Default Mode Network Research Articles

Mapping Functional Connectivity Signatures of Pharmacoresistant Focal Cortical Dysplasia-Related Epilepsy.

To determine common network alterations in focal cortical dysplasia pharmacoresistant epilepsy (FCD-PRE) using functional connectivity analysis of resting-state functional magnetic resonance imaging (rsfMRI). This is a retrospective imaging cohort from Children's National Hospital (Washington, DC, USA) from January, 2011 to January, 2022. Patients with 3-T MRI-confirmed FCD-PRE underwent rsfMRI as part of routine clinical care. Patients were included if they were age 5-22 years at the time of the scan, and had a minimum of 18 months of follow-up. Healthy, typically-developing controls were included from Children's National Hospital (n = 16) and matched from Human Connectome Project-Development public dataset (n = 100). A total of 42 FCD-PRE patients (20 M:22 F, aged 14.2 ± 4.1 years) and 116 healthy controls (56 M:60 F, aged 13.7 ± 3.3 years) with rsfMRI were included. Seed-based functional connectivity maps were generated for each FCD, and each seed was used to generate a patient-specific z-scored connectivity map on 116 controls. FCD-PRE patients had mutual altered connectivity in regions of dorsal attention, default mode, and control networks. Functional connectivity was diminished within the FCD dominant functional network, as well as in homotopic regions. Cluster specific connectivity patterns varied by pathological subtype. Higher FCD connectivity to the limbic network was associated with increased odds of Engel I outcome. This study demonstrates diminished functional connectivity patterns in FCD-PRE, which may represent a neuromarker for the disease, independent of FCD location, involving the dorsal attention, default mode, and control functional networks. Higher connectivity to the limbic network is associated with a seizure-free outcome. Future multicenter, prospective studies are needed to allow for much earlier detection of signatures of treatment-resistant epilepsy. ANN NEUROL 2024.

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Investigating changes of functional brain networks in major depressive disorder by graph theoretical analysis of resting-state fMRI

BackgroundMajor Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity. MethodsPreprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox. ResultsThe obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05. LimitationsAll participants were Chinese. ConclusionsCollectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

Altered cerebellar-cerebral dynamic functional connectivity in patients with pontine stroke: a resting-state fMRI study.

Potential changes in patterns of dynamic functional network connections at the cerebellar-cerebral level in pontine infarction (PI) patients remain unclear. The study aimed to investigate the abnormal patterns of dynamic functional connectivity (dFC) between the cerebellar subregions within networks and regions of the cerebral cortex in patients with PI. Forty-six chronic left pontine infarction (LPI), 32 chronic right pontine infarction (RPI), and 50 healthy controls (HCs) were recruited to undergo resting-state fMRI scans. Cerebellar-cerebral dFC was characterized using the sliding window method and seed-based connectivity analyses. Correlations between altered dFC values and clinical variables (The Rey Auditory Verbal Learning Test and Flanker task) in PI patients and healthy controls were investigated. Compared with HCs, the PI groups showed significantly aberrant cerebellar-cerebral dFC between cerebellar subregions within networks and supratentorial cerebral cortex, including executive, default-mode, and motor networks. Furthermore, Correlation analysis showed a decoupling between abnormal dFC and cognitive functions in PI patients. These findings indicate that PI patients are accompanied by damage to cerebellar subregions within networks and cerebellar-cerebral pathways, which may provide a potential target for treatment or an indication of therapeutic efficacy.

Effects of four-week intranasal oxytocin administration on large-scale brain networks in older adults

Oxytocin (OT) is a crucial modulator of social cognition and behavior. Previous work primarily examined effects of acute intranasal oxytocin administration (IN-OT) in younger males on isolated brain regions. Not well understood are (i) chronic IN-OT effects, (ii) in older adults, (iii) on large-scale brain networks, representative of OT's wider-ranging brain mechanisms. To address these research gaps, 60 generally healthy older adults (mean age = 70.12 years, range = 55–83) were randomly assigned to self-administer either IN-OT or placebo twice daily via nasal spray over four weeks. Chronic IN-OT reduced resting-state functional connectivity (rs-FC) of both the right insula and the left middle cingulate cortex with the salience network but enhanced rs-FC of the left medial prefrontal cortex with the default mode network as well as the left thalamus with the basal ganglia–thalamus network. No significant chronic IN-OT effects were observed for between-network rs-FC. However, chronic IN-OT increased selective rs-FC of the basal ganglia–thalamus network with the salience network and the default mode network, indicative of more specialized, efficient communication between these networks. Directly comparing chronic vs. acute IN-OT, reduced rs-FC of the right insula with the salience network and between the default mode network and the basal ganglia–thalamus network, and greater selective rs-FC of the salience network with the default mode network and the basal ganglia–thalamus network, were more pronounced after chronic than acute IN-OT. Our results delineate the modulatory role of IN-OT on large-scale brain networks among older adults.

