pain acceptance. We propose that pain acceptance might be a behavioural biomarker target that could modulate problematic headache perceptions and brain networks abnormalities. This study highlights the potential use of emerging behavioural biomarkers in headache management, such as pain acceptance, and their role in modifying the headache experience. Notably, grey matter reorganization in the cerebellum and other known brain pain networks, could indicate brain networks that can be modified from targeted behavioural interventions to help decode the nociplastic mechanisms that predominates in headaches.

Structural and functional brain markers of cognitive impairment in healthcare workers following mild SARS-CoV-2 infection during the original stream.

Severe acute respiratory syndrome coronavirus 2 infection often involves the nervous system, leading to cognitive dysfunctions, fatigue and many other neurological signs that are becoming increasingly recognized. Despite mild forms of the disease accounting for most cases worldwide, research on the pathophysiology driving mild coronavirus disease 2019 (COVID-19) has received little attention. In this respect, recent evidence has pointed out that around 30-40% of non-critical, mild-to-moderate severity COVID-19 survivors may display cognitive disturbances several months post-illness. Hence, the impact of COVID-19 on the brain structure and function, through potential neuropathological mechanisms underpinning cognitive alterations in post-mild COVID-19 infections, remains largely unexplored. This retrospective multicentre observational cohort study, entirely based on a healthcare worker sample (n = 65; 55% females, aged 21-61), investigated the cognitive status and the structural and functional brain integrity among non-hospitalized individuals who developed mild COVID-19 symptoms during the occurrence of severe acute respiratory syndrome coronavirus 2 variants Alpha to Delta, compared with healthy controls tested before the pandemic onset. All evaluations were performed at an average of 9-month follow-up post-infection period. Participants completed a comprehensive neuropsychological assessment and structural and functional MRI exams. Radiological inspection sought to detect the presence of white matter hyperintensities on axial fluid-attenuated inversion recovery images. Global and regional grey matter integrity assessment, analysing changes in grey matter volumes and cortical thinning, and functional connectivity alterations of resting-state brain networks were also conducted. Regression analyses tested the relationships between the presence of specific cognitive impairments and potential structural and functional brain findings. Our results revealed that clinical, cognitive screening and neuropsychological examinations were average between both groups, except for specific impairments related to executive functions in the mild COVID-19. Compared to healthy controls, mild COVID-19 subjects exhibited increased juxtacortical white matter hyperintensities, thalamic and occipital volume loss and diminished resting-state functional connectivity involving the left precuneus and cuneus in default-mode network and affecting the right angular gyrus and left precuneus in the dorsal attentional network. Reduced thalamic volume was the only variable selected in the final model explaining the observed executive function impairment in mild COVID-19. The presence of cognitive, structural and functional brain abnormalities over time suggests that the action of widespread neurovascular and inflammatory phenomena on the nervous system might also occur in mild forms following COVID-19 infection rather than permanent brain damage linked to the direct or indirect action of the virus. Our findings emphasize the need to pay attention to the long-term brain-related consequences of mild COVID-19 infections during the original stream.

Cerebellum and basal ganglia connectivity in isolated REM sleep behaviour disorder and Parkinson's disease: an exploratory study.

REM sleep behaviour disorder (RBD) is a parasomnia characterised by dream-enacting behaviour with loss of muscle atonia during REM sleep and is a prodromal feature of α-synucleinopathies like Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. Although cortical-to-subcortical connectivity is well-studied in RBD, cerebellar and subcortical nuclei reciprocal connectivity is less established. Nonetheless, it could be relevant since RBD pathology involves brainstem structures with an ascending gradient. In this study, we utilised resting-state functional MRI to investigate 13 people with isolated RBD (iRBD), 17 with Parkinson's disease and 16 healthy controls. We investigated the connectivity between the basal ganglia, thalamus and regions of the cerebellum. The cerebellum was segmented using a functional atlas, defined by a resting-state network-based parcellation, rather than an anatomical one. Controlling for age, we found a significant group difference (F4,82 = 5.47, pFDR = 0.017) in cerebellar-thalamic connectivity, with iRBD significantly lower compared to both control and Parkinson's disease. Specifically, cerebellar areas involved in this connectivity reduction were related to the default mode, language and fronto-parietal resting-state networks. Our findings show functional connectivity abnormalities in subcortical structures that are specific to iRBD and may be relevant from a pathophysiological standpoint. Further studies are needed to investigate how connectivity changes progress over time and whether specific changes predict disease course or phenoconversion.

