Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance

Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference

Learning new rules rapidly and effectively via instructions is ubiquitous in our daily lives, yet the underlying cognitive and neural mechanisms are complex. Using functional magnetic resonance imaging we examined the effects of different instructional load conditions (4 vs. 10 stimulus-response rules) on functional couplings during rule implementation (always 4 rules). Focusing on connections of lateral prefrontal cortex (LPFC) regions, the results emphasized an opposing trend of load-related changes in LPFC-seeded couplings. On the one hand, during the low-load condition LPFC regions were more strongly coupled with cortical areas mostly assigned to networks such as the fronto-parietal network and the dorsal attention network. On the other hand, during the high-load condition, the same LPFC areas were more strongly coupled with default mode network areas. These results suggest differences in automated processing evoked by features of the instruction and an enduring response conflict mediated by lingering episodic long-term memory traces when instructional load exceeds working memory capacity limits. The ventrolateral prefrontal cortex (VLPFC) exhibited hemispherical differences regarding whole-brain coupling and practice-related dynamics. Left VLPFC connections showed a persistent load-related effect independent of practice and were associated with 'objective' learning success in overt behavioral performance, consistent with a role in mediating the enduring influence of the initially instructed task rules. Right VLPFC's connections, in turn, were more susceptible to practice-related effects, suggesting a more flexible role possibly related to ongoing rule updating processes throughout rule implementation.

Reward enhances connectivity between the ventral striatum and the default mode network

The default mode network (DMN) has been theorized to participate in a range of social, cognitive, and affective functions. Yet, previous accounts do not consider how the DMN contributes to other brain regions depending on psychological context, thus rendering our understanding of DMN function incomplete. We addressed this gap by applying a novel network-based psychophysiological interaction (nPPI) analysis to the reward task within the Human Connectome Project. We first focused on the task-evoked responses of the DMN and other networks involving the prefrontal cortex, including the executive control network (salience network) and the left and right frontoparietal networks. Consistent with a host of prior studies, the DMN exhibited a relative decrease in activation during the task, while the other networks exhibited a relative increase during the task. Next, we used nPPI analyses to assess whether these networks exhibit task-dependent changes in connectivity with other brain regions. Strikingly, we found that the experience of reward enhances task-dependent connectivity between the DMN and the ventral striatum, an effect that was specific to the DMN. Surprisingly, the strength of DMN-VS connectivity was correlated with personality characteristics relating to openness. Taken together, these results advance models of DMN by demonstrating how it contributes to other brain systems during task performance and how those contributions relate to individual differences.

Functional Connectivity as a Potential Mechanism for Language Plasticity

BACKGROUND AND OBJECTIVES

Task fMRI is a clinical tool for language lateralization, but has limitations, and cannot provide information about network-level plasticity. Additional methods are needed to improve the precision of presurgical language mapping. We investigate language resting-state functional connectivity (RS fMRI; FC) in typically developing children (TD) and children with epilepsy. Our objectives were to (1) understand how FC components differ between TD children and those with epilepsy; (2) elucidate how the location of disease (frontal/temporal epilepsy foci) affects FC; and (3) investigate the relationship between age and FC.

METHODS

Our sample included 55 TD children (mean age 12 years, range 7-18) and 31 patients with focal epilepsy (mean age 13 years, range 7-18). All participants underwent RS fMRI. Using a bilateral canonical language map as target, vertex-wise intrahemispheric FC map and interhemispheric FC map for each participant were computed and thresholded at top 10% to compute an FC laterality index (FCLI; [(L - R)/(L + R)]) of the frontal and temporal regions for both integration (intrahemispheric FC; FCLIi) and segregation (interhemispheric FC; FCLIs) maps.

RESULTS

We found FC differences in the developing language network based on disease, seizure foci location, and age. Frontal and temporal FCLIi was different between groups (t[84] = 2.82, p < 0.01; t[84] = 4.68, p < 0.01, respectively). Frontal epilepsy foci had the largest differences from TD (Cohen d frontal FCLIi = 0.84, FCLIs = 0.51; temporal FCLIi = 1.29). Development and disease have opposing influences on the laterality of FC based on groups. In the frontal foci group, FCLIi decreased with age (r = -0.42), whereas in the temporal foci group, FCLIi increased with age (r = 0.40). Within the epilepsy group, increases in right frontal integration FCLI relates to increased right frontal task activation in our mostly left language dominant group (r = 0.52, p < 0.01). Language network connectivity is associated with higher verbal intelligence in children with epilepsy (r = 0.45, p < 0.05).

DISCUSSION

These findings lend preliminary evidence that FC reflects network plasticity in the form of adaptation and compensation, or the ability to recruit support and reallocate resources within and outside of the traditional network to compensate for disease. FC expands on task-based fMRI and provides complementary and potentially useful information about the language network that is not captured using task-based fMRI alone.

Attention networks and the intrinsic network structure of the human brain

Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large‐scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti‐grayordinates. Resulting group maps were compared to the group‐level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto‐parietal and midcingulo‐insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

Stronger connectivity and higher extraversion protect against stress-related deterioration of cognitive functions

Here we attempted to define the relationship between: EEG activity, personality and coping during lockdown. We were in a unique situation since the COVID-19 outbreak interrupted our independent longitudinal study. We already collected a significant amount of data before lockdown. During lockdown, a subgroup of participants willingly continued their engagement in the study. These circumstances provided us with an opportunity to examine the relationship between personality/cognition and brain rhythms in individuals who continued their engagement during lockdown compared to control data collected well before pandemic. The testing consisted of a one-time assessment of personality dimensions and two sessions of EEG recording and deductive reasoning task. Participants were divided into groups based on the time they completed the second session: before or during the COVID-19 outbreak ‘Pre-pandemic Controls’ and ‘Pandemics’, respectively. The Pandemics were characterized by a higher extraversion and stronger connectivity, compared to Pre-pandemic Controls. Furthermore, the Pandemics improved their cognitive performance under long-term stress as compared to the Pre-Pandemic Controls matched for personality traits to the Pandemics. The Pandemics were also characterized by increased EEG connectivity during lockdown. We posit that stronger EEG connectivity and higher extraversion could act as a defense mechanism against stress-related deterioration of cognitive functions.