Modulation of Posterior Default Mode Network Activity During Interoceptive Attention and Relation to Mindfulness

BackgroundInteroceptive attention to internal sensory signals, such as the breath, is fundamental to mindfulness. However, interoceptive attention can be difficult to study, with many studies relying on subjective and retrospective measures. Response consistency is an established method for evaluating variability of attention on exteroceptive attention tasks, but it has rarely been applied to interoceptive attention tasks. MethodsIn this study, we measured consistency of response times on a breath-monitoring task with simultaneous electroencephalography in individuals across the life span (15–91 years of age, N = 324). ResultsWe found that consistency on the breath-monitoring task was positively correlated with attentive performance on an exteroceptive inhibitory control task. Electroencephalography source reconstruction showed that on-task alpha band (8–12 Hz) activity was greater than that measured at rest. Low-consistency/longer breath responses were associated with elevated brain activity compared with high-consistency responses, particularly in posterior default mode network (pDMN) brain regions. pDMN activity was inversely linked with functional connectivity to the frontoparietal network and the cingulo-opercular network on task but not at rest, suggesting a role for these frontal networks in on-task regulation of pDMN activity. pDMN activity within the precuneus region was greater in participants who reported low subjective mindfulness and was adaptively modulated by task difficulty in an independent experiment. ConclusionsElevated pDMN alpha activity serves as an objective neural marker for low-consistency responding during interoceptive breath attention, scales with task difficulty, and is associated with low subjective mindfulness.

Functional Network Connectivity for Components of Depression-Related Psychological Fragility.

Psychological resilience (PR) is known to be inversely associated with depression. While there is a growing body of research examining how depression alters activity across multiple functional neural networks, how differences in PR affect these networks is largely unexplored. This study examines the relationship between PR and functional connectivity in the alpha and beta bands within (and between) eighteen established cortical nodes in the default mode network, the central executive network, and the salience network. Resting-state EEG data from 99 adult participants (32 depressed, 67 non-depressed) were used to measure the correlation between the five factors of PR sourced from the Connor-Davidson Resilience Scale and eLORETA-based measures of coherence and phase synchronisation. Distinct functional connectivity patterns were seen across each resilience factor, with a notable absence of overlapping positive results across the depressed and non-depressed samples. These results indicate that depression may modulate how resilience is expressed in terms of fundamental neural activity.

The impact of insomnia on brain networks topology in depressed patients: A resting-state fMRI study

ObjectiveDepression and insomnia frequently co-occur, but the neural mechanisms between patients with varying degrees of these conditions are not fully understood. The specific topological features and connectivity patterns of this co-morbidity have not been extensively studied. This study aimed to investigate the topological characteristics of topological characteristics and functional connectivity of brain networks in depressed patients with insomnia. MethodsResting-state functional magnetic resonance imaging data from 32 depressed patients with a high level of insomnia (D-HI), 35 depressed patients with a low level of insomnia (D-LI), and 81 healthy controls (HC) were used to investigate alterations in brain topological organization functional networks. Nodal and global properties were analyzed using graph-theoretic techniques, and network-based statistical analysis was employed to identify changes in brain network functional connectivity. ResultsCompared to the HC group, both the D-HI and D-LI groups showed an increase in the global efficiency (Eglob) values, local efficiency (Eloc) was decreased in the D-HI group, and Lambda and shortest path length (Lp) values were decreased in the D-LI group. At the nodal level, the right parietal nodal clustering coefficient (NCp) values were reduced in D-HI and D-LI groups compared to those in HC. The functional connectivity of brain networks in patients with D-HI mainly involves default mode network (DMN)-cingulo-opercular network (CON), DMN-visual network (VN), DMN-sensorimotor network (SMN), and DMN-cerebellar network (CN), while that in patients with D-LI mainly involves SMN-CON, SMN-SMN, SMN-VN, and SMN-CN. The values of the connection between the midinsula and postoccipital gyrus was negatively correlated with scores for early awakening in D-HI. ConclusionThese findings may contribute to our understanding of the underlying neuropsychological mechanisms in depressed patients with insomnia.

