Connectivity Of Large-scale Brain Networks Research Articles

Multivariate classification of earthquake survivors with post-traumatic stress disorder based on large-scale brain networks.

The identification of post-traumatic stress disorder (PTSD) among natural disaster survivors is remarkably challenging, and there are no reliable objective signatures that can be used to assist clinical diagnosis and optimize treatment. The current study aimed to establish a neurobiological signature of PTSD from the connectivity of large-scale brain networks and clarify the brain network mechanisms of PTSD. We examined fifty-seven unmedicated survivors with chronic PTSD and 59 matched trauma-exposed healthy controls (TEHCs) using resting-state functional magnetic resonance imaging (rs-fMRI). We extracted the node-to-network connectivity and obtained a feature vector with a dimensionality of 864 (108 nodes×8 networks) to represent each subject's functional connectivity (FC) profile. Multivariate pattern analysis with a relevance vector machine was then used to distinguish PTSD patients from TEHCs. We achieved a promising diagnostic accuracy of 89.2% in distinguishing PTSD patients from TEHCs. The most heavily weighted connections for PTSD classification were among the default mode network (DMN), visual network (VIS), somatomotor network, limbic network, and dorsal attention network (DAN). The strength of the anticorrelation of FC between the ventral medial prefrontal cortex (vMPFC) in DMN and the VIS and DAN was associated with the severity of PTSD. This study achieved relatively high accuracy in classifying PTSD patients vs. TEHCs at the individual level. This performance demonstrates that rs-fMRI-derived multivariate classification based on large-scale brain networks can provide potential signatures both to facilitate clinical diagnosis and to clarify the underlying brain network mechanisms of PTSD caused by natural disasters.

Changes in inter-network functional connectivity of large-scale brain networks in patients with chronic migraine

Migraine is a neurovascular disease characterized by headache and accompanied by mood disorders and impaired attention. Functional magnetic resonance imaging (fMRI) at rest allows to identify changes in the functional connectivity (FC) of various neural networks of the brain associated with the pathogenesis of pain in migraine. Previous studies of patients with migraine describe a change in FC only in certain brain structures or within some of its neural networks. However violation of the interconnected functioning of neural networks is of predominant importance within the framework of a developing theory of the common nature of the neuro- and psychopathological process. The aim of the study is to consider pathogenetic mechanism of migraine and concomitant psychopathology in the aspect of changes in the inter-network functional connectivity. Methods and patients. FMRI scanning of 25 healthy people as a control group and of 25 patients at rest between migraine attacks was performed. Post-processing and secondary analysis were performed using the CONN functional connectivity toolbox version 18b software based on an unpaired t-test and regression analysis, taking into account the Benjamini-Hochberg correction. A set of questionnaires and tests was used to clarify the qualitative characteristics of pain and the accompanying psychopathology in the patients. Results. An increase in FC in patients was observed in comparison with the control group between the salience and sensorimotor networks, the salience and default mode networks, and also within default mode networks; a decrease in FC was observed between the salience network and the dorsal attention network and within dorsal attention network. Conclusion. The obtained results indicate a common nature of pain and affective disorders in patients with migraine and identify a possible mechanism of developing chronicity. The data of the study can be used to objectify the patient’s condition in the process of clinical diagnosis.

Transdiagnostic modulation of brain networks by electroconvulsive therapy in schizophrenia and major depression

Major depressive disorder (MDD) and schizophrenia (SCZ) share neurobiological and clinical commonalities. Altered functional connectivity of large-scale brain networks has been associated with both disorders. Electroconvulsive therapy (ECT) has proven to be an effective treatment in severe forms of MDD and SCZ. However, the role of ECT on the modulation of the dynamics of brain networks is still unknown. In this study, we used resting state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity in 16 pharmacoresistant patients with SCZ or MDD and a matched group of normal controls. Patients were scanned before and after right-sided unilateral ECT. Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate the effects of ECT treatment on intrinsic components (INs). Functional network connectivity (FNC) was calculated between pairs of INs. Patients had reduced connectivity within a striato-thalamic network in the thalamus as well as increased low frequency oscillations in a striatal network. ECT reduced low frequency oscillations (LFOs) on a striatal network along with increasing functional connectivity in the medial prefrontal cortex within the DMN. Following ECT treatment, the FNC of the executive network was reduced with the DMN and increased with the salience network, respectively.Our findings suggest transnosological effects of ECT on the connectivity of large-scale networks as well as at the level of their interplay. Furthermore, they support a transnosological approach for the investigation not only of the neural correlates of the disease but also of the brain mechanism of treatment of mental disorders.