Gyral-sulcal contrast in intrinsic functional brain networks across task performances

Functional mechanism of the brain and its relationship with the brain structural substrate have been an interest for multiple disciplines for centuries. Recently, gyri and sulci, two basic cortical folding patterns, have been demonstrated to act different functional roles. Specifically, a variety of functional MRI (fMRI) studies have consistently suggested that gyri represent a global functional center while sulci serve as a local functional unit under either resting state or task stimulus, which are further supported by brain structural analysis reporting that gyri have thicker cortex and denser long-distance axonal fibers. However, the consistency of such gyral-sulcal functional difference across different task stimuli, as well as its association with task conditions, remains to be explored. To this end, we used intrinsic networks as the testbed for cross-task comparison, and adopted a computational framework of dictionary learning and sparse representation of whole-brain fMRI signals to systematically examine the potential gyral-sulcal difference in signal representation residual (SRR) which reflected the degree of global functional communication. Using all seven task-based fMRI datasets in Human Connectome Project Q1 release, we found that within the intrinsic functional networks, the fMRI SRR was significantly smaller on gyral regions than on sulcal regions across different task stimuli, indicating that gyral regions were more involved in global functions of the brain and interregional communications. Moreover, the magnitudes of such gyral-sulcal difference varied across task conditions and intrinsic networks. Our work adds novel explanation and insight to the existing knowledge of functional differences between gyri and sulci.

Task-evoked reconfiguration of the fronto-parietal network is associated with cognitive performance in brain tumor patients

In healthy participants, the strength of task-evoked network reconfigurations is associated with cognitive performance across several cognitive domains. It is, however, unclear whether the capacity for network reconfiguration also plays a role in cognitive deficits in brain tumor patients. In the current study, we examined whether the level of reconfiguration of the fronto-parietal (‘FPN’) and default mode network (‘DMN’) during task execution is correlated with cognitive performance in patients with different types of brain tumors. For this purpose, we combined data from a resting state and task-fMRI paradigm in patients with a glioma or meningioma. Cognitive performance was measured using the in-scanner working memory task, as well as an out-of-scanner cognitive flexibility task. Task-evoked changes in functional connectivity strength (defined as the mean of the absolute values of all connections) and in functional connectivity patterns within and between the FPN and DMN did not differ significantly across meningioma and fast (HGG) and slowly growing glioma (LGG) patients. Across these brain tumor patients, a significant and positive correlation was found between the level of task-evoked reconfiguration of the FPN and cognitive performance. This suggests that the capacity for FPN reconfiguration also plays a role in cognitive deficits in brain tumor patients, as was previously found for normal cognitive performance in healthy controls.

Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity

Adverse cognitive and behavioral conditions and psychiatric disorders are considered a critical and unmitigated risk during future long-duration space missions (LDSM). Monitoring and mitigating crew health and performance risks during these missions will require tools and technologies that allow to reliably assess cognitive performance and mental well-being. Electroencephalography (EEG) has the potential to meet the technical requirements for the non-invasive and objective monitoring of neurobehavioral conditions during LDSM. Weightlessness is associated with fluid and brain shifts, and these effects could potentially challenge the interpretation of resting state EEG recordings. Head-down tilt bed rest (HDBR) provides a unique spaceflight analog to study these effects on Earth. Here, we present data from two long-duration HDBR experiments, which were used to systematically investigate the time course of resting state electrocortical activity during prolonged HDBR. EEG spectral power significantly reduced within the delta, theta, alpha, and beta frequency bands. Likewise, EEG source localization revealed significantly lower activity in a broad range of centroparietal and occipital areas within the alpha and beta frequency domains. These changes were observed shortly after the onset of HDBR, did not change throughout HDBR, and returned to baseline after the cessation of bed rest. EEG resting state functional connectivity was not affected by HDBR. The results provide evidence for a postural effect on resting state brain activity that persists throughout long-duration HDBR, indicating that immobilization and inactivity per se do not affect resting state electrocortical activity during HDBR. Our findings raise an important issue on the validity of EEG to identify the time course of changes in brain function during prolonged HBDR, and highlight the importance to maintain a consistent body posture during all testing sessions, including data collections at baseline and recovery.

Functional connectivity hemispheric contrast (FC-HC): A new metric for language mapping

Development of a task-free method for presurgical mapping of language function is important for use in young or cognitively impaired patients. Resting state connectivity fMRI (RS-fMRI) is a task-free method that may be used to identify cognitive networks. We developed a voxelwise RS-fMRI metric, Functional Connectivity Hemispheric Contrast (FC-HC), to map the language network and determine language laterality through comparison of within-hemispheric language network connections (Integration) to cross-hemispheric connections (Segregation). For the first time, we demonstrated robustness and efficacy of a RS-fMRI metric to map language networks across five groups (total N = 243) that differed in MRI scanning parameters, fMRI scanning protocols, age, and development (typical vs pediatric epilepsy). The resting state FC-HC maps for the healthy pediatric and adult groups showed higher values in the left hemisphere, and had high agreement with standard task language fMRI; in contrast, the epilepsy patient group map was bilateral. FC-HC has strong but not perfect agreement with task fMRI and thus, may reflect related and complementary information about language plasticity and compensation.