Replay-triggered brain-wide activation in humans

The consolidation of discrete experiences into a coherent narrative shapes the cognitive map, providing structured mental representations of our experiences. In this process, past memories are reactivated and replayed in sequence, fostering hippocampal-cortical dialogue. However, brain-wide engagement coinciding with sequential reactivation (or replay) of memories remains largely unexplored. In this study, employing simultaneous EEG-fMRI, we capture both the spatial and temporal dynamics of memory replay. We find that during mental simulation, past memories are replayed in fast sequences as detected via EEG. These transient replay events are associated with heightened fMRI activity in the hippocampus and medial prefrontal cortex. Replay occurrence strengthens functional connectivity between the hippocampus and the default mode network, a set of brain regions key to representing the cognitive map. On the other hand, when subjects are at rest following learning, memory reactivation of task-related items is stronger than that of pre-learning rest, and is also associated with heightened hippocampal activation and augmented hippocampal connectivity to the entorhinal cortex. Together, our findings highlight a distributed, brain-wide engagement associated with transient memory reactivation and its sequential replay.

Brain signatures of catastrophic events: Emotion, salience, and cognitive control.

Anticipatory brain activity makes it possible to predict the occurrence of expected situations. However, events such as traffic accidents are statistically unpredictable and can generate catastrophic consequences. This study investigates the brain activity and effective connectivity associated with anticipating and processing such unexpected, unavoidable accidents. We asked 161 participants to ride a motorcycle simulator while recording their electroencephalographic activity. Of these, 90 participants experienced at least one accident while driving. We conducted both within-subjects and between-subjects comparisons. During the pre-accident period, the right inferior parietal lobe (IPL), left anterior cingulate cortex (ACC), and right insula showed higher activity in the accident condition. In the post-accident period, the bilateral orbitofrontal cortex, right IPL, bilateral ACC, and middle and superior frontal gyrus also showed increased activity in the accident condition. We observed greater effective connectivity within the nodes of the limbic network (LN) and between the nodes of the attentional networks in the pre-accident period. In the post-accident period, we also observed greater effective connectivity between networks, from the ventral attention network (VAN) to the somatomotor network and from nodes in the visual network, VAN, and default mode network to nodes in the frontoparietal network, LN, and attentional networks. This suggests that activating salience-related processes and emotional processing allows the anticipation of accidents. Once an accident has occurred, integration and valuation of the new information takes place, and control processes are initiated to adapt behavior to the new demands of the environment.

Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography.

In response to a growing interest in refining brain connectivity assessments, this study focuses on integrating white matter fiber-specific microstructural properties into structural connectomes. Spanning ages 8-19 years in a developmental sample, it explores age-related patterns of microstructure-informed network properties at both local and global scales. First, the diffusion-weighted signal fraction associated with each tractography-reconstructed streamline was constructed. Subsequently, the convex optimization modeling for microstructure-informed tractography (COMMIT) approach was employed to generate microstructure-informed connectomes from diffusion MRI data. To complete the investigation, network characteristics within eight functionally defined networks (visual, somatomotor, dorsal attention, ventral attention, limbic, fronto-parietal, default mode, and subcortical networks) were evaluated. The findings underscore a consistent increase in global efficiency across child and adolescent development within the visual, somatomotor, and default mode networks (p < 0.005). Additionally, mean strength exhibits an upward trend in the somatomotor and visual networks (p < 0.001). Notably, nodes within the dorsal and ventral visual pathways manifest substantial age-dependent changes in local efficiency, aligning with existing evidence of extended maturation in these pathways. The outcomes strongly support the notion of a prolonged developmental trajectory for visual association cortices. This study contributes valuable insights into the nuanced dynamics of microstructure-informed brain connectivity throughout different developmental stages.