Altered functional connectivity of resting-state networks and the correlation with clinical characteristics in intermittent exotropia adult patients: a resting-state magnetic resonance imaging study

BackgroundThe pathogenesis of intermittent exotropia (IXT) remains unclear. The study aims to investigate alterations of resting-state networks (RSNs) in IXT adult patients using resting-state functional magnetic resonance imaging (rs-fMRI) data to explore the potential neural mechanisms.MethodsTwenty-six IXT adult patients and 22 age-, sex-, handedness-, and education-matched healthy controls (HCs) underwent fMRI scanning and ophthalmological examinations. Brain areas with significant functional connectivity (FC) differences between the IXT and HC groups were selected as regions of interest (ROI) and mean z-scores were calculated to control for individual differences.ResultsCompared with HCs, IXT patients exhibited altered FC in various brain regions within RSNs involved in binocular fusion, stereopsis, ocular movement, emotional processes and social cognition, including the default mode network (DMN), the dorsal attention network (DAN), the visual network (VN), the sensorimotor network (SMN), the executive control network (ECN), the frontoparietal network (FPN) and the auditory network (AN). The degree of exodeviation was positively correlated with FC value of left middle occipital gyrus (MOG) within the VN. Correspondingly, we found a negative correlation between the degree of exodeviation and the FC value of left angular gyrus (AG) within FPN (P < 0.05). The FNC analysis between different RSNs also provides evidence on visual-motor cortical plasticity.ConclusionsIXT patients showed widespread changes of brain activity within RSNs related to binocular fusion, stereopsis, oculomotor control, emotional processes, and social cognition. These findings extend our current understanding of the neuropathological mechanisms of IXT.Trial registrationBeginning date of the trial: 2021-09-01. Date of registration:2021-07-18. Trial registration number: ChiCTR 2,100,048,852. Trial registration site: http://www.chictr.org.cn/index.aspx.

Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine.

Psychedelics have recently attracted significant attention for their potential to mitigate symptoms associated with various psychiatric disorders. However, the precise neurobiological mechanisms responsible for these effects remain incompletely understood. A valuable approach to gaining insights into the specific mechanisms of action involves comparing psychedelics with substances that have partially overlapping neurophysiological effects, i.e., modulating the same neurotransmitter systems. Imaging data were obtained from the clinical trial NCT03019822, which explored the acute effects of lysergic acid diethylamide (LSD), d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers. The clinical trial employed a double-blind, placebo-controlled, crossover design. Herein, various resting-state connectivity measures were examined, including within-network connectivity (integrity), between-network connectivity (segregation), seed-based connectivity of resting-state networks, and global connectivity. Differences between placebo and the active conditions were assessed using repeated-measures ANOVA, followed by post-hoc pairwise t-tests. Changes in voxel-wise seed-based connectivity were correlated with serotonin 2 A receptor density maps. Compared to placebo, all substances reduced integrity in several networks, indicating both common and unique effects. While LSD uniquely reduced integrity in the default-mode network (DMN), the amphetamines, in contrast to our expectations, reduced integrity in more networks than LSD. However, LSD exhibited more pronounced segregation effects, characterized solely by decreases, in contrast to the amphetamines, which also induced increases. Across all substances, seed-based connectivity mostly increased between networks, with LSD demonstrating more pronounced effects than both amphetamines. Finally, while all substances decreased global connectivity in visual areas, compared to placebo, LSD specifically increased global connectivity in the basal ganglia and thalamus. These findings advance our understanding of the distinctive neurobiological effects of psychedelics, prompting further exploration of their therapeutic potential.

Resting-state brain networks alterations in adolescents with Internet Gaming Disorder associate with cognitive control impairments.