Neuropsychopharmacological effects of midazolam on the human brain

As a commonly used anesthetic agent, midazolam has the properties of water-soluble, rapid onset, and short duration of action. With the rapid development in the field of neuroimaging, numerous studies have investigated how midazolam acts on the human brain to induce the alteration of consciousness. However, the neural bases of midazolam-induced sedation or anesthesia remain beginning to be understood in detail. In this review, we summarize findings from neuroimaging studies that have used midazolam to study altered consciousness at different levels and content. We also compare the results to those of neuroimaging studies using diverse anesthetic agents and describe the common neural correlates of anesthetic-induced alteration of consciousness.

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.

Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study

The functional connectome is organized into several separable intrinsic connectivity networks (ICNs) that are thought to be the building blocks of the mind. However, it is currently not well understood how these networks are engaged by emotionally salient information, and how such engagement fits into emotion theories. The current study assessed how ICNs respond during the processing of angry and fearful faces in a large sample (N = 843) and examined how connectivity changes relate to the ICNs. All ICNs were modulated by emotional faces and showed functional interactions, a finding which is in line with the "theory of constructed emotions" that assumes that basic emotion do not arise from separable ICNs but from their interplay. We further identified a set of brain regions whose connectivity changes during the tasks suggest a special role as "affective hubs" in the brain. While hubs were located in all ICNs, we observed high selectivity for the amygdala within the subcortical network, a finding which also fits into "primary emotion" theory. The topology of hubs corresponded closely to a set of brain regions that has been implicated in anxiety disorders, pointing towards a clinical relevance of the present findings. The present data are the most comprehensive mapping of connectome-wide changes in functionally connectivity evoked by an affective processing task thus far and support two competing views on how emotions are represented in the brain, suggesting that the connectome paradigm might help with unifying the two ideas.

Subjective Uncontrollability over Aversive Events Reduces Working Memory Performance and Related Large-Scale Network Interactions

Lack of control over significant events may induce a state of learned helplessness that is characterized by cognitive, motivational, and affective deficits. Although highly relevant in the pathogenesis of several mental disorders, the extent of the cognitive deficits induced by experiences of uncontrollability and the neural mechanisms underlying such deficits in humans remain poorly understood. Using functional magnetic resonance imaging (fMRI), we tested here whether uncontrollability over aversive events impairs subsequent working memory performance and, if so, which neural processes are involved in such deficits. We assessed working memory and the involved neurocircuitry in the MRI scanner before and after participants underwent a task in which they could either learn to avoid electric shocks or had no instrumental control over shocks. Our results show that subjective, but not objective, uncontrollability over aversive events impaired working memory performance. This impact of subjective uncontrollability was linked to altered prefrontal and parahippocampal activities and connectivity as well as decreased crosstalk between frontoparietal executive and salience networks. Our findings show that the perceived uncontrollability over aversive events, rather than the aversive events themselves or the actual, objective control over them, disrupts subsequent working memory processes, most likely through altered crosstalk between prefrontal, temporal, and parietal areas.

Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps

Functional connectivity (FC) maps from brain fMRI data can be derived with dual regression, a proposed alternative to traditional seed-based FC (SFC) methods that detect temporal correlation between a predefined region (seed) and other regions in the brain. As with SFC, incorporating nuisance regressors (NR) into the dual regression must be done carefully, to prevent potential bias and insensitivity of FC estimates. Here, we explore the potentially untoward effects on dual regression that may occur when NR correlate highly with the signal of interest, using both synthetic and real fMRI data to elucidate mechanisms responsible for loss of accuracy in FC maps. Our tests suggest significantly improved accuracy in FC maps derived with dual regression when highly correlated temporal NR were omitted. Single-map dual regression, a simplified form of dual regression that uses neither spatial nor temporal NR, offers a viable alternative whose FC maps may be more easily interpreted, and in some cases be more accurate than those derived with standard dual regression.

Longitudinal brain functional changes between mania and euthymia in bipolar disorder

OBJECTIVES

While widespread cortical and subcortical brain functional abnormalities have been found in bipolar disorder, the changes that take place between illness phases and recovery are less clearly documented. Only a small number of longitudinal studies of manic patients, in particular, have been carried out.

METHODS

Twenty-six bipolar patients underwent fMRI during performance of the n-back working memory task when manic and again after recovery. Twenty-six matched healthy controls were also scanned on two occasions. Task-related activations and de-activations were examined.

RESULTS

When manic, the patients showed clusters of significantly reduced activation in the left dorsolateral prefrontal cortex (DLPFC)/precentral cortex and the parietal cortex/superior precuneus bilaterally. They also showed failure of de-activation in the ventromedial frontal cortex (vmPFC). After recovery, activation in the left DLPFC/precentral cortex and in the bilateral parietal cortex/superior precuneus clusters increased significantly. However, failure of de-activation remained present in the vmPFC.

CONCLUSIONS

Recovery from mania is associated with normalization of DLPFC and parietal hypoactivation, but not with vmPFC failure of de-activation, which accordingly appears to represent a trait abnormality in the disorder.