Directed Connectivity in Large-scale Brain Networks for Precision Grip Force Control.

Precision grip requires accurate but dynamic control of the magnitude and direction of fingertip forces. It is still not well known whether directed information flow across activated cortical regions could change with different force outputs during precision grip. This study aimed to investigate the directed connectivity in large-scale brain networks for precision grip force control. Eight healthy volunteers participated in the experiment. Totally 32 channel electroencephalography (EEG) signals were recorded during a precision grip task that requires accurate force control following a dynamically changed visual target. The force target changed between 10% and 1% Maximal Voluntary Contraction (MVC). Horizontal visibility graph transfer entropy (HVG-TE) was used to measure the directed connectivity between pairs of EEG channels. In addition, the relative HVG-TE (rHVG-TE) was applied to evaluate the activation of each EEG channel. Results showed relatively higher rHVG-TE values in the posterior brain region but lower rHVG-TE values in the anterior brain region within the whole spectrum (4-70Hz), indicating a posterior-to-anterior information flow. This study revealed that the activation of posterior region is higher than the anterior region. There is a directed functional connectivity of cortex from posterior to anterior region for precision grip force control. This study shed light on how to quantify activation and large-scale connectivity of cortical regions, and reveal the in-depth central mechanisms for peripheral fine motor control.

Resting-State Functional Connectivity Is Associated With Cerebrospinal Fluid Levels of the Synaptic Protein NPTX2 in Non-demented Older Adults.

Intrinsic functional connectivity of large-scale brain networks has been shown to change with aging and Alzheimer’s disease (AD). These alterations are thought to reflect changes in synaptic function, but the underlying biological mechanisms are poorly understood. This study examined whether Neuronal Pentraxin 2 (NPTX2), a synaptic protein that mediates homeostatic strengthening of inhibitory circuits to control cortical excitability, is associated with functional connectivity as measured by resting-state functional magnetic resonance imaging (rsfMRI) in five large-scale cognitive brain networks. In this cross-sectional study, rsfMRI scans were obtained from 130 older individuals (mean age = 69 years) with normal cognition (N = 113) and Mild Cognitive Impairment (N = 17); NPTX2 was measured in the same individuals in cerebrospinal fluid (CSF). Higher levels of NPTX2 in CSF were associated with greater functional connectivity in the salience/ventral attention network, based on linear regression analysis. Moreover, this association was stronger among individuals with lower levels of cognitive reserve, as measured by a composite score (comprised of years of education, reading, and vocabulary measures). Additionally, higher connectivity in the salience/ventral attention network was related to better performance on a composite measure of executive function. Levels of NPTX2 were not associated with connectivity in other networks (executive control, limbic, dorsal attention, and default-mode). Findings also confirmed prior reports that individuals with MCI have lower levels of NPTX2 compared to those with normal cognition. Taken together, the results suggest that NPTX2 mechanisms may play a central role among older individuals in connectivity within the salience/ventral attention network and for cognitive tasks that require modulation of attention and response selection.

Abnormal functional network centrality in drug-naïve boys with attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder in childhood and is characterized by inattention, impulsivity, and hyperactivity. Observations of distributed functional abnormalities in ADHD suggest aberrant large-scale brain network connectivity. However, few studies have measured the voxel-wise network centrality of boys with ADHD, which captures the functional relationships of a given voxel within the entire connectivity matrix of the brain. Here, to examine the network patterns characterizing children with ADHD, we recruited 47 boys with ADHD and 21 matched control boys who underwent resting-state functional imaging scanning in a 3.0 T MRI unit. We measured voxel-wise network centrality, indexing local functional relationships across the entire brain connectome, termed degree centrality (DC). Then, we chose the brain regions with altered DC as seeds to examine the remote functional connectivity (FC) of brain regions. We found that boys with ADHD exhibited (1) decreased centrality in the left superior temporal gyrus (STG) and increased centrality in the left superior occipital lobe (SOL) and right inferior parietal lobe(IPL); (2) decreased FC between the STG and the putamen and thalamus, which belong to the cognitive cortico-striatal-thalamic-cortical (CSTC) loop, and increased FC between the STG and medial/superior frontal gyrus within the affective CSTC loop; and (3) decreased connectivity between the SOL and cuneus within the dorsal attention network. Our results demonstrated that patients with ADHD show a connectivity-based pathophysiological process in the cognitive and affective CSTC loops and attention network.

Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks

Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric.To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.

Large-scale brain functional network topology disruptions underlie symptom heterogeneity in children with attention-deficit/hyperactivity disorder.

Accumulating evidence suggests brain network dysfunction in attention-deficit/hyperactivity disorder (ADHD). Whether large-scale brain network connectivity patterns reflect clinical heterogeneity in ADHD remains to be fully understood. This study aimed to characterize the differential within- and between-network functional connectivity (FC) changes in children with ADHD combined (ADHD-C) or inattentive (ADHD-I) subtypes and their associations with ADHD symptoms. We studied the task-free functional magnetic resonance imaging (fMRI) data of 58 boys with ADHD and 28 demographically matched healthy controls. We measured within- and between-network connectivity of both low-level (sensorimotor) and high-level (cognitive) large-scale intrinsic connectivity networks and network modularity. We found that children with ADHD-C but not those with ADHD-I exhibited hyper-connectivity within the anterior default mode network (DMN) compared with controls. Additionally, children with ADHD-C had higher inter-network FC between the left executive control (ECN) and the salience (SN) networks, between subcortical and visual networks, and between the DMN and left auditory networks than controls, while children with ADHD-I did not show differences compared with controls. Similarly, children with ADHD-C but not ADHD-I showed lower network modularity compared with controls. Importantly, these observed abnormal inter-network connectivity and network modularity metrics were associated with Child Behavioral Checklist (CBCL) attention-deficit/hyperactivity problems and internalizing problems in children with ADHD. This study revealed relatively greater loss of brain functional network segregation in childhood ADHD combined subtype compared to the inattentive subtype, suggesting differential large-scale functional brain network topology phenotype underlying childhood ADHD heterogeneity.

Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation.

Despite its widespread use in cognitive studies, there is still limited understanding of whether and how transcranial direct current stimulation (tDCS) modulates brain network function. To clarify its physiological effects, we assessed brain network function using functional magnetic resonance imaging (fMRI) simultaneously acquired during tDCS stimulation. Cognitive state was manipulated by having subjects perform a Choice Reaction Task or being at “rest.” A novel factorial design was used to assess the effects of brain state and polarity. Anodal and cathodal tDCS were applied to the right inferior frontal gyrus (rIFG), a region involved in controlling activity large‐scale intrinsic connectivity networks during switches of cognitive state. tDCS produced widespread modulation of brain activity in a polarity and brain state dependent manner. In the absence of task, the main effect of tDCS was to accentuate default mode network (DMN) activation and salience network (SN) deactivation. In contrast, during task performance, tDCS increased SN activation. In the absence of task, the main effect of anodal tDCS was more pronounced, whereas cathodal tDCS had a greater effect during task performance. Cathodal tDCS also accentuated the within‐DMN connectivity associated with task performance. There were minimal main effects of stimulation on network connectivity. These results demonstrate that rIFG tDCS can modulate the activity and functional connectivity of large‐scale brain networks involved in cognitive function, in a brain state and polarity dependent manner. This study provides an important insight into mechanisms by which tDCS may modulate cognitive function, and also has implications for the design of future stimulation studies.

Quality of maternal behaviour during infancy predicts functional connectivity between default mode network and salience network 9 years later

Infants’ experiences are considered to determine to a large degree the strength and effectiveness of neural connections and fine tune the development of brain networks. As one of the most pervasive and potent relational experiences of infancy, parent-child relationships appear to be prime candidates to account for experience-driven differences in children’s brain development. Yet, studies linking parenting and functional connectivity are surprisingly scarce, and restricted to the connectivity of limbic structures. Accordingly, this longitudinal study explored whether normative variation in the quality of early maternal behaviour predicts the functional connectivity of large-scale brain networks in late childhood. Maternal mind-mindedness and autonomy support were assessed with 28 children when they were 13 and 15 months old respectively. When children were 10 years of age, children underwent a resting-state functional MRI exam. Functional connectivity was assessed between key regions of the default mode network (DMN), salience network (SN), and frontal-parietal central executive network (CEN). Results revealed that higher mind-mindedness and autonomy support predicted stronger negative connectivity between DMN and SN regions. These findings are the first to provide preliminary evidence suggestive of a long-lasting impact of variation within the normative range of early maternal behaviour on functional connectivity between large-scale brain networks.

