Consistency and functional specialization in the default mode brain network

Disrupted topology of hippocampal connectivity is associated with short-term antidepressant response in major depressive disorder

BACKGROUND

Graph theoretical analyses have identified disrupted functional topological organization across the brain in patients with major depressive disorder (MDD). However, the relationship between brain topology and short-term treatment responses in patients with MDD remains unknown.

METHODS

Sixty-eight patients with MDD and 63 cognitively normal (CN) subjects were recruited at baseline and underwent resting-state functional magnetic resonance imaging scans. Graph theory analysis was used to examine group differences in the whole-brain functional topological properties. The association between altered brain topology and the early antidepressant response was examined.

RESULTS

Patients with MDD showed lower normalized clustering coefficients, lower small-worldness scalars and increased nodal efficiencies in the default mode network and decreased nodal efficiencies in basal ganglia and hippocampal networks. In addition, the decreased nodal efficiency in left hippocampus was negatively correlated with depressive severity at baseline and positively correlated with changes in the depressive scores after two weeks of antidepressant treatment.

LIMITATIONS

The patients in the present study received different medications.

CONCLUSION

These findings indicated that the altered brain functional topological organization in patients with MDD is associated with the treatment response in the early phase of medication. Therefore, brain topology assessments might be considered a useful and convenient predictor of short-term antidepressant responses.

Attentional set-shifting and social abilities in children with schizotypal and comorbid autism spectrum disorders

OBJECTIVE

While diagnostically independent, autism and schizotypal disorders can co-occur. Their concurrent impact on outcomes and phenotypes has not been investigated. We investigated the impact of comorbid autism and schizotypal disorders in children on executive functioning and socio-pragmatic skills - core features of both disorders.

METHOD

Executive functioning (assessed with the Cambridge Neuropsychological Test Automated Battery) and socio-pragmatic skills (assessed using the Melbourne Assessment of Schizotypy in Kids) were investigated in a total of 67 (6-12 year old) children with autism ( n = 15; M/F = 10/5), schizotypal disorder ( n = 8; M/F = 5/3) and comorbid autism and schizotypal disorder ( n = 12; M/F = 5/7) and typically developing children ( n = 32; M/F = 17/15).

RESULTS

Both the autism and schizotypal disorder groups performed more poorly than the typically developing group on socio-pragmatic skills and overall performance (i.e. number of stages completed) of the intra-/extra-dimensional set-shifting task (all ps < 0.001). Clear distinctions between the autism and schizotypal groups were present in the intra-/extra-dimensional task relative to the typically developing group - the autism group had difficulties with extra-dimensional shifts ( p < 0.001), and the schizotypal disorder group with intra-dimensional shifts ( p = 0.08). Interestingly, the overall performance of the comorbid group on the intra-/extra-dimensional task was not significantly different from the typically developing group, and they were superior to both the autism ( p = 0.019) and schizotypal disorder ( p = 0.042) groups on socio-pragmatic skills.

CONCLUSION

The phenotypical overlap between autism and schizotypal disorders may be precipitated by different cognitive styles and/or mechanisms associated with attention and information processing. We propose that sustaining and switching attention represent two poles of irregularities across the autism and schizotypal spectra, which appear to converge in a compensatory manner in the comorbid group. Our findings highlight the importance of investigating children with a dual diagnosis of autism and schizotypal disorders, and raise intriguing questions about possible mechanisms to explain the attenuated impairment observed in the group of children with comorbid autism and schizotpyal disorders.

The default network and the combination of cognitive processes that mediate self-generated thought

Self-generated cognitions, such as recalling personal memories or empathizing with others, are ubiquitous and essential for our lives. Such internal mental processing is ascribed to the Default Mode Network, a large network of the human brain, though the underlying neural and cognitive mechanisms remain poorly understood. Here, we tested the hypothesis that our mental experience is mediated by a combination of activities of multiple cognitive processes. Our study included four functional MRI experiments with the same participants and a wide range of cognitive tasks, as well as an analytical approach that afforded the identification of cognitive processes during self-generated cognition. We showed that several cognitive processes functioned simultaneously during self-generated mental activity. The processes had specific and localized neural representations, suggesting that they support different aspects of internal processing. Overall, we demonstrate that internally directed experience may be achieved by pooling over multiple cognitive processes.

Altered DMN functional connectivity and regional homogeneity in partial epilepsy patients: a seventy cases study

Purpose

Clinically diagnosed partial epilepsy is hard to be functionally diagnosed by regular electroencephalograph (EEG) and conventional magnetic resonance imaging (MRI). By collecting transient brain regional signals, blood oxygenation level-dependent (BOLD) function MRI (BOLD-fMRI) can provide brain function change information with high accuracy. By using resting state BOLD-fMRI technique, we aim to investigate the changes of brain function in partial epilepsy patients.

Methods

BOLD-fMRI scanning was performed in 70 partial epilepsy and 70 healthy people. BOLD-fMRI data was analyzed by using the Regional Homogeneity (ReHo) method and functional connectivity of Default Mode Network (DMN) methods. The abnormal brain functional connectivity in partial epilepsy patients was detected by Statistical Parametric Mapping 8 (SPM8) analysis.

Results

Compared to healthy group, epilepsy patients showed significant decreased ReHo in left inferior parietal lobule/pre- and post-central gyrus, right thalamus/paracentral lobule/Cerebellum anterior and posterior Lobe, bilateral insula. The DMN functional connectivity regions decreased significantly in right uncus, left Inferior parietal lobule, left supramarginal gyrus, left uncus, left parahippocampa gyrus, and left superior temporal gyrus, in epilepsy patients, compared to healthy controls.