Research indicates that cognitive control is compromised in individuals with internet gaming disorder (IGD). However, the neural mechanisms behind it are still unclear. This study aims to investigate alterations in resting-state brain networks in adolescents with IGD and the potential neurobiological mechanisms underlying cognitive dysfunction. A total of 44 adolescent IGD subjects (male/female: 38/6) and 50 healthy controls (male/female: 40/10) were enrolled. Participants underwent demographic assessments, Young's Internet Addiction Scale, Barratt Impulsiveness Scale 11 Chinese Revised Version, the Chinese Adolescents' Maladaptive Cognitions Scale, exploratory eye movement tests, and functional magnetic resonance imaging (fMRI). FMRI data were analyzed using the GIFT software for independent component analysis, focusing on functional connectivity within and between resting-state brain networks. In comparison to the control group, impulsivity in adolescent IGD subjects showed a positive correlation with the severity of IGD (r=0.6350, p < 0.001), linked to impairments in the Executive Control Network (ECN) and a decrease in functional connectivity between the Salience Network (SN) and ECN (r=0.4307, p=0.0021; r=-0.5147, p=0.0034). Decreased resting state activity of the dorsal attention network (DAN) was associated with attentional dysregulation of IGD in adolescents (r=0.4071, p=0.0017), and ECN increased functional connectivity with DAN. The degree of IGD was positively correlated with enhanced functional connectivity between the ECN and DAN (r=0.4283, p=0.0037). This research demonstrates that changes in the ECN and DAN correlate with heightened impulsivity and attentional deficits in adolescents with IGD. The interaction between cognitive control disorders and resting-state brain networks in adolescent IGD is related.

Eight brain structures mediate the age-related alterations of the working memory: forward and backward digit span tasks.

Working memory (WM) as one of the executive functions is an essential neurocognitive ability for daily life. Findings have suggested that aging is often associated with working memory and neural decline, but the brain structures and resting-state brain networks that mediate age-related differences in WM remain unclear. A sample consisting of 252 healthy participants in the age range of 20 to 70years was used. Several cognitive tasks, including the n-back task and the forward and backward digit span tests were used. Also, resting-state functional imaging, as well as structural imaging using a 3T MRI scanner, were performed, resulting in 85 gray matter volumes and five resting-state networks, namely the anterior and posterior default mode, the right and left executive control, and the salience networks. Also, mediation analyses were used to investigate the role of gray matter volumes and resting-state networks in the relationship between age and WM. Behaviorally, aging was associated with decreased performance in the digit span task. Also, aging was associated with a decreased gray matter volume in 80 brain regions, and with a decreased activity in the anterior default mode network, executive control, and salience networks. Importantly, the path analysis showed that the GMV of the medial orbitofrontal, precentral, parieto-occipital, amygdala, middle occipital, posterior cingulate, and thalamus areas mediated the age-related differences in the forward digit span task, and the GMV of superior temporal gyrus mediated the age-related differences in the backward digit span task. This study identified the brain structures mediating the relationship between age and working memory, and we hope that our research provides an opportunity for early detection of individuals at risk of age-related memory decline.

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

A brain structure learning-guided multi-view graph representation learning for brain network analysis.

Resting-state brain networks represent the interconnectivity of different brain regions during rest. Utilizing brain network analysis methods to model these networks can enhance our understanding of how different brain regions collaborate and communicate without explicit external stimuli. However, analyzing resting-state brain networks faces challenges due to high heterogeneity and noise correlation between subjects. This study proposes a brain structure learning-guided multi-view graph representation learning method to address the limitations of current brain network analysis and improve the diagnostic accuracy (ACC) of mental disorders. We first used multiple thresholds to generate different sparse levels of brain networks. Subsequently, we introduced graph pooling to optimize the brain network representation by reducing noise edges and data inconsistency, thereby providing more reliable input for subsequent graph convolutional networks (GCNs). Following this, we designed a multi-view GCN to comprehensively capture the complexity and variability of brain structure. Finally, we employed an attention-based adaptive module to adjust the contributions of different views, facilitating their fusion. Considering that the Smith atlas offers superior characterization of resting-state brain networks, we utilized the Smith atlas to construct the graph network. Experiments on two mental disorder datasets, the Autism Brain Imaging Data Exchange (ABIDE) dataset and the Mexican Cocaine Use Disorders (SUDMEX CONN) dataset, show that our model outperforms the state-of-the-art methods, achieving nearly 75% ACC and 70% area under the receiver operating characteristic curve (AUC) on both datasets. These findings demonstrate that our method of combining multi-view graph learning and brain structure learning can effectively capture crucial structural information in brain networks while facilitating the acquisition of feature information from diverse perspectives, thereby improving the performance of brain network analysis.

Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss.

Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3years. Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.

Resting-state brain network analysis and applied evaluation of global developmental delay in preterm infants: a functional near-infrared spectroscopic study

Global developmental delay (GDD) is a common neurodevelopmental disorder of in children under 5 years of age. This study assessed the potential of functional Near-Infrared Spectroscopy (fNIRS) technology to aid diagnosis of GDD in premature infants and explored brain pathogenesis of GDD. This prospective cohort study was conducted between December 2020 and June 2023 among preterm infants in five hospitals in China. The primary outcome was the diagnosis of GDD. A 5-minute fNIRS brain scan was performed in preterm infants at 4-, 8-, and 12-month corrected age in two hospitals. A diagnostic model based on fNIRS brain network and an integrated diagnostic model built on fNIRS data as well as birth head circumference was developed. Diagnostic models were assessed using the area under the curve (AUC) from receiver operating characteristic curves. Brain network analysis revealed significantly worse left-sided functional connectivity in GDD preterm infants. The fNIRS technique had satisfactory diagnostic value for GDD in all age groups. The diagnostic accuracy of the combined diagnostic model significantly improved. This study suggested that fNIRS could be considered for early screening and late auxiliary diagnosis of GDD in preterm infants. Furthermore, it can also be used to explore GDD brain pathogenesis.

Нейровизуализационный подход для выявления биомаркеров рабочей памяти у больных с хронической ишемией мозга

Verbal working memory (VWM) is a fundamental function responsible for temporary storage and short-term handling of verbal information. The study was aimed to determine the working memory biomarker associated with imaging of the source of infra-slow electrical activity in patients with chronic cerebral ischemia (CCI). A total of 50 patients with CCI took part in the study: 16 males and 34 females aged 50–85 years. VWM was evaluated by the Luria test. The subjects were divided into two groups matched by age with the VWM below and above the average level for the studied sample. The infra-slow, below 0.1 Hz, electrical activity, otherwise known as the DC potentials (DCPs) of the brain, was recorded with five monopolar leads: frontal, central, occipital, right and left temporal. The resting state fMRI was used to analyze brain regions with the activated BOLD (blood-oxygen-level-dependent) signal that were associated with the brain regions responsible for VWM and the DCP generation sources recorded with the non-polarizable electrodes. The differences in BOLD signal activation and infra-slow activity amplitude were found in two VWM groups. These resting-state neural networks, VWM and the neural network responsible for DCP generation, overlapped in frontal regions. There were significant differences in DCP recorded with the frontal lead in two VWM groups (р = 0.00004). In patients with CCI, infra-slow activity, recorded with the frontal lead that is generated by the neural network fragment representing an intersection of the VWM network and the part of the brain responsible for DCP generation in the frontal region, is a VWM biomarker.

Longitudinal development of resting-state functional connectivity in adolescents with and without internalizing disorders

Longitudinal studies using resting-state functional magnetic resonance imaging (rs-fMRI) focused on adolescent internalizing psychopathology are scarce and have mostly investigated standardized treatment effects on functional connectivity (FC) of the full amygdala. The role of amygdala subregions and large resting-state networks had yet to be elucidated, and treatment is in practice often personalized. Here, longitudinal FC development of amygdala subregions and whole-brain networks are investigated in a clinically representative sample.Treatment-naïve adolescents with clinical depression and comorbid anxiety who started care-as-usual (n = 23; INT) and healthy controls (n = 24; HC) participated in rs-fMRI scans and questionnaires at baseline (before treatment) and after three months. Changes between and within groups over time in FC of the laterobasal amygdala (LBA), centromedial amygdala (CMA) and whole-brain networks derived from independent component analysis (ICA) were investigated.Groups differed significantly in FC development of the right LBA to the postcentral gyrus and the left LBA to the frontal pole. Within INT, FC to the frontal pole and postcentral gyrus changed over time while changes in FC of the right LBA were also linked to symptom change. No significant interactions were observed when considering FC from CMA bilateral seeds or within ICA-derived networks.Results in this cohort suggest divergent longitudinal development of FC from bilateral LBA subregions in adolescents with internalizing disorders compared to healthy peers, possibly reflecting nonspecific treatment effects. Moreover, associations were found with symptom change. These results highlight the importance of differentiation of amygdala subregions in neuroimaging research in adolescents.