Large-scale brain network connectivity underlying creativity in resting-state and task fMRI: Cooperation between default network and frontal-parietal network

Creativity is imperative to social development and the promotion of well-being. Here, independent component analysis and functional network connectivity analysis methods were applied to both resting-state and divergent thinking task fMRI data from the same sample to investigate large-scale brain network connectivity underlying creativity. The results showed that the strength of the connectivity between the posterior default mode network (DMN) and right frontal-parietal network (FPN) was significantly greater whereas the right FPN and left FPN connectivity strength was weaker in the creative condition than in the control condition. In addition, the posterior DMN and right FPN connectivity strength in the divergent thinking task was positively correlated with the connectivity strength between anterior DMN and left FPN during the resting-state. Moreover, the anterior DMN and left FPN connectivity strength during the resting-state was posit ively correlated with the originality score derived from divergent thinking task. Taken together, these findings extend previous research by revealing the cooperation between the DMN and FPN involved in creative thinking during both resting-state and task fMRI and provide insights into the prediction of creative thinking performance through brain network connectivity during resting-state and task fMRI in the future.

12-Distinct between-network functional connectivity changes in Parkinson’s disease related to cognition: A follow-up study

Introduction Patients with Parkinson’s disease (PD) show abnormal functional connectivity in large-scale brain networks. We explored resting state connectivity among the main networks, including the default mode network (DMN), dorsal attentional network (DAN), frontoparietal control network (FPCN) and visual network (VN) in PD patients with and without cognitive impairment (PD-MCI and PD-NC). Methods 51 healthy controls (HC), 17 PD-NC and 24 PD-MCI were enrolled. Participants underwent cognitive testing and MRI examination at the baseline and at one year follow-up. Resting-state fMRI data were analysed and the inter-network connectivity was calculated between the FPCN and other networks. Spearman correlations with attention and executive domain z-scores were calculated. Results FPCN-DAN, FPCN-VN and FPCN-DMN connectivities were significantly reduced in PD-NC as compared to HC. The connectivities decreased in the order HC > PD-MCI > PD-NC. At one-year follow-up, significant increases in FPCN-VN and FPCN-DMN connectivities were observed in the merged PD group, while no changes were detectable in the HC group. Conclusion Decreased connectivity between the FPCN and other networks was observed already in PD-NC. PD-MCI tend to compensate for their cognitive deficit by increasing between-network connectivities. Moreover, the between-network connectivities were increased at the follow-up in the PD-all group, while no changes were found in HC. Acknowledgement: Supported by the EU Joint Programming initiative within Neurodegenerative Diseases, funded by the Norwegian Strategic Research Council (APGeM—Preclinical genotype-phenotype predictors of Alzheimer’s disease and other dementias, grant agreement number 3056-00001). We acknowledge also the core facility MAFIL of CEITEC supported by the MEYS CR (LM2015062 Czech-BioImaging).

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

Functional connectivity of large-scale brain networks in patients with anti-NMDA receptor encephalitis: an observational study

In anti-NMDA receptor (NMDAR) encephalitis, antibody-mediated dysfunction of NMDARs causes severe neuropsychiatric symptoms, including psychosis, memory deficits, and movement disorders. However, it remains elusive how antibody-mediated NMDAR dysfunction leads to these symptoms, and whether the symptoms arise from impairment in specific brain regions and the interactions between impaired regions. In this observational study, we recruited 43 patients with anti-NMDAR encephalitis from a tertiary university hospital and 43 age-matched and sex-matched healthy controls without a history of neurological or psychiatric disorders, who were recruited from the general population of Berlin. We used structural and resting-state functional MRI to investigate alterations in connectivity in all participants. We did functional connectivity analyses, including large-scale network analysis, whole-brain pair-wise connectivity, and machine-learning classification, and compared the results with patients' functional impairment. Although structural MRI was normal in 31 (72%) of the 43 patients, we observed widespread alterations of functional connectivity that correlated with clinical measures. These alterations included impaired hippocampal functional connectivity, decoupling of the medial temporal and the default-mode networks, and an overall impairment of frontotemporal connections. Furthermore, functional connectivity was impaired within distributed large-scale networks, including sensorimotor, frontoparietal, lateral-temporal, and visual networks. Memory impairment correlated with hippocampal and medial-temporal-lobe network connectivity, whereas schizophrenia-like symptoms were associated with functional connectivity changes in frontoparietal networks. Machine-learning analyses corroborated these findings and identified frontoparietal and frontotemporal connections as reliably discriminating features between patients and controls, yielding an overall accuracy of 81%. This study reveals a characteristic pattern of whole-brain functional connectivity alterations in anti-NMDAR encephalitis that is well suited to explain the major clinical symptoms of the disorder. These observations advance the pathophysiological understanding of NMDAR dysfunction in the human brain and could be similarly relevant for other neuropsychiatric disorders, such as schizophrenia. Deutsche Forschungsgemeinschaft, Israeli National Science Foundation, Ministry of Science and Technology of Israel, Orion Foundation, and the Agnes Ginges Center for Neurologenetics.

Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networks

We propose a new measure, horizontal visibility graph transfer entropy (HVG-TE), to estimate the direction of information flow between pairs of time series. HVG-TE quantifies the transfer entropy between the degree sequences of horizontal visibility graphs derived from original time series. Twenty-one Rössler attractors unidirectionally coupled in the posterior-to-anterior direction were used to simulate 21-channel Electroencephalography (EEG) brain networks and validate the performance of the HVG-TE. We showed that the HVG-TE is robust to different levels of coupling strengths between the coupled Rössler attractors, a wide range of time delays, different sample sizes, the effects of noise and linear mixing, and the choice of reference for EEG data. We also applied HVG-TE to EEG data in 20 healthy controls and compared its performance to a recently introduces phase-based TE measure (PTE). We found that compared with PTE, HVG-TE consistently detected stronger posterior-to-anterior information flow patterns in the alpha-band (8–13Hz) EEG brain networks for three different references. Moreover, in contrast to PTE, HVG-TE does not require an assumption on the periodicity of input signals, therefore it can be more widely applicable, even for non-periodic signals. This study shows that the HVG-TE is a directed connectivity measure to characterise the direction of information flow in large-scale brain networks.

Brief Mental Training Reorganizes Large-Scale Brain Networks.

Emerging evidences have shown that one form of mental training—mindfulness meditation, can improve attention, emotion regulation and cognitive performance through changing brain activity and structural connectivity. However, whether and how the short-term mindfulness meditation alters large-scale brain networks are not well understood. Here, we applied a novel data-driven technique, the multivariate pattern analysis (MVPA) to resting-state fMRI (rsfMRI) data to identify changes in brain activity patterns and assess the neural mechanisms induced by a brief mindfulness training—integrative body–mind training (IBMT), which was previously reported in our series of randomized studies. Whole brain rsfMRI was performed on an undergraduate group who received 2 weeks of IBMT with 30 min per session (5 h training in total). Classifiers were trained on measures of functional connectivity in this fMRI data, and they were able to reliably differentiate (with 72% accuracy) patterns of connectivity from before vs. after the IBMT training. After training, an increase in positive functional connections (60 connections) were detected, primarily involving bilateral superior/middle occipital gyrus, bilateral frontale operculum, bilateral superior temporal gyrus, right superior temporal pole, bilateral insula, caudate and cerebellum. These results suggest that brief mental training alters the functional connectivity of large-scale brain networks at rest that may involve a portion of the neural circuitry supporting attention, cognitive and affective processing, awareness and sensory integration and reward processing.

Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks

Anesthesia-induced changes in functional connectivity and cerebral blow flow (CBF) in large-scale brain networks have emerged as key markers of reduced consciousness. However, studies of functional connectivity disagree on which large-scale networks are altered or preserved during anesthesia, making it difficult to find a consensus amount studies. Additionally, pharmacological alterations in CBF could amplify or occlude changes in connectivity due to the shared variance between CBF and connectivity. Here, we used data-driven connectivity methods and multi-modal imaging to investigate shared and unique neural correlates of reduced consciousness for connectivity in large-scale brain networks. Rs-fMRI and CBF data were collected from the same subjects during an awake and deep sedation condition induced by propofol. We measured whole-brain connectivity using the intrinsic connectivity distribution (ICD), a method not reliant on pre-defined seed regions, networks of interest, or connectivity thresholds. The shared and unique variance between connectivity and CBF were investigated. Finally, to account for shared variance, we present a novel extension to ICD that incorporates cerebral blood flow (CBF) as a scaling factor in the calculation of global connectivity, labeled CBF-adjusted ICD). We observed altered connectivity in multiple large-scale brain networks including the default mode (DMN), salience, visual, and motor networks and reduced CBF in the DMN, frontoparietal network, and thalamus. Regional connectivity and CBF were significantly correlated during both the awake and propofol condition. Nevertheless changes in connectivity and CBF between the awake and deep sedation condition were only significantly correlated in a subsystem of the DMN, suggesting that, while there is significant shared variance between the modalities, changes due to propofol are relatively unique. Similar, but less significant, results were observed in the CBF-adjusted ICD analysis, providing additional evidence that connectivity differences were not fully explained by CBF. In conclusion, these results provide further evidence of alterations in large-scale brain networks are associated with reduced consciousness and suggest that different modalities capture unique aspects of these large scale changes.

Are Anxiety Disorders Associated with Accelerated Aging? A Focus on Neuroprogression.

Anxiety disorders (AnxDs) are highly prevalent throughout the lifespan, with detrimental effects on daily-life functioning, somatic health, and quality of life. An emerging perspective suggested that AnxDs may be associated with accelerated aging. In this paper, we explored the association between AnxDs and hallmarks of accelerated aging, with a specific focus on neuroprogression. We reviewed animal and human findings that suggest an overlap between processes of impaired neurogenesis, neurodegeneration, structural, functional, molecular, and cellular modifications in AnxDs, and aging. Although this research is at an early stage, our review suggests a link between anxiety and accelerated aging across multiple processes involved in neuroprogression. Brain structural and functional changes that accompany normal aging were more pronounced in subjects with AnxDs than in coevals without AnxDs, including reduced grey matter density, white matter alterations, impaired functional connectivity of large-scale brain networks, and poorer cognitive performance. Similarly, molecular correlates of brain aging, including telomere shortening, Aβ accumulation, and immune-inflammatory and oxidative/nitrosative stress, were overrepresented in anxious subjects. No conclusions about causality or directionality between anxiety and accelerated aging can be drawn. Potential mechanisms of this association, limitations of the current research, and implications for treatments and future studies are discussed.

Brain network connectivity-behavioral relationships exhibit trait-like properties: Evidence from hippocampal connectivity and memory.

Despite a growing number of studies showing relationships between behavior and resting-state functional MRI measures of large-scale brain network connectivity, no study to our knowledge has sought to investigate whether intrinsic connectivity-behavioral relationships are stable over time. In this study, we investigated the stability of such brain-behavior relationships at two timepoints, approximately 1 week apart. We focused on the relationship between the strength of hippocampal connectivity to posterior cingulate cortex and episodic memory performance. Our results showed that this relationship is stable across samples of a different age and reliable over two points in time. These findings provide the first evidence that the relationship between large-scale intrinsic network connectivity and episodic memory performance is a stable characteristic that varies between individuals.

Left-hemispheric microstructural abnormalities in children with high-functioning autism spectrum disorder.

Current theories of the neurobiological basis of autism spectrum disorder (ASD) posit an altered pattern of connectivity in large-scale brain networks. Here we used diffusion tensor imaging to investigate the microstructural properties of the white matter (WM) that mediates interregional connectivity in 36 high-functioning children with ASD (HF-ASD) as compared with 37 controls. By employing an atlas-based analysis using large deformation diffeometric morphic mapping registration, a widespread but left-lateralized pattern of abnormalities was revealed. The mean diffusivity (MD) of water in the WM of HF-ASD children was significantly elevated throughout the left hemisphere, particularly in the outer-zone cortical WM. Across diagnostic groups, there was a significant effect of age on left-hemisphere MD, with a similar reduction in MD during childhood in both typically developing and HF-ASD children. The increased MD in children with HF-ASD suggests hypomyelination and may reflect increased short-range cortico-cortical connections subsequent to early WM overgrowth. These findings also highlight left-hemispheric connectivity as relevant to the pathophysiology of ASD and indicate that the spatial distribution of microstructural abnormalities in HF-ASD is widespread and left-lateralized. This altered left-hemispheric connectivity may contribute to deficits in communication and praxis observed in ASD.