Significance

This study clarified that both ReHo and functional connectivity of DMN decreased in partial epilepsy patients compared to healthy controls. While left inferior parietal lobule was detected in both ReHo and DMN, many other identified regions were different by using these two BOLD-fMRI techniques. We propose that both ReHo and DMN patterns in BOLD-fMRI may suggest networks responsible for partial epilepsy genesis or progression.

Dual Temporal and Spatial Sparse Representation for Inferring Group-Wise Brain Networks From Resting-State fMRI Dataset

Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. First, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject. Next, based on the current brain science knowledge, a simple mathematical model is proposed to describe the complex nonlinear dynamic coupling mechanism of the brain networks, with which the group-wise intrinsic connectivity networks (ICNs) can be inferred by sparse representation for these brain functional networks (spatial domain) of all subjects. Experiments on Leiden_2180 dataset show that most group-wise ICNs obtained by the proposed DTSSR are interpretable by current brain science knowledge and are consistent with previous literature reports. The robustness of DTSSR and the reproducibility of the results are demonstrated by experiments on three different datasets (Leiden_2180, Leiden_2200, and our own dataset). The present work also shed new light on exploring the coupling mechanism of brain networks from perspective of information science.

Social brain circuitry and social cognition in infants born preterm

Preterm birth is associated with an increased risk of adverse neurologic, psychiatric, and cognitive outcomes. The brain circuits involved in processing social information are critical to all of these domains, but little work has been done to examine whether and how these circuits may be especially sensitive to prematurity. This paper contains a brief summary of some of the cognitive, psychiatric, and social outcomes associated with prematurity, followed by a description of findings from the modest body of research into social-cognitive development in infants and children born preterm. Next, findings from studies of structural and functional brain development in infants born preterm are reviewed, with an eye toward the distinctive role of the brain circuits implicated in social functioning. The goal of this review is to investigate the extent to which the putative "social brain" may have particular developmental susceptibilities to the insults associated with preterm birth, and the role of early social-cognitive development in later neurodevelopmental outcomes. Much work has been done to characterize neurobehavioral outcomes in the preterm population, but future research must incorporate both brain and behavioral measures to identify early biomarkers linked to later emerging social-cognitive clinical impairment in order to guide effective, targeted intervention.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

Human ventromedial prefrontal cortex and the positive affective processing of safety signals

Human functional magnetic resonance imaging (fMRI) studies suggest that the ventromedial prefrontal cortex (vmPFC) contributes to the learned discrimination of threat and safety signals, although its precise contribution to these processes remains unclear. One hypothesis is that the vmPFC supports the positive affective processing of safety signals linked to their implicit stress-relieving properties. We set out to test this hypothesis and to examine the specificity of vmPFC responses to safety signal processing versus its high level of 'default mode' activity. Sixty participants completed an fMRI conditioning task that involved the generation of a conditioned threat (CS+) and safety (CS-) signal following the completion of a pre-conditioning baseline. Confirming past findings, activation of the vmPFC and other midline cortical and parietal areas - broadly resembling the default mode network - robustly discriminated between the CS- and CS+. However, when adjusting for this network's characteristic 'baseline' activity, only a subset of regions, including the vmPFC, was activated by the CS-. Regional selectivity for safety signal processing was confirmed by demonstrating a significant correlation between the magnitude of vmPFC responses and self-rated positive affect evoked by the CS-. Taken together, our current findings confirm a link between human vmPFC activity and the positive affective processing of safety signals. We discuss these findings with regards a broader model of human vmPFC function and its suggested higher-order contribution to emotionally adaptive behavior.

Maturation of Brain Regions Related to the Default Mode Network during Adolescence Facilitates Narrative Comprehension

Objectives

Although the Default Mode Network (DMN) has been examined extensively in adults, developmental characteristics of this network during childhood are not fully understood.

Methods

In this longitudinal study, we characterized the developmental changes in the DMN in fifteen children who were each scanned three times during a narrative comprehension task using magnetic resonance imaging.

Results

Despite similar brain-activation patterns along developmental ages 5 to 18 years when listening to stories, increased, widely distributed deactivation of the DMN was observed in children between the ages of 11 and 18 years. Our findings suggest that changes occurring with increased age, primarily brain maturation and cognitive development drive deactivation of the DMN, which in turn might facilitate attendance to the task.

Conclusions

The interpretation of our results is as a possible reference for the typical course of deactivation of the DMN and to explain the impaired patterns in this neural network associated with different language-related pathologies.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

Frequency specific patterns of resting-state networks development from childhood to adolescence: A magnetoencephalography study

OBJECTIVE

The present study investigated frequency dependent developmental patterns of the brain resting-state networks from childhood to adolescence.

METHOD

Magnetoencephalography (MEG) data were recorded from 20 healthy subjects at resting-state with eyes-open. The resting-state networks (RSNs) was analyzed at source-level. Brain network organization was characterized by mean clustering coefficient and average path length. The correlations between brain network measures and subjects' age during development from childhood to adolescence were statistically analyzed in delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), and beta (12-30Hz) frequency bands.

RESULTS

A significant positive correlation between functional connectivity with age was found in alpha and beta frequency bands. A significant negative correlation between average path lengths with age was found in beta frequency band.

