Resting-state Functional Connectivity Patterns Research Articles

Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses

Recent advances in cognitive neuroscience have focused on using resting-state functional connectivity (RSFC) data from fMRI scans to more accurately predict human traits and behaviors. Traditional approaches generally analyze RSFC by correlating averaged time-series data across regions of interest (ROIs) or networks, which may overlook important spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project and the Brain Genomics Superstruct Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. To enhance prediction accuracy, we developed an ensemble learner that combines these qualitatively different methods using a weighted average approach. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model, integrating these diverse weak learners, outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Through a comprehensive search in PubMed and Scopus databases, we reviewed 5258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps. No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis in the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3"transcranial magnetic stimulation therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Unearthing the hidden links: Investigating the functional connectivity between amygdala subregions and brain networks in bipolar disorder through resting-state fMRI

IntroductionBipolar disorder is a multifaceted psychiatric condition characterized by fluctuating activity levels and dysfunctional mood states, oscillating between manic and depressive episodes. These mood disturbances are accompanied by persistent functional and cognitive impairments, even during periods of euthymia. Prior studies have underscored the critical role of amygdala activity in the pathophysiology of bipolar disorder. This research aims to utilize resting-state functional Magnetic Resonance Imaging (rs-fMRI) to explore the functional modifications in the six sub-regions that compose the amygdala of individuals diagnosed with bipolar disorder. MethodThe study encompassed 80 participants, bifurcated into two groups: 40 individuals with bipolar disorder and 40 healthy controls. Each group comprised an equal gender distribution of 20 females and 20 males, ranging in age from 21 to 50 years. Using rs-fMRI, we examined the functional connectivity within six amygdala sub-regions across eight regional functional networks. ResultsComparative analysis between the control group and the bipolar patients revealed that all six amygdala sub-regions demonstrated connectivity with the eight functional brain networks. Notable similarities and disparities were observed in the connectivity patterns between the bipolar group and controls, particularly within the amygdala's sub-regions and other brain networks. The most significant functional connectivity alterations were found with the salience network and the default mode network. Additionally, alterations in the functional connectivity between the amygdala, sensory-motor, and visual networks were noted in bipolar patients. ConclusionThe study's findings highlight the distinct patterns of resting-state functional connectivity of the amygdala and various brain networks in differentiating bipolar patients from healthy controls. These variations suggest the existence of multiple pathophysiological mechanisms contributing to emotional dysregulation in bipolar disorder.

Shared and distinct patterns of default mode network dysfunction in major depressive disorder and bipolar disorder: A comparative meta-analysis

BackgroundWhile patients with major depressive disorder (MDD) and bipolar disorder (BD) exhibited default mode network (DMN) dysfunction revealed by aberrant resting-state functional connectivity (rsFC) patterns, previous findings have been inconsistent. Little is known about the similarities and differences in DMN rsFC between MDD and BD. MethodsA voxel-wise meta-analysis of seed-based DMN rsFC studies on MDD or BD was performed using the Seed-based d Mapping software with permutation of subject images (SDM-PSI). Aberrant DMN rsFC in both disorders was investigated separately, followed by conjunction and between-disorder comparison analyses. Functional decoding was performed to implicate the psychophysiological underpinnings of derived brain abnormalities. ResultsThirty-four studies comparing 1316 MDD patients with 1327 HC, and 22 studies comparing 1059 BD patients with 1396 HC were included. Compared to HC, MDD patients exhibited DMN hyperconnectivity with frontolimbic systems, and hypoconnectivity with temporal lobe and posterior cingulate cortex. BD patients displayed increased DMN connectivity with bilateral precuneus, and reduced connectivity with prefrontal cortex and middle temporal gyrus. No common patterns of DMN rsFC abnormalities were observed between MDD and BD. Compared to BD, MDD patients showed DMN hyperconnectivity with triangular part of the left inferior frontal gyrus and left fusiform gyrus. Functional decoding found that patterns of DMN rsFC alteration between MDD and BD were primarily related to action and perception domains. ConclusionDistinct DMN dysfunction patterns in MDD and BD enhance current understanding of the neural substrates of mood disorders and may provide a potential biomarker for differentiation.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Through a comprehensive search in PubMed and Scopus databases, we reviewed 5,258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of consistent antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation (TMS) therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps. No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis across the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3" TMS therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Unveiling the core functional networks of cognition: An ontology-guided machine learning approach

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

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.