Initial learning in the brain: From rules to action

Abstract We used fMRI to investigate the neural changes and representational dynamics associated with different learning modes during initial learning and subsequent implementation of previously acquired stimulus-response (S-R) associations. We compared instruction-based learning (INS) and trial-and-error learning (TE) via a third observation-based learning (OBS) condition. This was yoked to the TE condition and shared features with both, the INS and TE conditions. During learning, neural changes were observed in the Frontoparietal and Default Mode Networks across learning modes, consistent with a general decrease in cognitive control demand as learning progresses. INS and TE exhibited condition-specific signal changes, which we interpreted in the context of covert motor preparation during INS, and intentional action and increased cognitive control demand during early TE trials, respectively. Multivariate pattern analysis revealed individual rule information in bilateral prefrontal, premotor, and parietal cortices across learning modes. Most regions revealed consistent representations of individual S-R rules between the learning stage and subsequent implementation stage, regardless of the learning mode. This suggests that initially formed S-R rule representations guide task performance during S-R rule implementation, irrespective of how they are acquired. Finally, within the primary motor and sensory cortices, individual S-R rules were decodable during the learning stage not only when motor responses were overtly executed, as in TE, but also in the absence of overt motor execution, as in INS. This finding substantiates previous claims of covert motor preparatory mechanisms during INS.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

BackgroundInternet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. MethodResting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. ResultsIGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. ConclusionThe dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models

BackgroudTemporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE.MethodsResting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database.ResultsFive HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function.ConclusionsThis study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

Diagnostic value of structural, functional and effective connectivity in bipolar disorder.

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS. A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria. Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Neuroimaging correlates of Alzheimer's disease biomarker concentrations in a racially diverse high-risk cohort of middle-aged adults.

In this study, we investigated biomarkers in a midlife, racially diverse, at-risk cohort to facilitate early identification and intervention. We examined neuroimaging measures, including resting state functional magnetic resonance imaging (fMRI), white matter hyperintensity vo (WMH), and hippocampal volumes, alongside cerebrospinal fluid (CSF) markers. Our data set included 76 cognitively unimpaired, middle-aged, Black Americans (N=29, F/M=17/12) and Non-Hispanic White (N=47, F/M=27/20) individuals. We compared cerebrospinal fluid phosphorylated tau141 and amyloid beta (Aβ)42 to fMRI default mode network (DMN) subnetwork connectivity, WMH volumes, and hippocampal volumes. Results revealed a significant race × Aβ42 interaction in Black Americans: lower Aβ42 was associated with reduced DMN connectivity and increased WMH volumes regions but not in non-Hispanic White individuals. Our findings suggest that precuneus DMN connectivity and temporal WMHs may be linked to Alzheimer's disease risk pathology during middle age, particularly in Black Americans. Cerebrospinal fluid (CSF) amyloid beta (Aβ)42 relates to precuneus functional connectivity in Black, but not White, Americans. Higher white matter hyperintensity volume relates to lower CSF Aβ42 in Black Americans. Precuneus may be a hub for early Alzheimer's disease pathology changes detected by functional connectivity.

Neural signatures of default mode network subsystems in first-episode, drug-naive patients with major depressive disorder after 6-week thought induction psychotherapy treatment.

Evidence indicates that the default mode network (DMN) plays a crucial role in the neuropathology of major depressive disorder (MDD). However, the neural signatures of DMN subsystems in MDD after low resistance Thought Induction Psychotherapy (TIP) remain incompletely understood. We collected functional magnetic resonance imaging data from 20 first-episode, drug-naive MDD and 20 healthy controls (HCs). The DMN was segmented into three subsystems and seed-based functional connectivity (FC) was computed. After 6-week treatment, the significantly reduced FCs with the medial temporal lobe memory subsystem in MDD at baseline were enhanced and were comparable to that in HCs. Changed Hamilton Depression Rating Scale scores were significantly related with changed FC between the posterior cingulate cortex (PCC) and the right precuneus (PCUN). Further, changed serotonin 5-hydroxytryptamine levels were significantly correlated with changed FCs between the PCC and the left PCUN, between the posterior inferior parietal lobule and the left inferior temporal gyrus, and between the retrosplenial cortex and the right inferior frontal gyrus, opercular part. Finally, the support vector machine obtained an accuracy of 67.5% to distinguish between MDD at baseline and HCs. These findings may deepen our understanding of the neural basis of the effects of TIP on DMN subsystems in MDD.

Individual variability in neural representations of mind-wandering.

Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population- rather than individual-based inferences owing to limited within-person sampling. Here, three densely sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously unrecognized interindividual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model

BackgroundThe widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD. MethodsThis study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability. ResultsModel constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = − 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN. ConclusionsThis study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.