CONCLUSIONS

The results suggest that there are significant developmental changes of resting-state networks from childhood to adolescence, which matures from a lattice network to a small-world network.

Neural correlates of anxiety symptoms in mild Parkinson's disease: A prospective longitudinal voxel-based morphometry study

BACKGROUND

Anxiety is prevalent in patients with Parkinson's disease (PD) and may affect patients' quality of life. Yet, little is known about the neural basis of anxiety in PD, and none have used a longitudinal design.

METHODS

73 patients with mild PD were recruited and followed up for 18months. A whole-brain analysis was first used to identify brain regions associated with anxiety symptoms, followed by a regional analysis focusing on a priori hypothesised regions at baseline. A multivariate generalized estimating equations analysis was then conducted to determine the longitudinal association between grey matter (GM) volumetric changes of these significant regions and changes of anxiety symptoms.

RESULTS

At baseline, anxiety symptom severity was associated with decreased GM volumes in the bilateral precuneus and anterior cingulate cortex (ACC). Over 18months, increased severity of anxiety symptoms was associated with decreased GM volume in the left precuneus and ACC, independent of age, gender, education, depressive symptom severity or use of psychiatric medication.

CONCLUSIONS

These results mainly implicate the precuneus and ACC in the pathogenesis of anxiety in PD. We speculate that these structural changes could reflect the disrupted default mode network due to PD pathology, contributing to spontaneous anxiety-related self-focused thoughts.

The cerebellum, internal models and prediction in 'non-motor' aspects of language: A critical review

The emergence of studies on cerebellar contributions in 'non-motor' aspects of predictive language processing has long been awaited by researchers investigating the neural foundations of language and cognition. Despite (i) progress in research implicating the cerebellum in language processing, (ii) the widely-accepted nature of the uniform, multi-modal computation that the cerebellum implements in the form of internal models, as well as (iii) the long tradition of psycholinguistic studies addressing prediction mechanisms, research directly addressing cerebellar contributions to 'non-motor' predictive language processing has only surfaced in the last five years. This paper provides the first review of this novel field, along with a critical assessment of the studies conducted so far. While encouraging, the evidence for cerebellar involvement in 'non-motor' aspects of predictive language processing remains inconclusive under further scrutiny. Future directions are finally discussed with respect to outstanding questions in this novel field of research.

Graph-Theoretical Study of Functional Changes Associated with the Iowa Gambling Task

The primary aim of this study was to examine changes in functional brain network organization from rest to the Iowa Gambling Task (IGT) using a graph-theoretical approach. Although many functional neuroimaging studies have examined task-based activations in complex-decision making tasks, changes in functional network organization during this task remain unexplored. This study used a repeated-measures approach to examine changes in functional network organization across multiple sessions of resting-state and IGT scans. The results revealed that global network organization shifted from a local, clustered organization at rest to a more global, integrated organization during the IGT. In addition, network organization was stable across sessions of rest and the IGT. Regional analyses of the Default Mode Network (DMN) and Fronto-Parietal Network (FPN) revealed differential patterns of change in regional network organization from rest to the IGT. The results of this study reveal that global and regional network organization is significantly modulated across states and fairly stable over time, and that network changes in the FPN are particularly important in the decision-making processes necessary for successful IGT performance.

Neural signatures of human fear conditioning: an updated and extended meta-analysis of fMRI studies

Classical Pavlovian fear conditioning remains the most widely employed experimental model of fear and anxiety, and continues to inform contemporary pathophysiological accounts of clinical anxiety disorders. Despite its widespread application in human and animal studies, the neurobiological basis of fear conditioning remains only partially understood. Here we provide a comprehensive meta-analysis of human fear-conditioning studies carried out with functional magnetic resonance imaging (fMRI), yielding a pooled sample of 677 participants from 27 independent studies. As a distinguishing feature of this meta-analysis, original statistical brain maps were obtained from the authors of 13 of these studies. Our primary analyses demonstrate that human fear conditioning is associated with a consistent and robust pattern of neural activation across a hypothesized genuine network of brain regions resembling existing anatomical descriptions of the 'central autonomic-interoceptive network'. This finding is discussed with a particular emphasis on the neural substrates of conscious fear processing. Our associated meta-analysis of functional deactivations-a scarcely addressed dynamic in fMRI fear-conditioning studies-also suggests the existence of a coordinated brain response potentially underlying the 'safety signal' (that is, non-threat) processing. We attempt to provide an integrated summary on these findings with the view that they may inform ongoing studies of fear-conditioning processes both in healthy and clinical populations, as investigated with neuroimaging and other experimental approaches.

The Self-Pleasantness Judgment Modulates the Encoding Performance and the Default Mode Network Activity

