Network Connectivity Patterns Research Articles

Unique Brain Network Identification Number for Parkinson's and Healthy Individuals Using Structural MRI.

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson's disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2-(i-1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person's unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33-42 years (n1 = 18, n2 = 14), C: 43-52 years (n1 = 42, n2 = 23), D: 53-62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.

Disrupted resting-state functional connectivity and network topology in mild traumatic brain injury: an arterial spin labelling study.

Mild traumatic brain injury can cause different degrees of cognitive impairment and abnormal brain structure and functional connectivity, but there is still a lack of research on the functional connectivity and topological organization of cerebral blood flow fluctuations. This study explored the cerebral blood flow, functional connectivity and topological organization of the cerebral blood flow network in acute mild traumatic brain injury patients. In total, 48 mild traumatic brain injury patients and 46 well-matched healthy controls underwent resting-state arterial spin labelling perfusion MRI and neuropsychological assessments. The functional connectivity and topological organization of the cerebral blood flow network were analysed. Then, the correlation between the changes in cerebral blood flow network characteristics and cognitive function was explored. Acute mild traumatic brain injury patients showed decreased cerebral blood flow in the right insula and increased cerebral blood flow in the right inferior temporal gyrus and left superior temporal gyrus. Abnormal cerebral blood flow network connection patterns mainly occur in sensorimotor network, default mode network, cingulo-opercular network and occipital network-related regions. Furthermore, mild traumatic brain injury disrupted the topological organization of the whole brain, which manifested as (i) reduced global efficiency; (ii) abnormal degree centrality, betweenness centrality, nodal clustering coefficient and nodal efficiency; and (iii) decreased intermodular connectivity between the occipital network and sensorimotor network. Finally, the change in network topology was correlated with the cognitive score of the mild traumatic brain injury. This study provided evidence of abnormal functional connectivity and network topology based on cerebral blood flow in acute mild traumatic brain injury patients, revealing their potential use as early markers for mild traumatic brain injury, which may contribute to both disease diagnosis and assessment.

Different patterns of intrinsic functional connectivity at the default mode and attentional networks predict crystalized and fluid abilities in childhood.

Crystallized abilities are skills used to solve problems based on experience, while fluid abilities are linked to reasoning without evoke prior knowledge. To what extent crystallized and fluid abilities involve dissociated or overlapping neural systems is debatable. Due to often deployed small sample sizes or different study settings in prior work, the neural basis of crystallized and fluid abilities in childhood remains largely unknown. Here we analyzed within and between network connectivity patterns from resting-state functional MRI of 2707 children between 9 and 10years from the ABCD study. We hypothesized that differences in functional connectivity at the default mode network (DMN), ventral, and dorsal attentional networks (VAN, DAN) explain differences in fluid and crystallized abilities. We found that stronger between-network connectivity of the DMN and VAN, DMN and DAN, and VAN and DAN predicted crystallized abilities. Within-network connectivity of the DAN predicted both crystallized and fluid abilities. Our findings reveal that crystallized abilities rely on the functional coupling between attentional networks and the DMN, whereas fluid abilities are associated with a focal connectivity configuration at the DAN. Our study provides new evidence into the neural basis of child intelligence and calls for future comparative research in adulthood during neuropsychiatric diseases.

Inflammation affects dynamic functional network connectivity pattern changes via plasma NFL in cognitive impairment patients.

Plasma neurofilament light chain (NFL) is a biomarker of inflammation and neurodegenerative diseases such as Alzheimer's disease (AD). However, the underlying neural mechanisms by which NFL affects cognitive function remain unclear. In this study, we investigated the effects of inflammation on cognitive integrity in patients with cognitive impairment through the functional interaction of plasma NFL with large-scale brain networks. This study included 29 cognitively normal, 55 LowNFL patients, and 55 HighNFL patients. Group independent component analysis (ICA) was applied to the resting-state fMRI data, and 40 independent components (IC) were extracted for the whole brain. Next, the dynamic functional network connectivity (dFNC) of each subject was estimated using the sliding-window method and k-means clustering, and five dynamic functional states were identified. Finally, we applied mediation analysis to investigate the relationship between plasma NFL and dFNC indicators and cognitive scales. The present study explored the dynamics of whole-brain FNC in controls and LowNFL and HighNFL patients and highlighted the temporal properties of dFNC states in relation to psychological scales. A potential mechanism for the association between dFNC indicators and NFL levels in cognitively impaired patients. Our findings suggested the decreased ability of information processing and communication in the HighNFL group, which helps us to understand their abnormal cognitive functions clinically. Characteristic changes in the inflammation-coupled dynamic brain network may provide alternative biomarkers for the assessment of disease severity in cognitive impairment patients.