Associations Between Changes in Psychological Resilience and Resting-State Functional Connectivity Throughout Pediatric Concussion Recovery.

Purpose: This study investigated the association between psychological resilience and resting-state network functional connectivity of three major brain networks in pediatric concussion. Methods: This was a substudy of a randomized controlled trial, recruiting children with concussion and orthopedic injury. Participants completed the Connor-Davidson Resilience 10 Scale and underwent magnetic resonance imaging at 72 h and 4-weeks postinjury. We explored associations between resilience and connectivity with the default mode network (DMN), central executive network (CEN), and salience network (SN) at both timepoints and also any change that occurred over time. We also explored associations between resilience and connectivity within each network. Results: A total of 67 children with a concussion (median age = 12.87 [IQR: 11.79-14.36]; 46% female) and 30 with orthopedic injury (median age = 12.27 [IQR: 11.19-13.94]; 40% female) were included. Seed-to-voxel analyses detected a positive correlation between 72-h resilience and CEN connectivity in the concussion group. Group moderated associations between resilience and SN connectivity at 72 h, as well as resilience and DMN connectivity over time. Regions-of-interest analyses identified group as a moderator of longitudinal resilience and within-DMN connectivity. Conclusions: These results suggest that neural recovery from concussion could be reliant on resilience. Resilience was related to functional connectivity with three of the main networks in the brain that are often impacted by concussion. Improving resilience might be investigated as a modifiable variable in children as both a protective and restorative in the context of concussion. Clinical Trial Registration Identifier: NCT05105802. PedCARE+MRI team (see Supplementary Appendix S1).

A multi-modal assessment of self-knowledge in adolescents with non-suicidal self-injury: a research domains criteria (RDoC) study.

Adolescence is a critical period for brain development, consolidation of self-understanding, and onset of non-suicidal self-injury (NSSI). This study evaluated the RDoC (Research Domain Criteria) sub-construct of Self-Knowledge in relation to adolescent NSSI using multiple units of analysis. One hundred and sixty-four adolescents assigned female at birth (AFAB), ages 12-16 years with and without a history of NSSI entered a study involving clinical assessment and magnetic resonance imaging (MRI), including structural, resting-state functional MRI (fMRI), and fMRI during a self-evaluation task. For imaging analyses, we used an a priori defined Self Network (anterior cingulate, orbitofrontal, and posterior cingulate cortices; precuneus). We first examined interrelationships among multi-level Self variables. We then evaluated the individual relationships between NSSI severity and multi-level Self variables (self-report, behavior, multi-modal brain Self Network measures), then conducted model testing and multiple regression to test how Self variables (together) predicted NSSI severity. Cross-correlations revealed key links between self-reported global self-worth and self-evaluation task behavior. Individually, greater NSSI severity correlated with lower global self-worth, more frequent and faster negative self-evaluations, lower anterior Self Network activation during self-evaluation, and lower anterior and posterior Self Network resting-state connectivity. Multiple regression analysis revealed the model including multi-level Self variables explained NSSI better than a covariate-only model; the strongest predictive variables included self-worth, self-evaluation task behavior, and resting-state connectivity. Disruptions in Self-Knowledge across multiple levels of analysis relate to NSSI in adolescents. Findings suggest potential neurobiological treatment targets, potentially enhancing neuroplasticity in Self systems to facilitate greater flexibility (more frequently positive) of self-views in AFAB adolescents.

The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI.

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.

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.