In this functional magnetic resonance imaging (fMRI) study, we evaluated the effect of self-relevance on cerebral activity and behavioral performance during an incidental encoding task. Recent findings suggest that pleasantness judgments reliably induce self-oriented (internal) thoughts and increase default mode network (DMN) activity. We hypothesized that this increase in DMN activity would relate to increased memory recognition for pleasantly-judged stimuli (which depend on internally-oriented attention) but decreased recognition for unpleasantly-judged items (which depend on externally-oriented attention). To test this hypothesis, brain activity was recorded from 21 healthy participants while they performed a pleasantness judgment requiring them to rate visual stimuli as pleasant or unpleasant. One hour later, participants performed a surprise memory recognition test outside of the scanner. Thus, we were able to evaluate the effects of pleasant and unpleasant judgments on cerebral activity and incidental encoding. The behavioral results showed that memory recognition was better for items rated as pleasant than items rated as unpleasant. The whole brain analysis indicated that successful encoding (SE) activates the inferior frontal and lateral temporal cortices, whereas unsuccessful encoding (UE) recruits two key medial posterior DMN regions, the posterior cingulate cortex (PCC) and precuneus (PCU). A region of interest (ROI) analysis including classic DMN areas, revealed significantly greater involvement of the medial prefrontal cortex (mPFC) in pleasant compared to unpleasant judgments, suggesting this region’s involvement in self-referential (i.e., internal) processing. This area may be responsible for the greater recognition performance seen for pleasant stimuli. Furthermore, a significant interaction between the encoding performance (successful vs. unsuccessful) and pleasantness was observed for the PCC, PCU and inferior frontal gyrus (IFG). Overall, our results suggest the involvement of medial frontal and parietal DMN regions during the evaluation of self-referential pleasantness. We discuss these results in terms of the introspective referential of pleasantness judgments and the differential brain modulation based on internally- vs. externally-oriented attention during encoding.

Are batterers different from other criminals? An fMRI study

Intimate partner violence (IPV) is a complex and global phenomenon that requires a multi-perspective analysis. Nevertheless, the number of neuroscientific studies conducted on this issue is scarce as compared with studies of other types of violence, and no neuroimaging studies comparing batterers to other criminals have been conducted. Thus, the main aim of this study was to compare the brain functioning of batterers to that of other criminals when they are exposed to IPV or general violence pictures. An fMRI study was conducted in 21 batterers and 20 other criminals while they observed IPV images (IPVI), general violence images (GVI) and neutral images (NI). Results demonstrated that batterers, compared with other criminals, exhibited a higher activation in the anterior and posterior cingulate cortex and in the middle prefrontal cortex and a decreased activation in the superior prefrontal cortex to IPVI compared to NI. The paired t-test comparison between IPVI and GVI for each group showed engagement of the medial prefrontal cortex, the posterior cingulate and the left angular cortices to IPVI in the batterer group only. These results could have important implications for a better understanding of the IPV phenomenon.

Mapping the self in the brain's default mode network

The brain's default mode network (DMN) has become closely associated with self-referential mental activity, particularly in the resting-state. While the DMN is important for such processes, it has functions other than self-reference, and self-referential processes are supported by regions outside of the DMN. In our study of 88 participants, we examined self-referential and resting-state processes to clarify the extent to which DMN activity was common and distinct between the conditions. Within areas commonly activated by self-reference and rest we sought to identify those that showed additional functional specialization for self-referential processes: these being not only activated by self-reference and rest but also showing increased activity in self-reference versus rest. We examined the neural network properties of the identified 'core-self' DMN regions-in medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and inferior parietal lobule-using dynamic causal modeling. The optimal model identified was one in which self-related processes were driven via PCC activity and moderated by the regulatory influences of MPFC. We thus confirm the significance of these regions for self-related processes and extend our understanding of their functionally specialized roles.

Neurodevelopmental correlates of proneness to guilt and shame in adolescence and early adulthood

Investigating how brain development during adolescence and early adulthood underlies guilt- and shame-proneness may be important for understanding risk processes for mental disorders. The aim of this study was to investigate the neurodevelopmental correlates of interpersonal guilt- and shame-proneness in healthy adolescents and young adults using structural magnetic resonance imaging (sMRI). Sixty participants (age range: 15–25) completed sMRI and self-report measures of interpersonal guilt- and shame-proneness. Independent of interpersonal guilt, higher levels of shame-proneness were associated with thinner posterior cingulate cortex (PCC) thickness and smaller amygdala volume. Higher levels of shame-proneness were also associated with attenuated age-related reductions in thickness of lateral orbitofrontal cortex (lOFC). Our findings highlight the complexities in understanding brain–behavior relationships during the adolescent/young adult period. Results were consistent with growing evidence that accelerated cortical thinning during adolescence may be associated with superior socioemotional functioning. Further research is required to understand the implications of these findings for mental disorders characterized by higher levels of guilt and shame.

Disrupted subject-specific gray matter network properties and cognitive dysfunction in type 1 diabetes patients with and without proliferative retinopathy

INTRODUCTION

Type 1 diabetes mellitus (T1DM) patients, especially with concomitant microvascular disease, such as proliferative retinopathy, have an increased risk of cognitive deficits. Local cortical gray matter volume reductions only partially explain these cognitive dysfunctions, possibly because volume reductions do not take into account the complex connectivity structure of the brain. This study aimed to identify gray matter network alterations in relation to cognition in T1DM.

METHODS

We investigated if subject-specific structural gray matter network properties, constructed from T1-weighted MRI scans, were different between T1DM patients with (n = 51) and without (n = 53) proliferative retinopathy versus controls (n = 49), and were associated to cognitive decrements and fractional anisotropy, as measured by voxel-based TBSS. Global normalized and local (45 bilateral anatomical regions) clustering coefficient and path length were assessed. These network properties measure how the organization of connections in a network differs from that of randomly connected networks.

RESULTS

Global gray matter network topology was more randomly organized in both T1DM patient groups versus controls, with the largest effects seen in patients with proliferative retinopathy. Lower local path length values were widely distributed throughout the brain. Lower local clustering was observed in the middle frontal, postcentral, and occipital areas. Complex network topology explained up to 20% of the variance of cognitive decrements, beyond other predictors. Exploratory analyses showed that lower fractional anisotropy was associated with a more random gray matter network organization.