Altered Static and Temporal Dynamic Amplitude of Low-Frequency Fluctuations in the Background Network During Working Memory States in Mild Cognitive Impairment

Previous studies investigating working memory performance in patients with mild cognitive impairment (MCI) have mainly focused on the neural mechanisms of alterations in activation. To date, very few studies have investigated background network alterations in the working memory state. Therefore, the present study investigated the static and temporal dynamic changes in the background network in MCI patients during a working memory task. A hybrid delayed-match-to-sample task was used to examine working memory performance in MCI patients. Functional magnetic resonance imaging (fMRI) data were collected and the marker of amplitude of low-frequency fluctuations (ALFF) was used to investigate alterations in the background network. The present study demonstrated static and dynamic alterations of ALFF in MCI patients during working memory tasks, relative to the resting state. Traditional static analysis revealed that ALFF decreased in the right ventrolateral prefrontal cortex (VLPFC), right dorsolateral PFC (DLPFC), and left supplementary motor area for normal controls (NCs) in the working memory state. However, the same regions showed increased ALFF in MCI patients. Furthermore, relative to NCs, MCI patients demonstrated altered performance-related functional connectivity (FC) patterns, with the right VLPFC and right DLPFC as ROIs. In terms of temporal dynamic analysis, the present study found that in the working memory state dynamic ALFF of bilateral thalamus regions was increased in NCs but decreased in MCI patients. Additionally, MCI patients demonstrated altered performance-related coefficient of variation patterns; the regions in MCI patients were larger and more widely distributed in the parietal and temporal lobes, relative to NCs. This is the first study to examine static and temporal dynamic alterations of ALFF in the background network of MCI patients in working memory states. The results extend previous studies by providing a new perspective on the neural mechanisms of working memory deficits in MCI patients.

Shared and differential default-mode related patterns of activity in an autobiographical, a self-referential and an attentional task

The default-mode network (DMN) comprises a set of brain regions that show deactivations during performance of attentionally demanding tasks, but also activation during certain processes including recall of autobiographical memories and processing information about oneself, among others. However, the DMN is not activated in a homogeneous manner during performance of such tasks, so it is not clear to what extent its activation patterns correspond to deactivation patterns seen during attention-demanding tasks. In this fMRI study we compared patterns of activation in response to an autobiographical memory task to those observed in a self/other-reflection task, and compared both to deactivations observed during the n-back working memory task. Autobiographical recall and self-reflection activated several common DMN areas, which were also deactivated below baseline levels by the n-back task. Activation in the medial temporal lobe was seen during autobiographical recall but not the self/other task, and right angular gyrus activity was specifically linked to other-reflection. ROI analysis showed that most, but not all DMN regions were activated above baseline levels during the autobiographical memory and self-reflection tasks. Our results provide evidence for the usefulness of the autobiographical memory task to study DMN activity and support the notion of interacting subsystems within this network.

Intrinsic connectivity networks underlying individual differences in control-averse behavior

When people sense that another person tries to control their decisions, some people will act against the control, whereas others will not. This individual tendency to control-averse behavior can have far-reaching consequences, such as engagement in illegal activities or noncompliance with medical treatments. Although individual differences in control-averse behavior have been well documented in behavioral studies, their neurological basis is less well understood. Here, we use a neural trait approach to examine whether individual differences in control-averse behavior might be linked to stable brain-based characteristics. To do so, we analyze the association between intrinsic connectivity networks as measured by resting state functional magnetic resonance imaging and control-averse behavior in an economic exchange game. In this game, subjects make choices that are either free or controlled by another person, with real consequences to both interaction partners. We find that the individual level of control-averse behavior can be positively predicted by intrinsic connectivity within the salience network, but not the central executive network or the default mode network. Specifically, subjects with a more prominent connectivity hub in the dorsal anterior cingulate cortex show greater levels of control-averse behavior. This finding provides the first evidence that the heterogeneity in control-averse behavior might originate in systematic differences of the stable functional brain organization.

Network Neuroscience and Personality

Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.

Mild Jugular Compression Collar Ameliorated Changes in Brain Activation of Working Memory after One Soccer Season in Female High School Athletes

Recent neuroimaging studies have suggested that repetitive subconcussive head impacts, even after only one sport season, may lead to pre- to post-season structural and functional alterations in male high school football athletes. However, data on female athletes are limited. In the current investigation, we aimed to (1) assess the longitudinal pre- to post-season changes in functional MRI (fMRI) of working memory and working memory performance, (2) quantify the association between the pre- to post-season change in fMRI of working memory and the exposure to head impact and working memory performance, and (3) assess whether wearing a neck collar designed to reduce intracranial slosh via mild compression of the jugular veins can ameliorate the changes in fMRI brain activation observed in the female high school athletes who did not wear collars after a full soccer season. A total of 48 female high school soccer athletes (age range: 14.00-17.97 years) were included in the study. These athletes were assigned to the non-collar group (n = 21) or to the collar group (n = 27). All athletes undewent MRI at both pre-season and post-season. In each session, a fMRI verbal N-Back task was used to engage working memory. A significant pre- to post-season increase in fMRI blood oxygen level dependent (BOLD) signal was demonstrated when performing the N-back working memory task in the non-collar group but not in the collar group, despite the comparable exposure to head impacts during the season between the two groups. The collar group demonstrated significantly smaller pre- to post-season change in fMRI BOLD signal than the non-collar group, suggesting a potential protective effect from the collar device. Significant correlations were also found between the pre- to post-season increase in fMRI brain activation and the decrease in task accuracy in the non-collar group, indicating an association between the compensatory mechanism in underlying neurophysiology and the alteration in the behavioral outcomes.

