Intrinsic Connectivity Patterns Research Articles

Aberrant hippocampal intrinsic morphological connectivity patterns in Neuromyelitis Optica Spectrum Disorder with cognitive impairment: insights from an individual-based morphological brain network

Aberrant hippocampal intrinsic morphological connectivity patterns in Neuromyelitis Optica Spectrum Disorder with cognitive impairment: insights from an individual-based morphological brain network

Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: machine learning applied to resting-state fMRI

Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great potential in medical and psychological diagnostics. The aim of the present study was to develop a machine learning tool for the detection of dyslexia in children based on the intrinsic connectivity patterns of different brain networks underlying perception and attention. Here, 117 children (8-12 years old; 58 females; 52 typical readers; TR and 65 children with dyslexia) completed cognitive and reading assessments and underwent 10 minutes of resting-state fMRI. Functional connectivity coefficients between 264 brain regions were used as features for machine learning. Different supervised algorithms were employed for classification of children with and without dyslexia. A classifier trained on dorsal attention network features exhibited the highest performance (accuracy .79, sensitivity .92, specificity .64). Auditory, visual, and fronto-parietal network-based classification showed intermediate accuracy levels (70-75%). These results highlight significant neurobiological differences in brain networks associated with visual attention between TR and children with dyslexia. Distinct neural integration patterns can differentiate dyslexia from typical development, which may be utilized in the future as a biomarker for the presence and/or severity of dyslexia.

Enhancing Fault Diagnosis in Mechanical Systems with Graph Neural Networks Addressing Class Imbalance

Recent advancements in intelligent diagnosis rely heavily on data-driven methods. However, these methods often encounter challenges in adequately addressing class imbalances in the context of the fault diagnosis of mechanical systems. This paper proposes the MeanRadius-SMOTE graph neural network (MRS-GNN), a novel framework designed to synthesize node representations in GNNs to effectively mitigate this issue. Through integrating the MeanRadius-SMOTE oversampling technique into the GNN architecture, the MRS-GNN demonstrates an enhanced capability to learn from under-represented classes while preserving the intrinsic connectivity patterns of the graph data. Comprehensive testing on various datasets demonstrates the superiority of the MRS-GNN over traditional methods in terms of classification accuracy and handling class imbalances. The experimental results on three publicly available fault diagnosis datasets show that the MRS-GNN improves the classification accuracy by 18 percentage points compared to some popular methods. Furthermore, the MRS-GNN exhibits a higher robustness in extreme imbalance scenarios, achieving an AUC-ROC value of 0.904 when the imbalance rate is 0.4. This framework not only enhances the fault diagnosis accuracy but also offers a scalable solution applicable to diverse mechanical and complex systems, demonstrating its utility and adaptability in various operating environments and fault conditions.

Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns

When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.

Circulating PACAP levels are associated with increased amygdala-default mode network resting-state connectivity in posttraumatic stress disorder

The pituitary adenylate cyclase-activating polypeptide (PACAP) system is implicated in posttraumatic stress disorder (PTSD) and related amygdala-mediated arousal and threat reactivity. PTSD is characterized by increased amygdala reactivity to threat and, more recently, aberrant intrinsic connectivity of the amygdala with large-scale resting state networks, specifically the default mode network (DMN). While the influence of PACAP on amygdala reactivity has been described, its association with intrinsic amygdala connectivity remains unknown. To fill this gap, we examined functional connectivity of resting-state functional magnetic resonance imaging (fMRI) in eighty-nine trauma-exposed adults (69 female) screened for PTSD symptoms to examine the association between blood-borne (circulating) PACAP levels and amygdala-DMN connectivity. Higher circulating PACAP levels were associated with increased amygdala connectivity with posterior DMN regions, including the posterior cingulate cortex/precuneus (PCC/Precun) and left angular gyrus (lANG). Consistent with prior work, this effect was seen in female, but not male, participants and the centromedial, but not basolateral, subregions of the amygdala. Clinical association analyses linked amygdala-PCC/Precun connectivity to anxious arousal symptoms, specifically exaggerated startle response. Taken together, our findings converge with previously demonstrated effects of PACAP on amygdala activity in PTSD-related processes and offer novel evidence for an association between PACAP and intrinsic amygdala connectivity patterns in PTSD. Moreover, these data provide preliminary evidence to motivate future work ascertaining the sex- and subregion-specificity of these effects. Such findings may enable novel mechanistic insights into neural circuit dysfunction in PTSD and how the PACAP system confers risk through a disruption of intrinsic resting-state network dynamics.