Abnormal functional connectivity of the putamen in obsessive-compulsive disorder

The putamen has been proposed to play a critical role in the development of obsessive-compulsive disorder (OCD). The primary objective of this study was to examine the resting-state regional activity and functional connectivity patterns of the putamen in individuals diagnosed with OCD. To achieve this, we employed resting-state functional magnetic resonance imaging (rs-fMRI) to collect data from a sample of 45 OCD patients and 53 healthy control participants. We aimed to use the regional amplitude of low-frequency fluctuation (ALFF) analysis to generate the ROI masks of the putamen and then conduct the whole brain functional connectivity of the putamen in individuals with OCD. Compared to controls, the OCD group demonstrated decreased ALFF in bilateral putamen. The right putamen also displayed decreased FC with the left putamen extending to the inferior frontal gyrus (IFG), bilateral precuneus extending to calcarine, the right middle occipital cortex extending to the right middle temporal cortex, and the left middle occipital gyrus. The decreased connectivity between the right putamen and the left IFG was negatively correlated with Yale-Brown Obsessive Compulsive scale (Y-BOCS) Obsession Scores. This study aimed to reveal the putamen changes in resting-state activity and connectivity in OCD patients, highlighting the significance of aberrant ALFF/FC of the putamen is a key characteristic of OCD.

Triple network resting-state functional connectivity patterns of alcohol heavy drinking.

Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain's salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse. Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures. Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms-right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r =.25, p = .03). These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

Phenomena of hypo- and hyperconnectivity in basal ganglia-thalamo-cortical circuits linked to major depression: a 7T fMRI study.

Major depressive disorder (MDD) typically manifests itself in depressed affect, anhedonia, low energy, and additional symptoms. Despite its high global prevalence, its pathophysiology still gives rise to questions. Current research places alterations in functional connectivity among MDD's most promising biomarkers. However, given the heterogeneity of previous findings, the use of higher-resolution imaging techniques, like ultra-high field (UHF) fMRI (≥7 Tesla, 7T), may offer greater specificity in delineating fundamental impairments. In this study, 7T UHF fMRI scans were conducted on 31 MDD patients and 27 age-gender matched healthy controls to exploratorily contrast cerebral resting-state functional connectivity patterns between both groups. The CONN toolbox was used to generate functional network connectivity (FNC) analysis based on the region of interest (ROI)-to-ROI correlations in order to enable the identification of clusters of significantly different connections. Correction for multiple comparisons was implemented at the cluster level using a false discovery rate (FDR). The analysis revealed three significant clusters differentiating MDD patients and healthy controls. In Clusters 1 and 2, MDD patients exhibited between-network hypoconnectivity in basal ganglia-cortical pathways as well as hyperconnectivity in thalamo-cortical pathways, including several individual ROI-to-ROI connections. In Cluster 3, they showed increased occipital interhemispheric within-network connectivity. These findings suggest that alterations in basal ganglia-thalamo-cortical circuits play a substantial role in the pathophysiology of MDD. Furthermore, they indicate potential MDD-related deficits relating to a combination of perception (vision, audition, and somatosensation) as well as more complex functions, especially social-emotional processing, modulation, and regulation. It is anticipated that these findings might further inform more accurate clinical procedures for addressing MDD.

Neural correlates of approach–avoidance behavior in healthy subjects: Effects of low-frequency repetitive transcranial magnetic stimulation (rTMS) over the right dorsolateral prefrontal cortex