Altered Distant Synchronization of Background Network in Mild Cognitive Impairment during an Executive Function Task

Few studies to date have investigated the background network in the cognitive state relying on executive function in mild cognitive impairment (MCI) patients. Using the index of degree of centrality (DC), we explored distant synchronization of background network in MCI during a hybrid delayed-match-to-sample task (DMST), which mainly relies on the working memory component of executive function. We observed significant interactions between group and cognitive state in the bilateral posterior cingulate cortex (PCC) and the ventral subregion of precuneus. For normal control (NC) group, the long distance functional connectivity (FC) of the PCC/precuneus with the other regions of the brain was higher in rest state than that working memory state. For MCI patients, however, this pattern altered. There was no significant difference between rest and working memory state. The similar pattern was observed in the other cluster located in the right angular gyrus. To examine whether abnormal DC in PCC/precuneus and angular gyrus partially resulted from the deficit of FC between these regions and the other parts in the whole brain, we conducted a seed-based correlation analysis with these regions as seeds. The results indicated that the FC between bilateral PCC/precuneus and the right inferior parietal lobule (IPL) increased from rest to working memory state for NC participants. For MCI patients, however, there was no significant change between rest and working memory state. The similar pattern was observed for the FC between right angular gyrus and right anterior insula. However, there was no difference between MCI and NC groups in global efficiency and modularity. It may indicate a lack of efficient reorganization from rest state to a working memory state in the brain network of MCI patients. The present study demonstrates the altered distant synchronization of background network in MCI during a task relying on executive function. The results provide a new perspective regarding the neural mechanisms of executive function deficits in MCI patients, and extend our understanding of brain patterns in task-evoked cognitive states.

The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition

A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition.

SIGNIFICANCE STATEMENT

The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.

Differential failure to deactivate the default mode network in unipolar and bipolar depression

OBJECTIVES

Neuroimaging studies have revealed evidence of brain functional abnormalities in bipolar depressive disorder (BDD) and major depressive disorder (MDD). However, few studies to date have compared these two mood disorders directly.

METHODS

Matched groups of 26 BDD type I patients, 26 MDD patients and 26 healthy controls underwent functional magnetic resonance imaging (fMRI) while performing the n-back working memory task. A whole-brain ANOVA was used to compare the three groups and clusters of significant difference were examined further using region-of-interest (ROI) analysis.

RESULTS

The whole-brain ANOVA revealed a single cluster of significant difference in the medial frontal cortex. The BDD and MDD patients both showed failure to deactivate in this area compared to the controls. The BDD patients showed significantly greater failure of deactivation than the MDD patients, which was not accounted for by differences in severity or chronicity of illness between them.

CONCLUSIONS

Failure of deactivation, considered to reflect default mode network dysfunction, is present to a greater extent in bipolar than unipolar depression. The study of this network may be useful in the search for brain markers that distinguish the two disorders.

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.

Targeting neuronal dysfunction in schizophrenia with nicotine: Evidence from neurophysiology to neuroimaging

Patients with schizophrenia self-administer nicotine at rates higher than is self-administered for any other psychiatric illness. Although the reasons are unclear, one hypothesis suggests that nicotine is a form of 'self-medication' in order to restore normal levels of nicotinic signaling and target abnormalities in neuronal function associated with cognitive processes. This brief review discusses evidence from neurophysiological and neuroimaging studies in schizophrenia patients that nicotinic agonists may effectively target dysfunctional neuronal circuits in the illness. Evidence suggests that nicotine significantly modulates a number of these circuits, although relatively few studies have used modern neuroimaging techniques (e.g. functional magnetic resonance imaging (fMRI)) to examine the effects of nicotinic drugs on disease-related neurobiology. The neuronal effects of nicotine and other nicotinic agonists in schizophrenia remain a priority for psychiatry research.

Reason's Enemy Is Not Emotion: Engagement of Cognitive Control Networks Explains Biases in Gain/Loss Framing

In the classic gain/loss framing effect, describing a gamble as a potential gain or loss biases people to make risk-averse or risk-seeking decisions, respectively. The canonical explanation for this effect is that frames differentially modulate emotional processes, which in turn leads to irrational choice behavior. Here, we evaluate the source of framing biases by integrating functional magnetic resonance imaging data from 143 human participants performing a gain/loss framing task with meta-analytic data from >8000 neuroimaging studies. We found that activation during choices consistent with the framing effect were most correlated with activation associated with the resting or default brain, while activation during choices inconsistent with the framing effect was most correlated with the task-engaged brain. Our findings argue against the common interpretation of gain/loss framing as a competition between emotion and control. Instead, our study indicates that this effect results from differential cognitive engagement across decision frames.SIGNIFICANCE STATEMENT The biases frequently exhibited by human decision makers have often been attributed to the presence of emotion. Using a large fMRI sample and analysis of whole-brain networks defined with the meta-analytic tool Neurosynth, we find that neural activity during frame-biased decisions was more significantly associated with default behaviors (and the absence of executive control) than with emotion. These findings point to a role for neuroscience in shaping long-standing psychological theories in decision science.

Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task

Task-based connectivity studies facilitate the understanding of how the brain functions during cognition, which is commonly impaired in schizophrenia (SZ). Our aim was to investigate functional connectivity during a working memory task in SZ. We hypothesized that the task-negative (default mode) network and the cognitive control (frontoparietal) network would show dysconnectivity. Twenty-five SZ patient and 31 healthy control scans were collected using the customized 3T Siemens Skyra MRI scanner, previously used to collect data for the Human Connectome Project. Blood oxygen level dependent signal during the 0-back and 2-back conditions were extracted within a network-based parcelation scheme. Average functional connectivity was assessed within five brain networks: frontoparietal (FPN), default mode (DMN), cingulo-opercular (CON), dorsal attention (DAN), and ventral attention network; as well as between the DMN or FPN and other networks. For within-FPN connectivity, there was a significant interaction between n-back condition and group (p = 0.015), with decreased connectivity at 0-back in SZ subjects compared to controls. FPN-to-DMN connectivity also showed a significant condition × group effect (p = 0.003), with decreased connectivity at 0-back in SZ. Across groups, connectivity within the CON and DAN were increased during the 2-back condition, while DMN connectivity with either CON or DAN were decreased during the 2-back condition. Our findings support the role of the FPN, CON, and DAN in working memory and indicate that the pattern of FPN functional connectivity differs between SZ patients and control subjects during the course of a working memory task.