Disrupted longitudinal restoration of brain connectivity during weight normalization in severe anorexia nervosa

Altered intrinsic brain connectivity of patients with anorexia nervosa has been observed in the acute phase of the disorder, but it remains unclear to what extent these alterations recover during weight normalization. In this study, we used functional imaging data from three time points to probe longitudinal changes in intrinsic connectivity patterns in patients with severe anorexia nervosa (BMI ≤ 15.5 kg/m2) over the course of weight normalization. At three distinct stages of inpatient treatment, we examined resting-state functional connectivity in 27 women with severe anorexia nervosa and 40 closely matched healthy controls. Using network-based statistics and graph-theoretic measures, we examined differences in global network strength, subnetworks with altered intrinsic connectivity, and global network topology. Patients with severe anorexia nervosa showed weakened intrinsic connectivity and altered network topology which did not recover during treatment. The persistent disruption of brain networks suggests sustained alterations of information processing in weight-recovered severe anorexia nervosa.

Intrinsic hippocampal connectivity is associated with individual differences in retrospective duration processing

The estimation of incidentally encoded durations of time intervals (retrospective duration processing) is thought to rely on the retrieval of contextual information associated with a sequence of events, automatically encoded in medial temporal lobe regions. “Time cells” have been described in the hippocampus (HC), encoding the temporal progression of events and their duration. However, whether the HC supports explicit retrospective duration judgments in humans, and which neural dynamics are involved, is still poorly understood. Here we used resting-state fMRI to test the relation between variations in intrinsic connectivity patterns of the HC, and individual differences in retrospective duration processing, assessed using a novel task involving the presentation of ecological stimuli. Results showed that retrospective duration discrimination performance predicted variations in the intrinsic connectivity of the bilateral HC with the right precentral gyrus; follow-up exploratory analyses suggested a role of the CA1 and CA4/DG subfields in driving the observed pattern. Findings provide insights on neural networks associated with implicit processing of durations in the second range.

Aberrant intrinsic hippocampal and orbitofrontal connectivity in drug-naive adolescent patients with major depressive disorder.

Alterations in resting-state functional connectivity (rsFC) of hippocampus and orbitofrontal cortex (OFC) have been highly implicated in major depressive disorder (MDD) and the researches have penetrated to the subregional level. However, relatively little is known about the intrinsic connectivity patterns of these two regions in adolescent MDD (aMDD), especially that of their functional subregions. Therefore, in the current study, we recruited 68 first-episode drug-naive aMDD patients and 43 matched typically developing controls (TDC) to characterize the alterations of whole-brain rsFC patterns in hippocampus and OFC at both regional and subregional levels in aMDD. The definition of specific functional subregions in hippocampus and OFC were based on the prior functional clustering-analysis results. Furthermore, the relationship between rsFC alterations and clinical features was also explored. Compared to TDC group, aMDD patients showed decreased connectivity of the left whole hippocampus with bilateral OFC and right inferior temporal gyrus at the regional level and increased connectivity between one of the right hippocampal subregions and right posterior insula at the subregional level. Reduced connectivity of OFC was only found in the subregion of left OFC with left anterior insula extending to lenticula in aMDD patients relative to TDC group. Our study identifies that the aberrant hippocampal and orbitofrontal rsFC was predominantly located in the insular cortex and could be summarized as an altered hippo-orbitofrontal-insular circuit in aMDD, which may be the unique features of brain network dysfunction in depression at this particular age stage. Moreover, we observed the distinct rsFC alterations in adolescent depression at the subregional level, especially the medial and lateral OFC.

Affective Enhancement of Episodic Memory Is Associated With Widespread Patterns of Intrinsic Functional Connectivity in the Brain Across the Adult Lifespan.

Subjectively arousing experiences tend to be better remembered than neutral ones. While numerous task-related neuroimaging studies have revealed the neural mechanisms associated with this phenomenon, it remains unclear how variability in the extent to which individuals show superior memory for subjectively arousing stimuli is associated with the intrinsic functional organization of their brains. Here, we addressed this issue using functional magnetic resonance imaging data collected at rest from a sample drawn from the Cambridge Centre for Ageing and Neuroscience cohort (N = 269, 18–86 years). Specifically, we performed multi-voxel pattern analysis of intrinsic functional connectivity, an unbiased, data-driven approach to examine whole-brain voxel-wise connectivity patterns. This technique allowed us to reveal the most important features from the high-dimensional, whole-brain connectivity structure without a priori hypotheses about the topography and direction of functional connectivity differences. Behaviorally, both item and associative memory accuracy were enhanced for trials with affectively arousing (positive or negative) stimuli than those with neutral ones. Whole-brain multi-voxel pattern analysis of functional connectivity revealed that the affective enhancement of memory was associated with intrinsic connectivity patterns of spatially distributed brain regions belonging to several functional networks in the cerebral cortex. Post hoc seed-based brain-behavior regression analysis and principal component analysis of the resulting correlation maps showed that these connectivity patterns were in turn primarily characterized by the involvement of heteromodal association and paralimbic (dorsal attention, salience, and default mode) networks of the cerebral cortex as well as select subcortical structures (striatum, thalamus, and cerebellum). Collectively, these findings suggest that the affective enhancement of episodic memory may be characterized as a whole-brain phenomenon, possibly supported by intrinsic functional interactions across several networks and structures in the brain.

Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

Effects of Smoking Status and State on Intrinsic Connectivity

BackgroundSmoking behavior during the first 24 hours of a quit attempt is a significant predictor of longer-term abstinence, yet little is known about the neurobiology of early tobacco abstinence. Specifically, the effects of acute tobacco deprivation and reinstatement on brain function—particularly at the level of large-scale network dynamics and assessed across the entire brain—remain incompletely understood. To address this gap, this study used a mixed within- and between-subjects design to assess the effects of smoking status (yes/no smoker) and state (deprived vs. satiated) on whole-brain patterns of intrinsic connectivity. MethodsParticipants included 42 tobacco smokers who underwent resting-state functional magnetic resonance imaging following overnight abstinence (deprived state) and following smoking reinstatement (satiated state, randomized order across participants). Sixty healthy control nonsmokers underwent a single resting-state scan using the same acquisition parameters. Functional connectivity data were analyzed using both a canonical network-of-interest approach and a whole-brain, data-driven approach, i.e., intrinsic connectivity distribution. ResultsNetwork-of-interest–based analyses indicated decreased functional connectivity within frontoparietal and salience networks among smokers relative to nonsmokers as well as effects of smoking state on default mode connectivity. In addition, intrinsic connectivity distribution analyses identified novel between-group differences in subcortical-cerebellar and corticocerebellar networks that were largely smoking state dependent. ConclusionsThese data demonstrate the importance of considering smoking state and the utility of using both theory- and data-driven analysis approaches. These data provide much-needed insight into the functional neurobiology of early abstinence, which may be used in the development of novel treatments.

Altered Insular Subregional Connectivity Associated With Cognitions for Distinguishing the Spectrum of Pre-clinical Alzheimer's Disease.

Background: Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are regarded as part of the pre-clinical Alzheimer's disease (AD) spectrum. The insular subregional networks are thought to have diverse intrinsic connectivity patterns that are involved in cognitive and emotional processing. We set out to investigate convergent and divergent altered connectivity patterns of the insular subregions across the spectrum of pre-clinical AD and evaluated how well these patterns can differentiate the pre-clinical AD spectrum.Method: Functional connectivity (FC) analyses in insular subnetworks were carried out among 38 patients with SCD, 56 patients with aMCI, and 55 normal controls (CNs). Logistic regression analyses were used to construct models for aMCI and CN, as well as SCD and CN classification. Finally, we conducted correlation analyses to measure the relationship between FCs of altered insular subnetworks and cognition.Results: Patients with SCD presented with reduced FC in the bilateral cerebellum posterior lobe and increased FC in the medial frontal gyrus and the middle temporal gyrus. On the other hand, patients with aMCI largely presented with decreased FC in the bilateral inferior parietal lobule, the cerebellum posterior lobe, and the anterior cingulate cortex, as well as increased FC in the medial and inferior frontal gyrus, and the middle and superior temporal gyrus. Logistic regression analyses indicated that a model composed of FCs among altered insular subnetworks in patients with SCD was able to appropriately classify 83.9% of patients with SCD and CN, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.876, 81.6% sensitivity, and 81.8% specificity. A model consisting of altered insular subnetwork FCs in patients with aMCI was able to appropriately classify 86.5% of the patients with aMCI and CNs, with an AUC of 0.887, 80.4% sensitivity, and 83.6% specificity. Furthermore, some of the FCs among altered insular subnetworks were significantly correlated with episodic memory and executive function.Conclusions: Patients with SCD and aMCI are likely to share similar convergent and divergent altered intrinsic FC patterns of insular subnetworks as the pre-clinical AD spectrum, and presented with abnormalities among subnetworks. Based on these abnormalities, individuals can be correctly differentiated in the pre-clinical AD spectrum. These results suggest that alterations in insular subnetworks can be utilized as a potential biomarker to aid in conducting a clinical diagnosis of the spectrum of pre-clinical AD.