Adolescent friendship, cross-sexuality ties, and attitudes toward sexual minorities: A social network approach to intergroup contact

Social ties between members of in- and outgroups are theorized to reduce individual levels of prejudice. However, instances of intergroup contact are not isolated events; cross-group interactions are embedded in broader networks defined by various social processes that guide the formation and maintenance of interpersonal relationships. This project reconsiders the potential benefits of intergroup contact by applying a network perspective to examine whether friendships between youth of different sexualities can shape individuals’ homophobic attitudes. The impact of cross-sexuality ties is evaluated through the application of stochastic actor-oriented models (SAOMs) to a two-wave sample of Dutch adolescents. Results indicate that the benefits of cross-sexuality connections become negligible when we account for how patterns of network connectivity and segregation are informed by other individual-level traits, such as age, religious background, ethnicity, and gender. In other words, heterosexual adolescents who are situated in network positions that provide opportunities to form cross-sexuality friendships would be expected to report less homophobic attitudes even in the absence of this intergroup contact. These findings suggest that the cross-sexuality contact observed in the social world often represents instances of “preaching to the choir,” limiting the potential for intergroup connections to challenge systems of social inequality.

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

Incorporating structural plasticity into self-organization recurrent networks for sequence learning.

Spiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-dependent plasticity (STDP), structural plasticity, and homeostatic plasticity, making network connection patterns and weights to change continuously during the lifecycle. However, it is unclear how these plasticities interact to shape neural networks and affect neural signal processing. Here, we propose a reward-modulated self-organization recurrent network with structural plasticity (RSRN-SP) to investigate this issue. Specifically, RSRN-SP uses spikes to encode information, and incorporate multiple plasticities including reward-modulated spike timing-dependent plasticity (R-STDP), homeostatic plasticity, and structural plasticity. On the one hand, combined with homeostatic plasticity, R-STDP is presented to guide the updating of synaptic weights. On the other hand, structural plasticity is utilized to simulate the growth and pruning of synaptic connections. Extensive experiments for sequential learning tasks are conducted to demonstrate the representational ability of the RSRN-SP, including counting task, motion prediction, and motion generation. Furthermore, the simulations also indicate that the characteristics arose from the RSRN-SP are consistent with biological observations.

Aberrant functional connectivity within and between brain networks in patients with early-onset bipolar disorder