CONCLUSION

T1DM and proliferative retinopathy affect cortical network organization that may consequently contribute to clinically relevant changes in cognitive functioning in these patients.

Reliability correction for functional connectivity: Theory and implementation

Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe's contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity.

Functional Connectivity Bias in the Prefrontal Cortex of Psychopaths

BACKGROUND

Psychopathy is characterized by a distinctive interpersonal style that combines callous-unemotional traits with inflexible and antisocial behavior. Traditional emotion-based perspectives link emotional impairment mostly to alterations in amygdala-ventromedial frontal circuits. However, these models alone cannot explain why individuals with psychopathy can regularly benefit from emotional information when placed on their focus of attention and why they are more resistant to interference from nonaffective contextual cues. The present study aimed to identify abnormal or distinctive functional links between and within emotional and cognitive brain systems in the psychopathic brain to characterize further the neural bases of psychopathy.

METHODS

High-resolution anatomic magnetic resonance imaging with a functional sequence acquired in the resting state was used to assess 22 subjects with psychopathy and 22 control subjects. Anatomic and functional connectivity alterations were investigated first using a whole-brain analysis. Brain regions showing overlapping anatomic and functional changes were examined further using seed-based functional connectivity mapping.

RESULTS

Subjects with psychopathy showed gray matter reduction involving prefrontal cortex, paralimbic, and limbic structures. Anatomic changes overlapped with areas showing increased degree of functional connectivity at the medial-dorsal frontal cortex. Subsequent functional seed-based connectivity mapping revealed a pattern of reduced functional connectivity of prefrontal areas with limbic-paralimbic structures and enhanced connectivity within the dorsal frontal lobe in subjects with psychopathy.

CONCLUSIONS

Our results suggest that a weakened link between emotional and cognitive domains in the psychopathic brain may combine with enhanced functional connections within frontal executive areas. The identified functional alterations are discussed in the context of potential contributors to the inflexible behavior displayed by individuals with psychopathy.

Pediatric applications of functional magnetic resonance imaging

Pediatric functional MRI has been used for the last 2 decades but is now gaining wide acceptance in the preoperative workup of children with brain tumors and medically refractory epilepsy. This review covers pediatrics-specific difficulties such as sedation and task paradigm selection according to the child's age and cognitive level. We also illustrate the increasing uses of functional MRI in the depiction of cognitive function, neuropsychiatric disorders and response to pharmacological agents. Finally, we review the uses of resting-state fMRI in the evaluation of children and in the detection of epileptogenic regions.

Depressive Rumination, the Default-Mode Network, and the Dark Matter of Clinical Neuroscience

The intuitive association between self-focused rumination in major depressive disorder (MDD) and the self-referential operations performed by the brain's default-mode network (DMN) has prompted interest in examining the role of the DMN in MDD. In this article, we present meta-analytic findings showing reliably increased functional connectivity between the DMN and subgenual prefrontal cortex (sgPFC)-connectivity that often predicts levels of depressive rumination. We also present meta-analytic findings that, while there is reliably increased regional cerebral blood flow in sgPFC in MDD, no such abnormality has been reliably observed in nodes of the DMN. We then detail a model that integrates the body of research presented. In this model, we propose that increased functional connectivity between sgPFC and the DMN in MDD represents an integration of the self-referential processes supported by the DMN with the affectively laden, behavioral withdrawal processes associated with sgPFC-an integration that produces a functional neural ensemble well suited for depressive rumination and that, in MDD, abnormally taxes only sgPFC and not the DMN. This synthesis explains a broad array of existing data concerning the neural substrates of depressive rumination and provides an explicit account of functional abnormalities in sgPFC in MDD.

Brains striving for coherence: Long-term cumulative plot formation in the default mode network

Many everyday activities, such as engaging in conversation or listening to a story, require us to sustain attention over a prolonged period of time while integrating and synthesizing complex episodic content into a coherent mental model. Humans are remarkably capable of navigating and keeping track of all the parallel social activities of everyday life even when confronted with interruptions or changes in the environment. However, the underlying cognitive and neurocognitive mechanisms of such long-term integration and profiling of information remain a challenge to neuroscience. While brain activity is generally traceable within the short time frame of working memory (milliseconds to seconds), these integrative processes last for minutes, hours or even days. Here we report two experiments on story comprehension. Experiment I establishes a cognitive dissociation between our comprehension of plot and incidental facts in narratives: when episodic material allows for long-term integration in a coherent plot, we recall fewer factual details. However, when plot formation is challenged, we pay more attention to incidental facts. Experiment II investigates the neural underpinnings of plot formation. Results suggest a central role for the brain's default mode network related to comprehension of coherent narratives while incoherent episodes rather activate the frontoparietal control network. Moreover, an analysis of cortical activity as a function of the cumulative integration of narrative material into a coherent story reveals to linear modulations of right hemisphere posterior temporal and parietal regions. Together these findings point to key neural mechanisms involved in the fundamental human capacity for cumulative plot formation.