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification

Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In this study, we first investigated statistical differences in large-scale resting-state networks (RSNs) on 192 healthy controls (HCs) and 103 ASD patients by using independent component analysis (ICA). Second, an image-based meta-analysis (IBMA) was applied to discover the consistency of spatial patterns from different sites. Last, utilizing these patterns as features, we used Support Vector Machine (SVM) classifier to identify whether a subject was suffering from ASD or not. As a result, six RSNs were obtained with ICA. In each RSN, we identified altered functional connectivity between ASD and HC across the multi-site data. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine the classification performance. The AUC value of classification reaches 0.988. In conclusion, the present study indicates that intrinsic connectivity patterns produced from rsfMRI data could yield a possible biomarker of ASD and contributed to the neurobiology of ASD.

Aberrant intrinsic connectivity in women victims of sexual assault.

This study aims to determine if resting-state functional connectivity may represent a marker for the progression of posttraumatic stress disorder (PTSD) in women victims of sexual assault. Participants were 25 adult women recruited three weeks following exposure to sexual assault (T1) and 19 age-matched healthy, non trauma-exposed controls (HC). Among the victims, 10 participants met (PTSD) and 15 did not meet (trauma-exposed controls, TEC) DSM-IV criteria for PTSD six months post-trauma (T2). At both visits, patterns of intrinsic connectivity, a measure of network centrality at each voxel of the brain, were derived from resting-state functional magnetic resonance imaging. Compared to both the HC and TEC groups, victims who developed PTSD at T2 showed higher centrality in the right middle/superior occipital gyrus at T1, while reduced centrality of the posterior cingulate cortex (PCC)/precuneus at T1 was found for the TEC group, compared to the HC group only. There were no differences in intrinsic connectivity at T1 between the TEC and PTSD groups. There were no significant between-group differences in intrinsic connectivity at T2, and no significant group-by-time interaction. This study indicates that increased occipital centrality three weeks post-trauma exposure may represent a marker of the later development of PTSD. On the other hand, reduced centrality of the PCC/precuneus may represent a marker of resilience to trauma exposure.

Regional homogeneity and degree of centrality in social jetlag and sleep deprivation and their correlations with appetite: a resting-state fMRI study

ABSTRACT This research aimed to explore in free-living condition, in healthy normal weight subjects, the effect of social jetlag (SJL) and sleep deprivation on resting-state brain connectivity and synchronization, as assessed by resting-state fMRI, and their association with appetite. We found abnormal local functional synchronization in resting-state, as depicted by altered regional homogeneity (ReHo), in posterior cerebellar regions in subjects with SJL and/or sleep deprivation. We also found altered intrinsic global connectivity patterns, as depicted by altered degree of centrality (DC), in cerebellar regions in these subjects. In our sample, ReHo was correlated with appetite in fasting state in subjects with SJL and in those with sleep deprivation, while DC was correlated with appetite only in subjects with SJL. The effect size evaluation showed that 21.1% to 35.7% of the variance in appetite of subjects with SJL was explained by altered ReHo and DC; in subjects with sleep deprivation 21.2% to 61.3% of the variance in appetite was explained by altered ReHo. This analysis provides the first evidence of abnormal resting-state local functional synchronization and intrinsic global connectivity patterns in healthy subjects with SJL in real-life settings. These changes were independent of sleep deprivation and correlated with appetite.

Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture

BackgroundDespite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior molecular imaging in healthy populations. ResultsTenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

Association of γ-aminobutyric acid and glutamate/glutamine in the lateral prefrontal cortex with patterns of intrinsic functional connectivity in adults.

This study examined how levels of neurotransmitters in the lateral prefrontal cortex (LPFC), a region underlying higher-order cognition, are related to the brain's intrinsic functional organization. Using magnetic resonance spectroscopy (MRS), GABA+ and Glx (glutamate + glutamine) levels in the left dorsal (DLPFC) and left ventral (VLPFC) lateral prefrontal cortex were obtained in a sample of 64 female adults (mean age = 48.5). We measured intrinsic connectivity via resting-state fMRI in three ways: (a) via seed-based connectivity for each of the two spectroscopy voxels; (b) via the spatial configurations of 17 intrinsic networks defined by a well-known template; and (c) via examination of the temporal inter-relationships between these intrinsic networks. The results showed that different neurotransmitter indexes (Glx-specific, GABA+-specific, Glx-GABA+ average and Glx-GABA+ ratio) were associated with distinct patterns of intrinsic connectivity. Neurotransmitter levels in the left LPFC are mainly associated with connectivity of right hemisphere prefrontal (e.g., DLPFC) or striatal (e.g., putamen) regions, two areas of the brain connected to LPFC via large white matter tracts. While the directions of these associations were mixed, in most cases, higher Glx levels are related to reduced connectivity. Prefrontal neurotransmitter levels are also associated with the degree of connectivity between non-prefrontal regions. These results suggest robust relationships between the brain's intrinsic functional organization and local neurotransmitters in the LPFC which may be constrained by white matter neuroanatomy.