ObjectiveThis study used independent component analysis (ICA) to investigate the connectivity patterns of resting-state functional large-scale brain networks in patients with early-onset bipolar disorder (BD). MethodsICA was used to extract brain functional network components from 43 early-onset BD patients and 21 healthy controls (HCs). Then, the functional connectivity (FC) and functional network connectivity (FNC) within and between the independent brain networks was calculated, and the correlation between the connectivity changes and neuropsychological scale was evaluated. ResultsCompared with HCs, FC increased in the right hippocampus and inferior temporal gyrus, and left triangular inferior frontal gyrus of the anterior default mode network (aDMN); right median cingulate and paracingulate gyri, and inferior parietal lobule of the posterior DMN (pDMN); and right precentral and postcentral gyrus of the sensorimotor network (SMN) in early-onset BD patients. However, FC decreased in the left superior frontal gyrus of the aDMN, left paracentral lobule of the SMN, and left lingual gyrus and calcarine of the visual network in early-onset BD patients. There was no significant correlation between FC values of differential brain regions within resting-state networks (RSNs) and neuropsychological scores (uncorrected p > 0.05). In addition, the FNC among the pDMN-auditory network, pDMN-visual network, left frontoparietal network (lFPN)-visual network, lFPN-aDMN and dorsal attention network-ventral attention network (DAN-VAN) were increased in early-onset BD patients. The zFNC of the pDMN-visual network was positively correlated with the anxiety/somatization score (r = 0.5833, p < 0.0001) and sleep disorders (r = 0.6150, p < 0.0001). The zFNC of the lFPN-aDMN was positively correlated with despair (r = 0.4505, p = 0.004 × 10 < 0.05 after Bonferroni correction). The zFNC of the DAN-VAN was positively correlated with cognitive impairment (r = 0.4598, p = 0.0032 × 10 < 0.05 after Bonferroni correction). The zFNC of the DAN-VAN showed a positive correlation trend with the Hamilton Depression Scale (HAMD) total score (r = 0.4404, p = 0.005 × 10 = 0.05 after Bonferroni correction). ConclusionsPatients with early-onset BD showed changes in a wide range of neural functional networks, involving changes in executive control, attention, perceptual regulation, cognition and other neural networks, which may provide new imaging evidence for understanding the pathogenesis of early-onset BD and for therapeutic intervention targets.

A rapid theta network mechanism for flexible information encoding

Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum.

Altered dynamic functional network connectivity states in patients with acute basal ganglia ischemic stroke

BackgroundDynamic functional network connectivity (dFNC) patterns are successfully able to capture the time-varying features of intrinsic fluctuations throughout a scan. We explored dFNC alterations across the entire brain in patients with acute ischemic stroke (AIS) of the basal ganglia (BG). MethodResting-state functional magnetic resonance imaging data were acquired from 26 patients with first-ever AIS in the BG and 26 healthy controls (HCs). Independent component analysis, the sliding window method, and the K-means clustering method were used to obtain reoccurring dynamic network connectivity patterns. Moreover, temporal features across diverse dFNC states were compared between the two groups, and the local and global efficiencies across states were analyzed to explore the characteristics of the topological networks among states. ResultsFour dFNC states were characterized for comparison of dynamic brain network connectivity patterns. In contrast to the HC group, the AIS group spent a significantly higher fraction of time in State 1, which is characterized by a relatively weaker brain network connectome. Conversely, compared with HC, patients with AIS showed a lower mean dwell time in State 2, which was characterized by a relatively stronger brain network connectome. Additionally, functional networks exhibited variable efficiency of information transfer across 4 states. ConclusionsAIS not only altered the interaction between the different dynamic networks but also promoted characteristic alterations in the temporal and topological features of large-scale dynamic network connectivity.

Targeting operational regimes of interest in recurrent neural networks.

Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, for spiking networks, it is challenging to predict which connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We establish a mapping between the stabilized supralinear network (SSN) and spiking activity which allows us to pinpoint the location in parameter space where these activity regimes occur. Notably, we find that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we show that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.

Aberrant voxel-based degree centrality and functional connectivity in Parkinson's disease patients with fatigue.

The study aimed to investigate alterations in the inherent connectivity pattern of global functional networks in Parkinson's disease (PD) patients with fatigue. Eighteen PD patients with fatigue (PD-F), 20 PD patients without fatigue (PD-NF), and 23 healthy controls (HCs) were recruited and analyzed by the voxel-wise degree centrality (DC) and the seed-based functional connectivity (FC) analysis. Meanwhile, the surface-based morphometry (SBM) analysis was also commanded to explore the structural alternations among groups. PD-F patients displayed reduced DC values in the left postcentral gyrus relative to PD-NF and HCs groups, while increased DC values in the bilateral precuneus compared to HCs. Simultaneously, altered DC value in the left postcentral gyrus negatively corresponded to the mean fatigue severity scale (FSS) in PD-F patients. Additionally, the receiver operating characteristic (ROC) curves uncovered that the reduced DC value of the left postcentral gyrus could discriminate PD-F from PD-NF and HCs groups. Our FC analysis further revealed that altered FC was located predominantly in the sensorimotor network in the PD-F group. Moreover, we discovered no statistically significant differences between the three groups concerning cortical thickness. Our findings indicated that the altered functional connectivity in the sensorimotor network centering on the left postcentral gyrus and the bilateral precuneus might be the potential pathogenesis of PD with fatigue.