An Exploratory Investigation of Functional Network Connectivity of Empathy and Default Mode Networks in a Free-Viewing Task

This study reports dynamic functional network connectivity (dFNC) analysis on time courses of putative empathy networks-cognitive, emotional, and motor-and the default mode network (DMN) identified from independent components (ICs) derived by the group independent component analysis (ICA) method. The functional magnetic resonance imaging (fMRI) data were collected from 15 subjects watching movies of three genres, an animation (S1), Indian Hindi (S2), and a Hollywood English (S3) movie. The hypothesis of the study is that empathic engagement in a movie narrative would modulate the activation with the DMN. The clippings were individually rated for emotional expressions, context, and empathy self-response by the fMRI subjects post scanning and by 40 participants in an independent survey who rated at four time intervals in each clipping. The analysis illustrates the following: (a) the ICA method separated ICs with areas reported for empathy response and anterior/posterior DMNs. An IC indicating insula region activation reported to be crucial for the emotional empathy network was separated for S2 and S3 movies only, but not for S1, (b) the dFNC between DMN and ICs corresponding to cognitive empathy network showed higher positive periodical fluctuating correlations for all three movies, while ICs with areas crucial to motor or emotional empathy display lower positive or negative correlation values with no distinct periodicity. A possible explanation for the lower values and anticorrelation between the DMN and emotional empathy networks could possibly be inhibition due to internal self-reflections, attributed to DMN, while processing and preparing a response to external emotional content. The positive higher correlation values for cognitive empathy networks may reflect a functional overlap with DMN for enhanced internal self-reflections, inferring beliefs and intentions about the 'other', all triggered by the external stimuli. The findings are useful in the study of deviations in functional synergies of large complex networks associated with empathy responses and DMN in clinical applications like autism and schizophrenia.

Rest Is Not Idleness: Implications of the Brain's Default Mode for Human Development and Education

When people wakefully rest in the functional MRI scanner, their minds wander, and they engage a so-called default mode (DM) of neural processing that is relatively suppressed when attention is focused on the outside world. Accruing evidence suggests that DM brain systems activated during rest are also important for active, internally focused psychosocial mental processing, for example, when recalling personal memories, imagining the future, and feeling social emotions with moral connotations. Here the authors review evidence for the DM and relations to psychological functioning, including associations with mental health and cognitive abilities like reading comprehension and divergent thinking. This article calls for research into the dimensions of internally focused thought, ranging from free-form daydreaming and off-line consolidation to intensive, effortful abstract thinking, especially with socioemotional relevance. It is argued that the development of some socioemotional skills may be vulnerable to disruption by environmental distraction, for example, from certain educational practices or overuse of social media. The authors hypothesize that high environmental attention demands may bias youngsters to focus on the concrete, physical, and immediate aspects of social situations and self, which may be more compatible with external attention. They coin the term constructive internal reflection and advocate educational practices that promote effective balance between external attention and internal reflection.

Oscillatory correlates of moral decision-making: Effect of personality

The role of emotion in moral decision-making is still a matter of debate. Greene, Sommerville, Nystrom, Darley, and Cohen (2001) argue that 'personal' moral judgments are driven by emotional responses, while 'impersonal' judgments are largely driven by cognitive processes. In this study, oscillatory correlates of decision-making were compared in moral personal, moral impersonal, and nonmoral conditions, as well as in trials associated with utilitarian (i.e., favoring the 'greater good' over individual rights) and non-utilitarian choices. Event-related synchronization in delta and theta bands was greater in the right temporal lobe in personal than in both nonmoral and impersonal moral condition. Graph-theoretical analysis of connectivity patterns showed the prominent role of the orbitofrontal and cingulate cortices in personal moral decision-making, implying greater emotional and self-processing. Higher conscientiousness and intellect and lower behavioral activation were associated with greater difference in oscillatory responses between utilitarian and non-utilitarian choices in personal than in impersonal condition, indicating that sensitivity to moral issues and the ability to grasp the nuances of moral situation are essential for understanding the implications of utilitarian choices in personal and impersonal conditions.

The default mode network as a biomarker for monitoring the therapeutic effects of meditation

The default mode network (DMN) is a group of anatomically separate regions in the brain found to have synchronized patterns of activation in functional magnetic resonance imaging (fMRI). Mentation associated with the DMN includes processes such as mind wandering, autobiographical memory, self-reflective thought, envisioning the future, and considering the perspective of others. Abnormalities in the DMN have been linked to symptom severity in a variety of mental disorders indicating that the DMN could be used as a biomarker for diagnosis. These correlations have also led to the use of DMN modulation as a biomarker for assessing pharmacological treatments. Concurrent research investigating the neural correlates of meditation, have associated DMN modulation with practice. Furthermore, meditative practice is increasingly understood to have a beneficial role in the treatment of mental disorders. Therefore we propose the use of DMN measures as a biomarker for monitoring the therapeutic effects of meditation practices in mental disorders. Recent findings support this perspective, and indicate the utility of DMN monitoring in understanding and developing meditative treatments for these debilitating conditions.

Disrupted cortical network as a vulnerability marker for obsessive-compulsive disorder

Morphological alterations of brain structure are generally assumed to be involved in the pathophysiology of obsessive–compulsive disorder (OCD). Yet, little is known about the morphological connectivity properties of structural brain networks in OCD or about the heritability of those morphological connectivity properties. To better understand these properties, we conducted a study that defined three different groups: OCD group with 30 subjects, siblings group with 19 subjects, and matched controls group with 30 subjects. A structural brain network was constructed using 68 cortical regions of each subject within their respective group (i.e., one brain network for each group). Both small-worldness and modularity were measured to reflect the morphological connectivity properties of each constructed structural brain network. When compared to the matched controls, the structural brain networks of patients with OCD indeed exhibited atypical small-worldness and modularity. Specifically, small-worldness showed decreased local efficiency, and modularity showed reduced intra-connectivity in Module III (default mode network) and increased interconnectivity between Module I (executive function) and Module II (cognitive control/spatial). Intriguingly, the structured brain networks of the unaffected siblings showed similar small-worldness and modularity as OCD patients. Based on the atypical structural brain networks observed in OCD patients and their unaffected siblings, abnormal small-worldness and modularity may indicate a candidate endophenotype for OCD.

Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity

Successful performance of challenging cognitive tasks depends on a consistent functional segregation of activity within the default-mode network, on the one hand, and control networks encompassing frontoparietal and cingulo-opercular areas on the other. Recent work, however, has suggested that in some cognitive control contexts nodes within the default-mode and control networks may actually cooperate to achieve optimal task performance. Here, we used functional magnetic resonance imaging to examine whether the ability to relate variables while solving a cognitive reasoning problem involves transient increases in connectivity between default-mode and control regions. Participants performed a modified version of the classic Wason selection task, in which the number of variables to be related is systematically varied across trials. As expected, areas within the default-mode network showed a parametric deactivation with increases in relational complexity, compared with neural activity in null trials. Critically, some of these areas also showed enhanced connectivity with task-positive control regions. Specifically, task-based connectivity between the striatum and the angular gyri, and between the thalamus and right temporal pole, increased as a function of relational complexity. These findings challenge the notion that functional segregation between regions within default-mode and control networks invariably support cognitive task performance, and reveal previously unknown roles for the striatum and thalamus in managing network dynamics during cognitive reasoning.

Enhanced disease characterization through multi network functional normalization in fMRI

Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

Differential relations between juvenile psychopathic traits and resting state network connectivity

Traditionally, neurobiological research on psychopathy has focused on categorical differences in adults. However, there is evidence that psychopathy is best described by a set of relatively independent personality dimensions, that is, callous-unemotional, grandiose-manipulative, and impulsive-irresponsible traits, which can be reliably detected in juveniles, allowing investigation of the neural mechanisms leading to psychopathy. Furthermore, complex psychiatric disorders like psychopathy are increasingly being conceptualized as disorders of brain networks. The intrinsic organization of the brain in such networks is reflected by coherent fluctuations in resting state networks (RSNs), but these have not been studied in sufficient detail in relation to juvenile psychopathic traits yet. The current study investigated the distinct associations of juvenile psychopathic traits dimensions with RSN connectivity. Resting-state functional MRI and independent component analysis were used to assess RSN connectivity in a large sample of adolescents (n = 130, mean age 17.8 years) from a childhood arrestee cohort. Associations between scores on each of the three psychopathic traits dimensions and connectivity within and between relevant RSNs were investigated. Callous-unemotional traits were related to aberrant connectivity patterns of the default mode network, which has been implicated in self-referential and moral processes. Impulsive-irresponsible traits were associated with altered connectivity patterns in the frontoparietal cognitive control networks. Grandiose-manipulative traits were not associated with altered connectivity patterns. These findings confirm the association between psychopathic traits and brain network connectivity, and considerably add to emerging evidence supporting neurobiological heterogeneity in the processes leading to psychopathy.

Developmental changes in large-scale network connectivity in autism

Background

Disrupted cortical connectivity is thought to underlie the complex cognitive and behavior profile observed in individuals with autism spectrum disorder (ASD). Previous neuroimaging research has identified patterns of both functional hypo- and hyper-connectivity in individuals with ASD. A recent theory attempting to reconcile conflicting results in the literature proposes that hyper-connectivity of brain networks may be more characteristic of young children with ASD, while hypo-connectivity may be more prevalent in adolescents and adults with the disorder when compared to typical development (TD) (Uddin etal., 2013). Previous work has examined only young children, mixed groups of children and adolescents, or adult cohorts in separate studies, leaving open the question of developmental influences on functional brain connectivity in ASD.

Methods

The current study tests this developmental hypothesis by examining within- and between-network resting state functional connectivity in a large sample of 26 children, 28 adolescents, and 18 adults with ASD and age- and IQ-matchedTD individuals for the first time using an entirely data-driven approach. Independent component analyses (ICA) and dual regression was applied to data from three age cohorts to examine the effects of participant age on patterns of within-networkwhole-brain functional connectivity in individuals with ASD compared with TD individuals. Between-network connectivity differences were examined for each age cohort by comparing correlations between ICA components across groups.

Results

We find that in the youngest cohort (age 11 and under), children with ASD exhibit hyper-connectivity within large-scale brain networks as well as decreased between-network connectivity compared with age-matchedTD children. In contrast, adolescents with ASD (age 11–18) do not differ from TD adolescents in within-network connectivity, yet show decreased between-network connectivity compared with TD adolescents. Adults with ASD show no within- or between-network differences in functional network connectivity compared with neurotypical age-matched individuals.

Conclusions

Characterizing within- and between-network functional connectivity in age-stratified cohorts of individuals with ASD and TD individuals demonstrates that functional connectivity atypicalities in the disorder are not uniform across the lifespan. These results demonstrate how explicitly characterizing participant age and adopting a developmental perspective can lead to a more nuanced understanding of atypicalities of functional brain connectivity in autism.