Resting-State Network Patterns Underlying Cognitive Function in Bipolar Disorder: A Graph Theoretical Analysis.

Background: Synchronous and antisynchronous activity between neural elements at rest reflects the physiological processes underlying complex cognitive ability. Regional and pairwise connectivity investigations suggest that perturbations in these activity patterns may relate to widespread cognitive impairments seen in bipolar disorder (BD). Here we take a network-based perspective to more meaningfully capture interactions among distributed brain regions compared to focal measurements and examine network-cognition relationships across a range of commonly affected cognitive domains in BD in relation to healthy controls. Methods: Resting-state networks were constructed as matrices of correlation coefficients between regionally averaged resting-state time series from 86 cortical/subcortical brain regions (FreeSurferv5.3.0). Cognitive performance measured using the Wechsler Adult Intelligence Scale, Cambridge Automated Neuropsychological Test Battery (CANTAB), and Reading the Mind in the Eyes tests was examined in relation to whole-brain connectivity measures and patterns of connectivity using a permutation-based statistical approach. Results: Faster response times in controls (n = 49) related to synchronous activity between frontal, parietal, cingulate, temporal, and occipital regions, while a similar response times in BD (n = 35) related to antisynchronous activity between regions of this subnetwork. Across all subjects, antisynchronous activity between the frontal, parietal, temporal, occipital, cingulate, insula, and amygdala regions related to improved memory performance. No resting-state subnetworks related to intelligence, executive function, short-term memory, or social cognition performance in the overall sample or in a manner that would explain deficits in these facets in BD. Conclusions: Our results demonstrate alterations in the intrinsic connectivity patterns underlying response timing in BD that are not specific to performance or errors on the same tasks. Across all individuals, no strong effects of resting-state global topology on cognition are found, while distinct functional networks supporting episodic and spatial memory highlight intrinsic inhibitory influences present in the resting state that facilitate memory processing. Impact Statement Regional and pairwise-connectivity investigations suggest altered interactions between brain areas may contribute to impairments in cognition that are observed in bipolar disorder. However, the distributed nature of these interactions across the brain remains poorly understood. Using recent advances in network neuroscience, we examine functional connectivity patterns associated with multiple cognitive domains in individuals with and without bipolar disorder. We discover distinct patterns of connectivity underlying response-timing performance uniquely in bipolar disorder and, independent of diagnosis, inhibitory interactions that relate to memory performance.

Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders.

Little is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk. Participants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups. Several fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1-4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79-88%). These results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.

Degrees of lateralisation in semantic cognition: Evidence from intrinsic connectivity

The semantic network is thought to include multiple components, including heteromodal conceptual representations and semantic control processes that shape retrieval to suit the circumstances. Much of this network is strongly left-lateralised; however, work to date has not considered whether separable components of semantic cognition have different degrees of lateralisation. This study examined intrinsic connectivity of four regions implicated in heteromodal semantic cognition, identified using large scale meta-analyses: two sites which have been argued to act as heteromodal semantic hubs in anterior temporal lobe (ATL) and angular gyrus (AG); and two sites implicated in semantic control in inferior frontal (IFG) and posterior middle temporal gyri (pMTG). We compared the intrinsic connectivity of these sites in left hemisphere (LH) and right hemisphere (RH), and linked individual differences in the strength of within- and between-hemisphere connectivity from left-lateralised seeds to performance on semantic tasks, in a sample of 196 healthy volunteers. ATL showed more symmetrical patterns of intrinsic connectivity than the other three sites. The connectivity between IFG and pMTG was stronger in the LH than the RH, suggesting that the semantic control network is strongly left-lateralised. The degree of hemispheric lateralisation also predicted behaviour: participants with stronger intrinsic connectivity within the LH had better semantic performance, while those with stronger intrinsic connectivity between left pMTG and homotopes of semantic regions in the RH performed more poorly on judgements of weak associations, which require greater control. Stronger connectivity between left AG and visual cortex was also linked to poorer perceptual performance. Overall, our results show that hemispheric lateralisation is particularly important for the semantic control network, and that this lateralisation has contrasting functional consequences for the retrieval of dominant and subordinate aspects of knowledge.