The dorsolateral prefrontal cortex (dlPFC) is implicated in top-down regulation of emotion, but the detailed network mechanisms require further elucidation. To investigate network-level functions of the dlPFC in emotion regulation, this study measured changes in task-based activation, resting-state and task-based functional connectivity (FC) patterns following suppression of dlPFC excitability by 1-Hz repetitive transcranial magnetic stimulation (rTMS). In a sham-controlled within-subject design, 1-Hz active or sham rTMS was applied to the right dlPFC of 19 healthy volunteers during two separate counterbalanced sessions. Following active and sham rTMS, functional magnetic resonance imaging (fMRI) was conducted in the resting state (rs-fMRI) and during approach–avoidance task responses to pictures with positive and negative emotional content (task-based fMRI). Activation and generalized psychophysiological interaction analyses were performed on task-based fMRI, and seed-based FC analysis was applied to rs-fMRI data. Task-based fMRI revealed greater and more lateralized activation in the right hemisphere during negative picture responses compared to positive picture responses. After active rTMS, greater activation was observed in the left middle prefrontal cortex compared to sham rTMS. Further, rTMS reduced response times and error rates in approach to positive pictures compared to negative pictures. Significant FC changes due to rTMS were observed predominantly in the frontoparietal network (FPN) and visual network (VN) during the task, and in the default mode network (DMN) and VN at rest. Suppression of right dlPFC activity by 1-Hz rTMS alters large-scale neural networks and modulates emotion, supporting potential applications for the treatment of mood disorders.

Abnormal hypothalamic functional connectivity associated with cognitive impairment in craniopharyngiomas

ObjectiveThis study sought to characterize resting-state functional connectivity (rsFC) patterns of the hypothalamic and extrahypothalamic nuclei in craniopharyngioma (CP) patients, and to investigate potential correlations between hypothalamic and extrahypothalamic rsFC maps and neurocognitive performance. MethodsNinety-two CP patients and 40 demographically-matched healthy controls were included. Whole-brain seed-to-voxel analyses were used to test for between-group rsFC differences, and regression analyses were used to correlate neurocognitive performance with voxel-wise hypothalamic and extrahypothalamic rsFC maps for CP patients. Finally, spectral DCM analysis was used to explore the hypothalamus circuit associated with neurocognitive performance. ResultsThe seed-to-voxel analyses demonstrated that the hypothalamic nuclei showed mainly significant rsFC reduction in brain areas overlayed with the cortical regions of default mode network (DMN), notably in the bilateral anterior cingulate cortices and posterior cingulate cortices. The extrahypothalamic nuclei showed significant rsFC reduction in the limbic system of bilateral caudate nuclei, corpus callosum, fornix, and thalamus. Regression analyses revealed that worse cognitive performance was correlated with abnormal hypothalamic rsFC with brain areas in DMN, and DCM analysis revealed a hypothalamus-DMN circuit responsible for functional modulation of cognitive impairment in CP patients. ConclusionsOur study demonstrated that CPs invading into hypothalamus impacted hypothalamic and extrahypothalamic rsFC with brain areas of DMN and limbic system, the severity of which was parallel with the grading system of hypothalamus involvement. In addition to the CP-induced structural damage to the hypothalamus alone, abnormal functional connectivity within the hypothalamus-DMN circuit might be a functional mechanism leading to the cognitive impairment in CP patients.

Examining putamen resting-state connectivity markers of suicide attempt history in depressed adolescents.

Suicide is a current leading cause of death in adolescents and young adults. The neurobiological underpinnings of suicide risk in youth, however, remain unclear and a brain-based model is lacking. In adult samples, current models highlight deficient serotonin release as a potential suicide biomarker, and in particular, involvement of serotonergic dysfunction in relation to the putamen and suicidal behavior. Less is known about associations among striatal regions and relative suicidal risk across development. The current study examined putamen connectivity in depressed adolescents with (AT) and without history of a suicide attempt (NAT), specifically using resting-state functional magnetic resonance imaging (fMRI) to evaluate patterns in resting-state functional connectivity (RSFC). We hypothesized the AT group would exhibit lower striatal RSFC compared to the NAT group, and lower striatal RSFC would associate with greater suicidal ideation severity and/or lethality of attempt. We examined whole-brain RSFC of six putamen regions in 17 adolescents with depression and NAT (MAge [SD] = 16.4[0.3], 41% male) and 13 with AT (MAge [SD] = 16.2[0.3], 31% male). Only the dorsal rostral striatum showed a statistically significant bilateral between-group difference in RSFC with the superior frontal gyrus and supplementary motor area, with higher RSFC in the group without a suicide attempt compared to those with attempt history (voxel-wise p<.001, cluster-wise p<.01). No significant associations were found between any putamen RSFC patterns and suicidal ideation severity or lethality of attempts among those who had attempted. The results align with recent adult literature and have interesting theoretical and clinical implications. A possible interpretation of the results is a mismatch of the serotonin transport to putamen and to the supplementary motor area and the resulting reduced functional connectivity between the two areas in adolescents with attempt history. The obtained results can be used to enhance the diathesis-stress model and the Emotional paiN and social Disconnect (END) model of adolescent suicidality by adding the putamen. We also speculate that connectivity between putamen and the supplementary motor area may in the future be used as a valuable biomarker of treatment efficacy and possibly prediction of treatment outcome.