Frequency-dependent amplitude alterations of resting-state spontaneous fluctuations in late-onset depression

There is limited amplitude of low-frequency fluctuation (ALFF) of resting-state functional magnetic resonance imaging (fMRI) studies in late-onset depression (LOD) but reported different results. This may be due to the impact of different frequency bands. In this study, we examined the ALFF in five different frequency bands (slow-6: 0-0.01 Hz; slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.167 Hz, and slow-2: 0.167-0.25 Hz) within the whole brain during resting-state fMRI in 16 LOD patients and 16 normal control (NC) subjects. The ALFF of primary effect of disease was widely distributed over left cerebellum anterior lobe, left cerebellum posterior lobe, left middle orbitofrontal gyrus, left superior occipital, and right superior parietal, while the interaction effect of disease and frequency was distributed over right superior frontal gyrus. Further relationship analysis findings suggest these abnormal ALFF may relate to cognitive dysfunction of LOD. Therefore, our data show that LOD patients have widespread abnormalities in intrinsic brain activity, which is dependent on the frequency band, and suggest that future studies should take the frequency bands into account when measuring intrinsic brain activity.

Oscillatory correlates of autobiographical memory

Recollection of events from one's own life is referred to as autobiographical memory. Autobiographical memory is an important part of our self. Neuroimaging findings link self-referential processes with the default mode network (DMN). Much evidence coming primarily from functional magnetic resonance imaging studies shows that autobiographical memory and DMN have a common neural base. In this study, electroencephalographic data collected in 47 participants during recollection of autobiographical episodes were analyzed using temporal and spatial independent component analyses in combination with source localization. Autobiographical remembering was associated with an increase of spectral power in alpha and beta and a decrease in delta band. The increase of alpha power, as estimated by sLORETA, was most prominent in the posterior DMN, but was also observed in visual and motor cortices, prompting an assumption that it is associated with activation of DMN and inhibition of irrelevant sensory and motor areas. In line with data linking delta oscillations with aversive states, decrease of delta power was more pronounced in episodes associated with positive emotions, whereas episodes associated with negative emotions were accompanied by an increase of delta power. Vividness of recollection correlated positively with theta oscillations. These results highlight the leading role of alpha oscillations and the DMN in the processes accompanying autobiographical remembering.

Resting-state anticorrelations between medial and lateral prefrontal cortex: association with working memory, aging, and individual differences

We examined how variation in working memory (WM) capacity due to aging or individual differences among young adults is associated with intrinsic or resting-state anticorrelations, particularly between (1) the medial prefrontal cortex (MPFC), a component of the default-mode network (DMN) that typically decreases in activation during external, attention-demanding tasks, and (2) the dorsolateral prefrontal cortex (DLPFC), a component of the fronto-parietal control network that supports executive functions and WM and typically increases in activation during attention-demanding tasks. We compared the magnitudes of MPFC-DLPFC anticorrelations between healthy younger and older participants (Experiment 1) and related the magnitudes of these anticorrelations to individual differences on two behavioral measures of WM capacity in two independent groups of young adults (Experiments 1 and 2). Relative to younger adults, older adults exhibited reductions in WM capacity and in MPFC-DLPFC anticorrelations. Within younger adults, greater MPFC-DLPFC anticorrelation at rest correlated with greater WM capacity. These findings show that variation in MPFC-DLPFC anticorrelations, whether related to aging or to individual differences, may reflect an intrinsic functional brain architecture supportive of WM capacity.

Exploring spatiotemporal network transitions in task functional MRI

A critical question for cognitive neuroscience regards how transitions between cognitive states emerge from the dynamic activity of functional brain networks. Here we combine a simple data reorganization with spatial independent component analysis (ICA), enabling a spatiotemporal ICA (stICA) which captures the consistent evolution of networks during the onset and offset of a task. The technique was applied to functional magnetic resonance imaging (MRI) (FMRI) datasets involving alternating between rest and task, and to simple synthetic data. Starting and finishing time-points of periods of interest (anchors) were defined at task block onsets and offsets. For each subject, the 10 volumes following each anchor were extracted and concatenated spatially, producing a single 3D sample. Samples for all anchors and subjects were concatenated along the fourth dimension. This 4D dataset was decomposed using ICA into spatiotemporal components. One component exhibited the transition with task onset from a default mode network (DMN) becoming less active to a frontoparietal control network becoming more active. We observed other changes with relevance to understanding network dynamics, for example, the DMN showed a changing spatial distribution, shifting to an anterior/superior pattern of deactivation during task from a posterior/inferior pattern during rest. By anchoring analyses to periods associated with the onsets and offsets of task, our approach reveals novel aspects of the dynamics of network activity accompanying these transitions. Importantly, these findings were observed without specifying a priori either the spatial networks or the task time courses.

Brain electrical activities of dancers and fast ball sports athletes are different

Exercise training has been shown not only to influence physical fitness positively but also cognition in healthy and impaired populations. However, some particular exercise types, even though comparable based on physical efforts, have distinct cognitive and sensorimotor features. In this study, the effects of different types of exercise, such as fast ball sports and dance training, on brain electrical activity were investigated. Electroencephalography (EEG) scans were recorded in professional dancer, professional fast ball sports athlete (FBSA) and healthy control volunteer groups consisting of twelve subjects each. In FBSA, power of delta and theta frequency activities of EEG was significantly higher than those of the dancers and the controls. Conversely, dancers had significantly higher amplitudes in alpha and beta bands compared to FBSA and significantly higher amplitudes in the alpha band in comparison with controls. The results suggest that cognitive features of physical training can be reflected in resting brain electrical oscillations. The differences in resting brain electrical oscillations between the dancers and the FBSA can be the result of innate network differences determining the talents and/or plastic changes induced by physical training.