Small Changes, But Huge Impact? The Right Anterior Insula's Loss of Connection Strength during the Transition of Old to Very Old Age

A major contribution to our understanding of the aging brain comes either from studies comparing young with older adults or from studies investigating pathological aging and using the healthy aging older adults as control group. In consequence, we know relatively well, what distinguishes young from old brains or pathological aging from healthy but that does not mean that we really understand the structural and functional transformations characterizing the healthy aging brain. By analyzing task-free fMRI data from a large cross-sectional sample of 186 older adults (mean age = 70.4, 97 female), we aimed to elucidate age-related changes in the intrinsically active functional architecture of the brain in our study group covering an age range from 65 to 85 years. First, we conducted an intrinsic connectivity contrast analysis (ICC) in order to detect the brain regions whose degree of connectedness was significantly correlated with increasing age. Secondly, using connectivity analyses we investigated how the clusters highlighted by the ICC analysis functionally related to the other major resting-state networks. The most important finding was the right anterior insula's loss of connectedness in the older participants of the study group because of the region's causal role in the switching from the task-negative to the task-positive state of the brain. Further, we found a higher functional dedifferentiation of two of the brain's major intrinsic connectivity networks, the DMN, and the cingulo-opercular network, caused by a reduction of functional connection strength, especially in the frontal regions. At last, we showed that all these age-related changes have the potential to impair older adult's performance of working memory tasks.

Resting-State Coupling between Core Regions within the Central-Executive and Salience Networks Contributes to Working Memory Performance

Previous studies investigated the distinct roles played by different cognitive regions and suggested that the patterns of connectivity of these regions are associated with working memory (WM). However, the specific causal mechanism through which the neuronal circuits that involve these brain regions contribute to WM is still unclear. Here, in a large sample of healthy young adults, we first identified the core WM regions by linking WM accuracy to resting-state functional connectivity with the bilateral dorsolateral prefrontal cortex (dLPFC; a principal region in the central-executive network, CEN). Then a spectral dynamic causal modeling (spDCM) analysis was performed to quantify the effective connectivity between these regions. Finally, the effective connectivity was correlated with WM accuracy to characterize the relationship between these connections and WM performance. We found that the functional connections between the bilateral dLPFC and the dorsal anterior cingulate cortex (dACC) and between the right dLPFC and the left orbital fronto-insular cortex (FIC) were correlated with WM accuracy. Furthermore, the effective connectivity from the dACC to the bilateral dLPFC and from the right dLPFC to the left FIC could predict individual differences in WM. Because the dACC and FIC are core regions of the salience network (SN), we inferred that the inter- and causal-connectivity between core regions within the CEN and SN is functionally relevant for WM performance. In summary, the current study identified the dLPFC-related resting-state effective connectivity underlying WM and suggests that individual differences in cognitive ability could be characterized by resting-state effective connectivity.

Functional magnetic resonance and diffusion tensor imaging analysis of verbal working memory in patients with temporal lobe epilepsy

OBJECTIVE

This study aimed to investigate the relationships between brain functions and verbal working memory (VWM) impairments in patients with temporal lobe epilepsy (TLE).

METHODS

Functional magnetic resonance imaging was performed on 15 healthy controls and 15 patients with TLE while they performed the N-back task in order to compare the brain areas that were activated during VWM. Diffusion tensor imaging was then performed to examine the integrity of the VWM-related fiber bundles.

RESULTS

The patient group exhibited a significant reduction in the activation of the brain areas that were negatively activated in the VWM task in the control group (P < 0.05), and no activation was observed in the patient group in the brain areas that were positively activated in the VWM task in the control group. The fractional anisotropy values of both cingulate bundles were significantly reduced in the patient group compared with those in the control group (P < 0.05), and these values were positively correlated with the number of activated pixels in the ipsilateral prefrontal areas (left: r = 0.790, P < 0.05; right: r = 0.852, P < 0.05).

CONCLUSIONS

Patients with TLE exhibited impaired VWM functions that were related to subtle alterations in the cingulate bundles.

Disruption of cortical integration during midazolam-induced light sedation

This work examines the effect of midazolam‐induced light sedation on intrinsic functional connectivity of human brain, using a randomized, double‐blind, placebo‐controlled, cross‐over, within‐subject design. Fourteen healthy young subjects were enrolled and midazolam (0.03 mg/kg of the participant's body mass, to a maximum of 2.5 mg) or saline were administrated with an interval of one week. Resting‐state fMRI was conducted before and after administration for each subject. We focus on two types of networks: sensory related lower‐level functional networks and higher‐order functions related ones. Independent component analysis (ICA) was used to identify these resting‐state functional networks. We hypothesize that the sensory (visual, auditory, and sensorimotor) related networks will be intact under midazolam‐induced light sedation while the higher‐order (default mode, executive control, salience networks, etc.) networks will be functionally disconnected. It was found that the functional integrity of the lower‐level networks was maintained, while that of the higher‐level networks was significantly disrupted by light sedation. The within‐network connectivity of the two types of networks was differently affected in terms of direction and extent. These findings provide direct evidence that higher‐order cognitive functions including memory, attention, executive function, and language were impaired prior to lower‐level sensory responses during sedation. Our result also lends support to the information integration model of consciousness. Hum Brain Mapp 36:4247–4261, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Neural networks supporting switching, hypothesis testing, and rule application

We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example "choose the blue letter". Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest learning speed late in the time course of learning. As subjects shifted from hypothesis testing to rule application, activity in this network decreased and activity in the somatomotor and default mode networks increased.