Resting-State Network Functional Connectivity Analysis: A Multimodal Imaging and Computational Modeling Approach

Functional connectivity is referred to as the temporal interaction between different brain regions that are spatially distant from each other. It is commonly examined during resting states by classifying resting-state functional connectivity (rs-FC) patterns and their associated synchronized activity to understand the underlying neural mechanisms that support cognitive processes. With the advancement of cutting-edge neuronal systems, multimodal imaging, and computational advances have served as promising tools for rs-FC studies. Despite having reviewed resting-state networks and their influence on a range of health anomalies in the existing literature, there nevertheless remains a lack of reviews that delve into diverse imaging modalities and computational modeling approaches for rs-FC analysis. Therefore, this systematic review aims to provide an understanding of these technologies that analyze the resting-state network functional connectivity and their correlation to cognitive functions. Through a systematic approach to analyzing studies, the most frequently employed modality was functional magnetic resonance imaging (fMRI) due to its high spatial resolution. Key findings demonstrated that higher functional connectivity levels are often linked to greater Exin ratios, which subsequently affect large-scale functional brain synchronization. Across various computational models, a non-parametric classifier has shown promise for rs-FC analysis in various contexts due to its ability to handle multiple EEG features. Moreover, the multivariate model is identified to have superior performance with a predictive area under the curve (AUC) of ~0.77. Overall, the present study underscores the vitality of using imaging and computational tools in elucidating the intricacies of rs-FC and its effects on individual cognition.

Aberrant Functional Connectivity of the Salience Network in Adult Patients with Tic Disorders: A Resting-State fMRI Study.

Tic disorders (TD) are characterized by the presence of motor and/or vocal tics. Common neurophysiological frameworks suggest dysregulations of the cortico-striatal-thalamo-cortical (CSTC) brain circuit that controls movement execution. Besides common tics, there are other "non-tic" symptoms that are primarily related to sensory perception, sensorimotor integration, attention, and social cognition. The existence of these symptoms, the sensory tic triggers, and the modifying effect of attention and cognitive control mechanisms on tics may indicate the salience network's (SN) involvement in the neurophysiology of TD. Resting-state functional MRI measurements were performed in 26 participants with TD and 25 healthy controls (HC). The group differences in resting-state functional connectivity patterns were measured based on seed-to-voxel connectivity analyses. Compared to HC, patients with TD exhibited altered connectivity between the core regions of the SN (insula, anterior cingulate cortex, and temporoparietal junction) and sensory, associative, and motor-related cortices. Furthermore, connectivity changes were observed in relation to the severity of tics in the TD group. The SN, particularly the insula, is likely to be an important site of dysregulation in TD. Our results provide evidence for large-scale neural deviations in TD beyond the CSTC pathologies. These findings may be relevant for developing treatment targets.

Short communication: Lifetime musical activity and resting-state functional connectivity in cognitive networks.

Participation in multimodal leisure activities, such as playing a musical instrument, may be protective against brain aging and dementia in older adults (OA). Potential neuroprotective correlates underlying musical activity remain unclear. This cross-sectional study investigated the association between lifetime musical activity and resting-state functional connectivity (RSFC) in three higher-order brain networks: the Default Mode, Fronto-Parietal, and Salience networks. We assessed 130 cognitively unimpaired participants (≥ 60 years) from the baseline cohort of the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Lifetime musical activity was operationalized by the self-reported participation in musical instrument playing across early, middle, and late life stages using the Lifetime of Experiences Questionnaire (LEQ). Participants who reported musical activity during all life stages (n = 65) were compared to controls who were matched on demographic and reserve characteristics (including education, intelligence, socioeconomic status, self-reported physical activity, age, and sex) and never played a musical instrument (n = 65) in local (seed-to-voxel) and global (within-network and between-network) RSFC patterns using pre-specified network seeds. Older participants with lifetime musical activity showed significantly higher local RSFC between the medial prefrontal cortex (Default Mode Network seed) and temporal as well as frontal regions, namely the right temporal pole and the right precentral gyrus extending into the superior frontal gyrus, compared to matched controls. There were no significant group differences in global RSFC within or between the three networks. We show that playing a musical instrument during life relates to higher RSFC of the medial prefrontal cortex with distant brain regions involved in higher-order cognitive and motor processes. Preserved or enhanced functional connectivity could potentially contribute to better brain health and resilience in OA with a history in musical activity. German Clinical Trials Register (DRKS00007966, 04/05/2015).