Changes in Patterns of Age-Related Network Connectivity are Associated With Risk for Schizophrenia

Changes in Patterns of Age-Related Network Connectivity are Associated With Risk for Schizophrenia

Cortico-basal ganglia networks dysfunction associated with disease severity in patients with idiopathic blepharospasm.

Structural changes occur in brain regions involved in cortico-basal ganglia networks in idiopathic blepharospasm (iBSP); whether these changes influence the function connectivity patterns of cortico-basal ganglia networks remains largely unknown. Therefore, we aimed to investigate the global integrative state and organization of functional connections of cortico-basal ganglia networks in patients with iBSP. Resting-state functional magnetic resonance imaging data and clinical measurements were acquired from 62 patients with iBSP, 62 patients with hemifacial spasm (HFS), and 62 healthy controls (HCs). Topological parameters and functional connections of cortico-basal ganglia networks were evaluated and compared among the three groups. Correlation analyses were performed to explore the relationship between topological parameters and clinical measurements in patients with iBSP. We found significantly increased global efficiency and decreased shortest path length and clustering coefficient of cortico-basal ganglia networks in patients with iBSP compared with HCs, however, such differences were not observed between patients with HFS and HCs. Further correlation analyses revealed that these parameters were significantly correlated with the severity of iBSP. At the regional level, the functional connectivity between the left orbitofrontal area and left primary somatosensory cortex and between the right anterior part of pallidum and right anterior part of dorsal anterior cingulate cortex was significantly decreased in patients with iBSP and HFS compared with HCs. Dysfunction of the cortico-basal ganglia networks occurs in patients with iBSP. The altered network metrics of cortico-basal ganglia networks might be served as quantitative markers for evaluation of the severity of iBSP.

Heterogeneous brain dynamic functional connectivity patterns in first‐episode drug‐naive patients with major depressive disorder

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Network-level dynamics underlying a combined rTMS and psychotherapy treatment for major depressive disorder: An exploratory network analysis

BackgroundDespite the growing use of repetitive transcranial magnetic stimulation (rTMS) as a treatment for depression, there is a limited understanding of the mechanisms of action and how potential treatment-related brain changes help to characterize treatment response. To address this gap in understanding we investigated the effects of an approach combining rTMS with simultaneous psychotherapy on global functional connectivity. MethodWe compared task-related functional connectomes based on an idiographic goal priming task tied to emotional regulation acquired before and after simultaneous rTMS/psychotherapy treatment for patients with major depressive disorders and compared these changes to normative connectivity patterns from a set of healthy volunteers (HV) performing the same task. ResultsAt baseline, compared to HVs, patients demonstrated hyperconnectivity of the DMN, cerebellum and limbic system, and hypoconnectivity of the fronto-parietal dorsal-attention network and visual cortex. Simultaneous rTMS/psychotherapy helped to normalize these differences, which were reduced after treatment. This finding suggests that the rTMS/therapy treatment regularizes connectivity patterns in both hyperactive and hypoactive brain networks. ConclusionsThese results help to link treatment to a comprehensive model of the neurocircuitry underlying depression and pave the way for future studies using network-guided principles to significantly improve rTMS efficacy for depression.