Age related-changes in the neural basis of self-generation in verbal paired associate learning

Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females) ages 18–76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning (“read”) or self-generation learning (“generate”) of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more “generate” words were correctly recognized than “read” words (p < 0.001) with older adults recognizing the “generated” words less accurately (p < 0.05). Independent component analysis of fMRI data identified task-related brain networks. Several components were positively correlated with the task reflecting multiple cognitive processes involved in self-generated encoding; other components correlated negatively with the task, including components of the default-mode network. Overall, memory performance on generated words decreased with age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified areas that were associated with age-related changes independent of performance.

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Verbal information is better retained when self-generated vs. received passively.

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Application of self-generation is associated with better retention across ages.

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Generated words were retained better than read words.

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Several components of network for word generation were identified.

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Age-associated changes within the network are discussed.

Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns

Individual learning performance of cognitive function is related to functional connections within ‘task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80–90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R² = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

Quantifying the reconfiguration of intrinsic networks during working memory

Rapid, flexible reconfiguration of connections across brain regions is thought to underlie successful cognitive control. Two intrinsic networks in particular, the cingulo-opercular (CO) and fronto-parietal (FP), are thought to underlie two operations critical for cognitive control: task-set maintenance/tonic alertness and adaptive, trial-by-trial updating. Using functional magnetic resonance imaging, we directly tested whether the functional connectivity of the CO and FP networks was related to cognitive demands and behavior. We focused on working memory because of evidence that during working memory tasks the entire brain becomes more integrated. When specifically probing the CO and FP cognitive control networks, we found that individual regions of both intrinsic networks were active during working memory and, as expected, integration across the two networks increased during task blocks that required cognitive control. Crucially, increased integration between each of the cognitive control networks and a task-related, non-cognitive control network (the hand somatosensory-motor network; SM) was related to increased accuracy. This implies that dynamic reconfiguration of the CO and FP networks so as to increase their inter-network communication underlies successful working memory.

Ongoing activity in temporally coherent networks predicts intra-subject fluctuation of response time to sporadic executive control demands

Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person's response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color-word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute "cognitive readiness," which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance.

The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks

Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context.

Precuneus is a functional core of the default-mode network

Efforts to understand the functional architecture of the brain have consistently identified multiple overlapping large-scale neural networks that are observable across multiple states. Despite the ubiquity of these networks, it remains unclear how regions within these large-scale neural networks interact to orchestrate behavior. Here, we collected functional magnetic resonance imaging data from 188 human subjects who engaged in three cognitive tasks and a resting-state scan. Using multiple tasks and a large sample allowed us to use split-sample validations to test for replication of results. We parceled the task-rest pairs into functional networks using a probabilistic spatial independent components analysis. We examined changes in connectivity between task and rest states using dual-regression analysis, which quantifies voxelwise connectivity estimates for each network of interest while controlling for the influence of signals arising from other networks and artifacts. Our analyses revealed systematic state-dependent functional connectivity in one brain region: the precuneus. Specifically, task performance led to increased connectivity (compared to rest) between the precuneus and the left frontoparietal network (lFPN), whereas rest increased connectivity between the precuneus and the default-mode network (DMN). The absolute magnitude of this effect was greater for DMN, suggesting a heightened specialization for resting-state cognition. All results replicated within the two independent samples. Our results indicate that the precuneus plays a core role not only in DMN, but also more broadly through its engagement under a variety of processing states.

Spontaneous neuronal activity predicts intersubject variations in executive control of attention

Executive control of attention regulates our thoughts, emotion and behavior. Individual differences in executive control are associated with task-related differences in brain activity. But it is unknown whether attentional differences depend on endogenous (resting state) brain activity and to what extent regional fluctuations and functional connectivity contribute to individual variations in executive control processing. Here, we explored the potential contribution of intrinsic brain activity to executive control by using resting-state functional magnetic resonance imaging (fMRI). Using the amplitude of low-frequency fluctuations (ALFF) as an index of spontaneous brain activity, we found that ALFF in the right precuneus (PCUN) and the medial part of left superior frontal gyrus (msFC) was significantly correlated with the efficiency of executive control processing. Crucially, the strengths of functional connectivity between the right PCUN/left msFC and distributed brain regions, including the left fusiform gyrus, right inferior frontal gyrus, left superior frontal gyrus and right precentral gyrus, were correlated with individual differences in executive performance. Together, the ALFF and functional connectivity accounted for 67% of the variability in behavioral performance. Moreover, the strength of functional connectivity between specific regions could predict more individual variability in executive control performance than regionally specific fluctuations. In conclusion, our findings suggest that spontaneous brain activity may reflect or underpin executive control of attention. It will provide new insights into the origins of inter-individual variability in human executive control processing.

Phenotypic variability in resting-state functional connectivity: current status

We reviewed the extant literature with the goal of assessing the extent to which resting-state functional connectivity is associated with phenotypic variability in healthy and disordered populations. A large corpus of work has accumulated to date (125 studies), supporting the association between intrinsic functional connectivity and individual differences in a wide range of domains-not only in cognitive, perceptual, motoric, and linguistic performance, but also in behavioral traits (e.g., impulsiveness, risky decision making, personality, and empathy) and states (e.g., anxiety and psychiatric symptoms) that are distinguished by cognitive and affective functioning, and in neurological conditions with cognitive and motor sequelae. Further, intrinsic functional connectivity is sensitive to remote (e.g., early-life stress) and enduring (e.g., duration of symptoms) life experience, and it exhibits plasticity in response to recent experience (e.g., learning and adaptation) and pharmacological treatment. The most pervasive associations were observed with the default network; associations were also widespread between the cingulo-opercular network and both cognitive and affective behaviors, while the frontoparietal network was associated primarily with cognitive functions. Associations of somatomotor, frontotemporal, auditory, and amygdala networks were relatively restricted to the behaviors linked to their respective putative functions. Surprisingly, visual network associations went beyond visual function to include a variety of behavioral traits distinguished by affective function. Together, the reviewed evidence sets the stage for testing causal hypothesis about the functional role of intrinsic connectivity and augments its potential as a biomarker for healthy and disordered brain function.