TEMPORARY REMOVAL: Pro-inflammatory markers are related to cortical network connectivity in women exposed to interpersonal trauma with PTSD

Exposure to interpersonal violence affects a significant number of individuals each year and further increases the risk for developing Posttraumatic Stress Disorder (PTSD). A growing body of research suggests that immune system dysfunction, in particular elevated inflammation, may contribute to the pathophysiology of PTSD. However, few studies have examined the neurobiological correlates of inflammation in women with PTSD using resting-state fMRI. The present study explored the relationship between pro-inflammatory cytokine levels, C-reactive protein (CRP), tumor necrosis factor alpha TNF-alpha), and interleukin-6 (IL-6), and resting-state functional connectivity patterns in three major cortical networks (default mode network (DMN), central executive network (CEN), and salience network (SN)) in a sample of women (N=18) exposed to interpersonal violence with PTSD. Results indicated that higher CRP levels were associated with stronger functional connectivity between the SN and visual areas, but weaker functional connectivity between the CEN and visual areas. These findings suggest that pro-inflammatory markers are related to connectivity of task-positive networks in women with PTSD. Further, our results provide evidence for potential neurobiological markers of inflammation in PTSD.

Testing the sensitivity of diagnosis-derived patterns in functional brain networks to symptom burden in a Norwegian youth sample.

Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms.

Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy

Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health. Functional near-infrared spectroscopy (fNIRS) can assess resting-state functional connectivity (RSFC) at the bedside. We acquired resting-state fNIRS data from 21 neonates with HIE (postmenstrual age [PMA] = 39.96), in 19 neonates the scans were acquired post-therapeutic hypothermia (TH), and from 20 term-born healthy newborns (PMA = 39.93). Twelve HIE neonates also underwent resting-state functional magnetic resonance imaging (fMRI) post-TH. RSFC was calculated as correlation coefficients amongst the time courses for fNIRS and fMRI data, respectively. The fNIRS and fMRI RSFC maps were comparable. RSFC patterns were then measured with graph theory metrics and compared between HIE infants and healthy controls. HIE newborns showed significantly increased clustering coefficients, network efficiency and modularity compared to controls. Using a support vector machine algorithm, RSFC features demonstrated good performance in classifying the HIE and healthy newborns in separate groups. Our results indicate the utility of fNIRS-connectivity patterns as potential biomarkers for HIE and fNIRS as a new bedside tool for newborns with HIE.

Altered functional connectivity of insular subregions in subjective cognitive decline.

Recent research has highlighted the insula as a critical hub in human brain networks and the most susceptible region to subjective cognitive decline (SCD). However, the changes in functional connectivity of insular subregions in SCD patients remain poorly understood. The present study aims to clarify the altered functional connectivity patterns within insular subregions in individuals with SCD using resting-state functional magnetic resonance imaging (rs-fMRI). In this study, we collected rs-fMRI data from 30 patients with SCD and 28 healthy controls (HCs). By defining three subregions of the insula, we mapped whole-brain resting-state functional connectivity (RSFC). We identified several distinct RSFC patterns of the insular subregions. Specifically, for positive connectivity, three cognitive-related RSFC patterns were identified within the insula, suggesting anterior-to-posterior functional subdivisions: (1) a dorsal anterior zone of the insula that shows RSFC with the executive control network (ECN); (2) a ventral anterior zone of the insula that shows functional connectivity with the salience network (SN); and (3) a posterior zone along the insula that shows functional connectivity with the sensorimotor network (SMN). Compared to the controls, patients with SCD exhibited increased positive RSFC to the sub-region of the insula, demonstrating compensatory plasticity. Furthermore, these abnormal insular subregion RSFCs are closely correlated with cognitive performance in the SCD patients. Our findings suggest that different insular subregions exhibit distinct patterns of RSFC with various functional networks, which are affected differently in patients with SCD.