Altered resting-state network connectivity patterns for predicting attentional function in deaf individuals: An EEG study

Multiple aspects of brain development are influenced by early sensory loss such as deafness. Despite growing evidence of changes in attentional functions for prelingual profoundly deaf, the brain mechanisms underlying these attentional changes remain unclear. This study investigated the relationships between differential attention and the resting-state brain network difference in deaf individuals from the perspective of brain network connectivity. We recruited 36 deaf individuals and 34 healthy controls (HC). We recorded each participant's resting-state electroencephalogram (EEG) and the event-related potential (ERP) data from the Attention Network Test (ANT). The coherence (COH) method and graph theory were used to build brain networks and analyze network connectivity. First, the ERPs of analysis in task states were investigated. Then, we correlated the topological properties of the network functional connectivity with the ERPs. The results revealed a significant correlation between frontal-occipital connection in the resting state and the amplitude of alert N1 amplitude in the alpha band. Specifically, clustering coefficients and global and local efficiency correlate negatively with alert N1 amplitude, whereas the characteristic path length positively correlates with alert N1 amplitude. In addition, deaf individuals exhibited weaker frontal-occipital connections compared to the HC group. In executive control, the deaf group had longer reaction times and larger P3 amplitudes. However, the orienting function did not significantly differ from the HC group. Finally, the alert N1 amplitude in the ANT task for deaf individuals was predicted using a multiple linear regression model based on resting-state EEG network properties. Our results suggest that deafness affects the performance of alerting and executive control while orienting functions develop similarly to hearing individuals. Furthermore, weakened frontal-occipital connections in the deaf brain are a fundamental cause of altered alerting functions in the deaf. These results reveal important effects of brain networks on attentional function from the perspective of brain connections and provide potential physiological biomarkers to predicting attention.

Motif adjacency matrix and spectral clustering of directed weighted networks

&lt;abstract&gt; &lt;p&gt;In the spectral clustering methods, different from the network division based on edges, some research has begun to divide the network based on network motifs; the corresponding objective function of partition also becomes related to the motif information. But, the related research on the directed weighted network needs to be further deepened. The weight of the network has a great influence on the structural attributes of the network, so it is necessary to extend the motif-based clustering to the weighted network. In this paper, a motif-based spectral clustering method for directed weighted networks is proposed. At the same time, this paper supplements the method of obtaining matrix expressions of the motif adjacency matrix in directed unweighted networks and provides a method to deal with the weight of networks, which will be helpful for the application research of motifs. This clustering method takes into account the higher-order connectivity patterns in networks and broadens the applicable range of spectral clustering to directed weighted networks. In this method, the motif-based clustering of directed weighted networks can be transformed into the clustering of the undirected weighted network corresponding to the motif-based adjacency matrix. The results show that the clustering method can correctly identify the partition structure of the benchmark network, and experiments on some real networks show that this method performs better than the method that does not consider the weight of networks.&lt;/p&gt; &lt;/abstract&gt;

Altered patterns of central executive, default mode and salience network activity and connectivity are associated with current and future depression risk in two independent young adult samples.

Neural markers of pathophysiological processes underlying the dimension of subsyndromal-syndromal-level depression severity can provide objective, biologically informed targets for novel interventions to help prevent the onset of depressive and other affective disorders in individuals with subsyndromal symptoms, and prevent worsening symptom severity in those with these disorders. Greater functional connectivity (FC) among the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. We examined in young adults (1) relationships among activity and FC in these networks and current depression severity, using a paradigm designed to examine WM and ER capacity in n = 90, age = 21.7 (2.0); (2) the extent to which these relationships were specific to depression versus mania/hypomania; (3) whether findings in a first, "discovery" sample could be replicated in a second, independent, "test" sample of young adults n = 96, age = 21.6 (2.1); and (4) whether such relationships also predicted depression at up to 12 months post scan and/or mania/hypomania severity in (n = 61, including participants from both samples, age = 21.6 (2.1)). We also examined the extent to which there were common depression- and anxiety-related findings, given that depression and anxiety are highly comorbid. In the discovery sample, current depression severity was robustly predicted by greater activity and greater positive functional connectivity among the CEN, DMN, and SN during working memory and emotional regulation tasks (all ps < 0.05 qFDR). These findings were specific to depression, replicated in the independent sample, and predicted future depression severity. Similar neural marker-anxiety relationships were shown, with robust DMN-SN FC relationships. These data help provide objective, neural marker targets to better guide and monitor early interventions in young adults at risk for, or those with established, depressive and other affective disorders.

Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.