Atypical modulation of distant functional connectivity by cognitive state in children with Autism Spectrum Disorders

We examined whether modulation of functional connectivity by cognitive state differed between pre-adolescent children with Autism Spectrum Disorders (ASD) and age and IQ-matched control children. Children underwent functional magnetic resonance imaging (fMRI) during two states, a resting state followed by a sustained attention task. A voxel-wise method was used to characterize functional connectivity at two levels, local (within a voxel's 14 mm neighborhood) and distant (outside of the voxel's 14 mm neighborhood to the rest of the brain) and regions exhibiting Group × State interaction were identified for both types of connectivity maps. Distant functional connectivity of regions in the left frontal lobe (dorsolateral [BA 11, 10]; supplementary motor area extending into dorsal anterior cingulate [BA 32/8]; and premotor [BA 6, 8, 9]), right parietal lobe (paracentral lobule [BA 6]; angular gyrus [BA 39/40]), and left posterior middle temporal cortex (BA 19/39) showed a Group × State interaction such that relative to the resting state, connectivity reduced (i.e., became focal) in control children but increased (i.e., became diffuse) in ASD children during the task state. Higher state-related increase in distant connectivity of left frontal and right angular gyrus predicted worse inattention in ASD children. Two graph theory measures (global efficiency and modularity) were also sensitive to Group × State differences, with the magnitude of state-related change predicting inattention in the ASD children. Our results indicate that as ASD children transition from an unconstrained to a sustained attentional state, functional connectivity of frontal and parietal regions with the rest of the brain becomes more widespread in a manner that may be maladaptive as it was associated with attention problems in everyday life.

Resting-state striato-frontal functional connectivity is sensitive to DAT1 genotype and predicts executive function

Individual differences in striatal dopamine (DA) signaling have been associated both with individual differences in executive function in healthy individuals and with risk for psychiatric disorders defined by executive dysfunction. We used resting-state functional connectivity in 50 healthy adults to examine whether a polymorphism of the dopamine transporter gene (DAT1), which regulates striatal DA function, affects striatal functional connectivity in healthy adults, and whether that connectivity predicts executive function. We found that 9/10 heterozygotes, who are believed to have higher striatal DA signaling, demonstrated stronger connectivity between dorsal caudate (DC) and insular, dorsal anterior cingulate, and dorsolateral prefrontal regions, as well as between ventral striatum and ventrolateral prefrontal cortex, than 10/10 homozygotes. Across subjects, stronger DC-seeded connectivity predicted superior N-back working memory performance, while stronger ventral striatum-seeded connectivity predicted reduced impulsivity in everyday life. Further, mediation analysis suggested that connectivity strength mediated relationships between DAT1 genotype and behavior. These findings suggest that resting-state striato-frontal connectivity may be an endophenotype for executive function in healthy individuals.

Schizophrenia and the brain's control network: aberrant within- and between-network connectivity of the frontoparietal network in schizophrenia

The deficit of executive control is a core feature of schizophrenia, and as such, it provides hints for the neural signature of this devastating mental illness. The frontoparietal network (FPN) is a newly defined network important for various tasks requiring executive control. This study aims to investigate both the within- and between-network connectivity of the FPN in schizophrenia using functional connectivity MRI (fcMRI). Thirty-six subjects with schizophrenia and thirty-six healthy controls were enrolled. Each subject received resting fMRI scanning, clinical evaluations and cognitive examinations. Twenty-two regions of interest (ROIs) in the key hubs of the FPN were defined according to the functional connectivity map of the left and right dorsolateral prefrontal cortex (dlPFC) and included the bilateral frontal pole, inferior parietal lobe (IPL), insula, dorsomedial prefrontal cortex (dmPFC), middle cingulate cortex (mCC), precuneus, caudate, thalamus and cerebellum. Between-group comparisons were conducted using both multiple ROI-based and brain-wise analyses. The ROI-based analysis revealed that the schizophrenic participants were associated with a prominent cortico-subcortical disconnection within the FPN. Further brain-wise analyses demonstrated that the schizophrenia patients showed increased functional connectivity between several ROIs in the FPN and regions belonging to the primary sensory processing or default mode networks. These results indicated that schizophrenia is associated with both within- and between-network dysconnectivity of the FPN. Together with our previous findings of the cortico-striatal disconnection of the cingulo-opercular network, we suggest that the brain's control networks may play an important role in the neural mechanisms of schizophrenia.

Neural markers of negative symptom outcomes in distributed working memory brain activity of antipsychotic-naive schizophrenia patients

Since working memory deficits in schizophrenia have been linked to negative symptoms, we tested whether features of the one could predict the treatment outcome in the other. Specifically, we hypothesized that working memory-related functional connectivity at pre-treatment can predict improvement of negative symptoms in antipsychotic-treated patients. Fourteen antipsychotic-naive patients with first-episode schizophrenia were clinically assessed before and after 7 months of quetiapine monotherapy. At baseline, patients underwent functional magnetic resonance imaging while performing a verbal n-back task. Spatial independent component analysis identified task-modulated brain networks. A linear support vector machine was trained with these components to discriminate six patients who showed improvement in negative symptoms from eight non-improvers. Classification accuracy and significance was estimated by leave-one-out cross-validation and permutation tests, respectively. Two frontoparietal and one default mode network components predicted negative symptom improvement with a classification accuracy of 79% (p = 0.003). Discriminating features were found in the frontoparietal networks but not the default mode network. These preliminary data suggest that functional patterns at baseline can predict negative symptom treatment-response in schizophrenia. This information may be used to stratify patients into subgroups thereby facilitating personalized treatment.